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A potential field based formation control methodology for robot swarms

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Title:
A potential field based formation control methodology for robot swarms
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English
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Barnes, Laura E
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University of South Florida
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Tampa, Fla
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Subjects / Keywords:
Swarm formation
Mobile robots
Robot control
Multirobot systems
Intelligent systems
Dissertations, Academic -- Computer Science and Engineering -- Doctoral -- USF   ( lcsh )
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non-fiction   ( marcgt )

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Abstract:
ABSTRACT: A novel methodology is presented for organizing swarms of robots into a formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions construct the surface which swarm members travel on, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of four and ten particles following circle, ellipse and wedge formations. Experimental results are also included with a swarm of four unmanned ground vehicles (UGV) as well as UGV swarm and unmanned aerial vehicle (UAV) coordination.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2008.
Bibliography:
Includes bibliographical references.
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by Laura E. Barnes.
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Title from PDF of title page.
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Document formatted into pages; contains 139 pages.
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Includes vita.

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ABSTRACT: A novel methodology is presented for organizing swarms of robots into a formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions construct the surface which swarm members travel on, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of four and ten particles following circle, ellipse and wedge formations. Experimental results are also included with a swarm of four unmanned ground vehicles (UGV) as well as UGV swarm and unmanned aerial vehicle (UAV) coordination.
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A Potential Field Based Forma tion Control Methodology for Robot Swarms by Laura E. Barnes A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science and Engineering College of Engineering University of South Florida Major Professor: Kimon Valavanis, Ph.D. Lawrence Hall, Ph.D. Rangachar Kasturi, Ph.D. Rafael Perez, Ph.D. Stephen Wilkerson, Ph.D. Maryanne Fields, Ph.D. Date of Approval: March 3, 2008 Keywords: swarm formation, mobile robots, robot control, multirobot systems, intelligent systems Copyright 2008, Laura E. Barnes

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Note to Reader The original of this document contains color th at is necessary for understanding the data. The original dissertation is on file with the USF library in Tampa, Florida.

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Dedication This work is dedicated to my family and friends without whom this work would not have been possible. I would like to specifically thank my parents for their ongoing support. I would also like to thank Richard Garcia for his consta nt friendship and technical assistance throughout our journey the past years. Last but not least, I would like to thank Sakis for his love and encouragement.

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Acknowledgments This research was supported in part by an appointment to Student Research Participation Program at U.S. Army Research Laboratory admini stered by the Oak Ridge Institute for Science and Education through interagenc y agreement between the U.S. Department of Energy and US ARL. This work was also supported partia lly by two grants ARO W911NF-06-1-0069 and SPAWAR N00039-06-C-0062. I would specifically like to thank Dr. MaryAnne Fields from the Army Research Lab for all her time and help.

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i Table of Contents List of Tables v List of Figures vi Abstract x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Problem Statement 2 1.3 Method of Approach 2 1.4 Research Contributions 2 1.5 Summary of Results 3 1.6 Thesis Outline 4 Chapter 2 Background Information 5 2.1 Mobile Robot Systems 5 2.2 Multirobot Systems 7 2.3 Multirobot Architectures 8 2.4 Robot Swarms 10 2.5 Swarm Formation Control 12 Chapter 3 Literature Review 14 3.1 Formation Control Methods 14 3.1.1 Behavior-Based and Potential Field Fo rmation Control Strategies 17 3.1.2 Leader-Follower and Graph Theoretic Formation Control Strategies 19 3.1.3 Virtual Structure Formation Control Strategies 22 3.1.4 Other Control Strategies in Formation Control 22 3.2 Foundation of the Proposed Approach 24 3.3 Summary 25 Chapter 4 Proposed Formation Control Approach 27 4.1 Swarm Surface 27 4.2 Formation Problem 28

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ii4.3 Generation of Vectors and Vector Field 31 4.3.1 Description of Vector Fields 31 4.3.2 Description of Limiting Functions 33 4.3.2.1 Sin and Sout Sigmoid Limiting Functions 33 4.3.2.1.1 Mathematical Solution for in-Control Variable 37 4.3.2.1.2 Mathematical Solution for out-Control Variable 38 4.3.2.1.3 Mathematical Proof for Convergence of Sin and Sout Limiting Functions 40 4.3.2.2 N Normal Limiting Function 42 4.3.3 Controlling Swarm Member Dispersion within Bands 45 4.4 Parameter Selection 47 4.4.1 Logical and Static Parameter Selection for R -Parameters 47 4.4.1.1 Ellipse Formation 47 4.4.1.2 Arc Formation 48 4.4.1.3 Line Formation 48 4.4.1.3.1 Line Formation with Skinny Ellipse 48 4.4.1.3.2 Leader-Follower Line Formation 49 4.4.2 Fuzzy Speed Control and Parameter Selection 49 4.4.2.1 Fuzzy Speed Control 50 4.4.2.2 Fuzzy Parameter Selection of Ravoid 53 Chapter 5 Simulation Software 55 5.1 Matlab Simulink Model 55 5.1.1 Vector Generator Block 57 5.1.2 Vehicle Model Block 57 5.1.2.1 Particle Model 57 5.1.2.2 Robot Model 58 5.1.2.2.1 Forward Body Reference Dynamics 58 5.1.2.2.2 Motor Model 59 5.1.2.2.3 Kinematic Calculations 60 Chapter 6 Hardware Architecture and Platform 63 6.1 Radio-Controlled Vehicles 64 6.1.1 Radio-Controlled Ground Vehicles 64

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iii6.1.2 Radio-Controlled Helicopter 65 6.2 Sensors 66 6.3 Computer System 67 Chapter 7 Software System Architecture 68 7.1 Operating System 68 7.2 Software Architecture 69 7.2.1 Sensor Suite 72 7.2.1.1 GPS Sensor 72 7.2.1.2 IMU Sensor 73 7.2.2 Navigation and Obstacle Avoidance 74 7.2.2.1 Swarm Formation Controller 75 7.2.2.2 Robot Motion Controller 75 7.2.3 Communication Server / Client Model 76 Chapter 8 Simulation Results 77 8.1 Simulations with Robot Model 77 8.1.1 Ten Heterogeneous Robots Circling a Point 77 8.1.2 Ten Homogeneous Robots Following a Straight Trajectory without Limiting Functions 79 8.1.3 Ten Heterogeneous Robots in a Line Formation 81 8.1.4 Ten Heterogeneous Robots in an Ellip se Formation 82 8.2 Simulations with Particle Model 84 8.2.1 Simulations with Particle Model and Static Variable Selection 84 8.2.1.1 Four Particles in Arc and Circle Formations 84 8.2.1.2 Ten Particles in Circle and Ellipse Formations 86 8.2.2 Simulations with Particle Model and Fu zzy Variable Selection 88 8.2.2.1 Four Particles in Arc / Wedge Formation 88 8.2.2.2 Four Particles in Circle / Square Formation 90 8.2.2.3 Four Particles in Ellipse / Rectangle Formation 92 Chapter 9 Field Experiments 94 9.1 Experiment 1: Four Robots in an Ellipse Formation with a Virtual Center 94 9.2 Experiment 2: Three Robots in an Ellipse Fo rmation with a Virtual Center 98 9.3 Experiment 3: Three Robots in an Ellipse Formation with a Robot Center 101 9.4 Experiment 4: Three Robots in a Line Formation 104

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iv9.5 Experiment 5: Three Robots in an Ellip se Formation with a Failure 106 9.6 Experiment 6: UAV-UGV Swarm Coordination 109 Chapter 10 Future Approach Utilizing Deformable Ellipses 113 10.1 Technical Approach 113 10.2 Improving Static Formations 113 10.3 Bending the Ellipsoid 116 10.4 Translating and Rotating the Ellipsoid 119 Chapter 11 Conclusions and Future Work 125 11.1 Conclusions 125 11.2 Future Work 125 References 127 Appendices 137 Appendix A Nomenclature for Mathema tical Variables 138 About the Author End Page

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v List of Tables Table 3.1. Formations with implicit robo tic control. 16 Table 3.2. Formations with explicit ro botic control utilizing leader-follower reference. 16 Table 3.3. Formations with explic it robotic control utilizing other reference types. 17 Table 5.1. Robot physical parameters. 59 Table 5.2. Motor parameters. 60 Table 7.1. GPS shared memory data structure. 72 Table 7.2. IMU shared memory data structure. 73 Table 8.1. Control variables with ten robots. 78 Table 8.2. Control variables with ten heterogeneous robots. 81 Table 8.3. Control variables with four particles. 84 Table 8.4. Control variables with ten particles. 86 Table 8.5. Control variables for arc formation utiliz ing fuzzy parameter selection. 88 Table 8.6. Control variables for circle formation uti lizing fuzzy parameter selection. 90 Table 8.7. Control variables for ellipse formation utilizing fuzzy parameter selection. 92 Table 9.1. Control variables for experiment 1. 95 Table 9.2. Control variables for experiment 2. 98 Table 9.3. Control variables for experiment 3. 101 Table 9.4. Control variables for experiment 4. 104 Table 9.5. Control variables for experiment 5. 107 Table 9.6. Control variables for experiment 6. 110 Table A.1. Swarm equation variables. 138

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vi List of Figures Figure 2.1. Examples of robots. 6 Figure 2.2. Multirobot system groups. 7 Figure 2.3. Centralized control model. 8 Figure 2.4. Distributed, decentralized control model. 9 Figure 2.5. Examples of swarms in nature. 11 Figure 3.1. Examples of formation shapes with three robots. 15 Figure 4.1. Convoy description. 28 Figure 4.2. Convoy of vehicles surrounded by concentric ellipses. 29 Figure 4.3. Elliptical attraction band for the swarm robots. 30 Figure 4.4. Vector fields directed away from the center (G-). 32 Figure 4.5. Vector fields directed towards the center (G+). 32 Figure 4.6. Vector fields directed perpendicular to the center (G). 33 Figure 4.7. General sigmoid function. 34 Figure 4.8. Combined in (G+) and out (G-) fields. 35 Figure 4.9. The weighting functions Sin and Sout as a function of the weighted distance r defined in (4.17). 36 Figure 4.10. Weighting function W(r) (shown in green) when Rin = Rou t. 41 Figure 4.11. Weighting function W(r) (shown in green) when Rou t > Rin. 42 Figure 4.12. The weighting function N as a function of the weighted distance r defined in (4.17). 43 Figure 4.13. Vector field with Sin, Sout and N limiting functions. 45 Figure 4.14 Skinny ellipse with swarm members trapped inside. 49 Figure 4.15. Leader-follower line formation approach 49 Figure 4.16. Fuzzy speed controller. 50 Figure 4.17. Distance from center ( dCenter ) input. 51 Figure 4.18. Distance from obstacles ( dObst ) input. 51

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viiFigure 4.19. Fuzzy speed output. 52 Figure 4.20. Fuzzy rules for speed controller. 52 Figure 4.21. Surface function for speed controller. 52 Figure 4.22. Distance to nearest neighbor ( dMembers ). 53 Figure 4.23. Fuzzy output for the Ravoid parameter. 54 Figure 4.24. Fuzzy rules for the Ravoid parameter selection. 54 Figure 4.25. Surface plot for the Ravoid parameter. 54 Figure 5.1. Matlab Simulink swarm simulation with n robots / particles. 56 Figure 5.2. Block diagram of the car model with feedback. 58 Figure 6.1. Overall hardware system for UGVs. 63 Figure 6.2. Custom-built RC-cars. 64 Figure 6.3. RC-car components. 65 Figure 6.4. Maxi Joker 2 helicopter. 66 Figure 6.5. Sensors. 66 Figure 6.6. On-board computer processing system. 67 Figure 7.1. Ad-hoc communication network utiliz ing Mobile Mesh. 69 Figure 7.2. Overall software system architecture. 70 Figure 7.3. Source code directory and file structure. 71 Figure 7.4. Pseudo-code for navigation of a single swarm member. 74 Figure 8.1. Ten heterogeneous robots circling a fixed center point. 78 Figure 8.2. Ten robot swarm following a trajectory with time on the z-axis without limiting functions. 79 Figure 8.3. Ten robot swarm at beginning ( tb), middle ( tm), and end ( tf) of mission without using limiting functions. 80 Figure 8.4. Ten robot swarm following a trajectory avoiding fixed obstacles without limiting functions. 80 Figure 8.5. Line formation with ten robots at different time steps. 82 Figure 8.6. Ellipse formation with ten robots at different time steps. 83 Figure 8.7. Ellipse formation with sine wave trajectory. 83 Figure 8.8. Particle paths from initial position in to arc formation. 85 Figure 8.9. Particle arc formation at beginning ( tb), middle ( tm), and end ( tf). 85 Figure 8.10. Particle circle formation at beginning ( tb), middle ( tm), and end ( tf). 86 Figure 8.11. Particle circle formation. 87

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viiiFigure 8.12. Particle ellipse formation. 87 Figure 8.13. Experiment 1: Particle arc formation at beginning, middle, and end. 89 Figure 8.14. Experiment 2: Particle arc formation at beginning, middle, and end. 89 Figure 8.15. Experiment 1: Particle circle formati on at beginning, middle, and end. 91 Figure 8.16. Experiment 2: Particle circle formati on at beginning, middle, and end. 91 Figure 8.17. Particle paths to ellipse formation. 93 Figure 8.18. Particles at different time steps to ellipse formation. 93 Figure 9.1. Experiment 1: Robot distance from center of swarm ( xc,yc). 96 Figure 9.2. Experiment 1: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission. 96 Figure 9.3. Experiment 1: Robot paths with respect to center ( xc, yc) with time on z-axis. 97 Figure 9.4. Experiment 1: Distance between swarm members. 97 Figure 9.5. Experiment 2: Robot distance from center of swarm ( xc,yc). 99 Figure 9.6. Experiment 2: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission. 99 Figure 9.7. Experiment 2: Robot paths with respect to center ( xc, yc) with time on z-axis. 100 Figure 9.8. Experiment 2: Distance betw een swarm members. 100 Figure 9.9. Experiment 3: Robot distance from center of swarm ( xc,yc). 102 Figure 9.10. Experiment 3: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission. 102 Figure 9.11. Experiment 3: Robot paths with respect to center ( xc, yc) with time on z-axis. 103 Figure 9.12. Experiment 3: Distance betw een swarm members. 103 Figure 9.13. Experiment 4: Distance betw een swarm members. 105 Figure 9.14. Experiment 4: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission. 105 Figure 9.15. Experiment 4: Robot paths with respect to center ( xc, yc) with time on z-axis. 106 Figure 9.16. Experiment 5: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission. 108 Figure 9.17. Experiment 5: Robot paths with respect to center ( xc, yc). 108

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ixFigure 9.18. Experiment 5: Distance betw een swarm members. 109 Figure 9.19. UAV-UGV swarm coordination. 110 Figure 9.20. Experiment 6: Robot distance from center of swarm ( xc,yc). 111 Figure 9.21. Experiment 6: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission. 111 Figure 9.22. Experiment 6: Robot paths with respect to center ( xc, yc) with time on z-axis. 112 Figure 9.23. Experiment 6: Distance betw een swarm members. 112 Figure 10.1. Examples of wedge formations. 115 Figure 10.2. A box distribution formation. 115 Figure 10.3. Bent ellipse. 118 Figure 10.4. Bent ellipse examples. 119 Figure 10.5. Diagram of transformations applied to vector field for the bent ellipse. 121 Figure 10.6. A rotated and bent ellipse. 124

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x A Potential Field Based Formation C ontrol Methodology for Robot Swarms Laura E. Barnes ABSTRACT A novel methodology is presented for organizing swarms of robots into a formation utilizing artificial potential fields generated from normal a nd sigmoid functions. These functions construct the surface which swarm members travel on, co ntrolling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and me mber spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, co mputationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of f our and ten particles following circle, ellipse and wedge formations. Experimental results are also included with a swarm of four unmanned ground vehicles (UGV) as well as UGV swarm and unmanned aerial vehicle (UAV) coordination.

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1 Chapter 1 Introduction 1.1 Motivation Multirobot and swarm systems are of mounting importance as robotics research progresses. Swarms and other multi-agent systems have been suggested as a means to accomplish tasks on the battlefield and in other environments. App lications of swarm inte lligence are clear when tasks are well defined and redundancy among swar m members is the most important factor. Swarm robots are generally equipped with few senso rs, limiting the tasks they can perform alone. Utilizing a group of robots to complete a task is sometimes necessary. These systems have been used for a variety of tasks including Robocup soccer, containing spills, finding and neutralizing mines, foraging, reconnaissance, and surveillance. The need for formation control of multi-robot sy stems performing a coordinated task has lead to the development of a challenging research field. The formation control problem is defined as finding a control algorithm ensuring that multip le autonomous vehicles can uphold a specific formation or specific set of formations while traversing a trajectory and avoiding collisions simultaneously. Formation control of th e group as well as the dynamic response and reconfiguration of a group in varying environments is of particular interest. In this thesis, the concern is to develop a simple control algorith m, which will allow multiple robots to traverse a trajectory in formation while avoiding collisions. The motivating area for the proposed approach is convoy protection where a swarm of robots assigned to protect a convoy of vehicles requires a specific formation around the vehicles. Thus, given a convoy of assets to protect, determining the configuration of the swarm and the arrangement of the support vehicles around the conv oy poses a particularly interesting problem.

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21.2 Problem Statement This research focuses on the ability to modify formation of a heterogeneous swarm given a set of parameters and limiting functions The swarm should be able to recover and reconfigure itself in the event of loss of a team member, loss of configuration or the addition of a new team member. It is also imperative that the form ation be reconfigurable based on dynamically changing parameters. The formati on control problem is defined as finding a control algorithm to ensure that multiple autonomous vehicles can main tain a specific formation or specific set of formations while traversing a trajectory and avoiding collisions simultaneously 1.3 Method of Approach The main contribution is the proposed swarm fo rmation control method that is based on troop movement models [1, 2] satisfying scalability (to varying numbers of swarm members) and supporting multiple formations. The central objective is the overall group formation and behavior, not control of an individual swarm member The proposed solution is based on potential fiel ds generated by bivariate normal functions that are used to control swarm geometry a nd inter-member spacing, as well as manage obstacle avoidance. The rationale behind us ing potential fields for formation control is that they facilitate, in a simple and straightforward way, the repr esentation of multiple constraints and goals in a swarm system. Repulsive and attractive forc es are utilized to control the overall swarm formation. Bivariate normal functions are used to cr eate the vector fields that control the velocity and heading of the robot swarms, with the swarm located on the mathematical surface. Potential fields are not only used in the classical sense for path planning, but also to hold robots in a specified formation while in motion. Fields genera te desired velocities and headings to maintain a path and to dynamically avoid obstacles while ma intaining a formation. Although potential field methods do face the local minima problem [3], the vectors are dynamically changing at each time step so the chances of hitting local mi nima are greatly reduced. 1.4 Research Contributions The contribution of this research is a fau lt-tolerant, scalable swarm formation control methodology. This approach is able to support loose ellipse-based confi gurations and is highly

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3flexible in varying environments. The advantage and difference of this approach compared to similar ones is the simplicity of the vector genera tion. Control parameters are easily modified and adjusted to change formation and relative dist ance between swarm members, while other methods are rigid in formation constraints [4]. Further, in the presented approach, since control parameters may be tuned off-line and used on-line, changi ng formation is comput ationally inexpensive (compared to, for example, the approach in [5] that suffers from computational complexity). The proposed method scales well to different swarm sizes as well as to heterogeneous swarms because the vector field generation is i ndependent of the specific robot vehicle platform, (as opposed to approaches in [6, 7] that are scalable in size but not in heterogeneity). The robot vehicle controller receives the generated vector as input and translates it to the corresponding motion. This presented method does not add communicati on overhead because a central controller is not a necessity. Depending on the mission, robots may intercommunicate on an as needed basis. Although the scope of this work is not on communication issues but on actual control and manipulation of swarm formations, a simple broadcast communication model is implemented in which robots only exchange information wh en predefined points are reached. 1.5 Summary of Results The presented method is demonstrated by simulation using both robot vehicles modeled after an RC-car with Ackerman steering and a simple particle model. A model is derived and simulations using up to ten (heterogeneous) such vehicles are presented, including two formations to demonstrate applicability and performance of the approach. Simulations are performed with up to ten robots, and real-time experiments are run with up to four custom built UGVs. Ellipse, circle, line, and arc formations are demonstrated. The real-world experiments are presented with either three or four autonomous UGVs. In addition, autonomous UAV-UGV swarm coordination is demonstrated. Dynamic formation change is demonstrated as well as continued performance with loss of team members. The results demonstrate that the proposed method can be successfully utilized to control robot swarm formation.

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41.6 Thesis Outline This work consists of eleven chapters each explaining the swarm formation control methodology used. Chapter 2 provi des the necessary background information from mobile robot systems to swarm formation control. Chapter 3 pr ovides the literature review. The most relevant formation control methods are discussed and a comparative study is given. Chapter 4 presents the problem formulation and de tails how the vector fields are generated to achieve formation. In Chapter 5, the simulation system and methods are discussed. In Chapter 6, the robot hardware platform and sensors are descr ibed. In Chapter 7, the software system is discussed. In Chapter 8, the simulation results on simulated and particle robots are presented. In Chapter 9, field experiments with the actual UGVs are presented. In Chapter 10, the approach to formation control is expanded u tilizing deformable ellipses to achieve more formations. Finally, Chapter 11 concludes this research and discusses future work.

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5 Chapter 2 Background Information In order to provide the necessary background knowledge for this research, a brief description of mobile robots is given Section 2.1. Multirobo t systems and multirobot system architectures are described in Section 2.2 and 2.3, respectively, and finally robot swarms and swarm formation control are described in Section 2.4 and Section 2.5. 2.1 Mobile Robot Systems A robot, in the loosest sense, may be defined as a physical agent who conveys by its appearance and motion that it has some level of autonomy. An autonomous robot is one that can perform a specific set of tasks without human supervision as opposed to a teleoperated robot which requires constant human supervision. A robot that has full autonomous capabilities will embody some level of artificial intelligence so the robot can ‘choose’ the right actions and in some cases adapt and/or learn from its ch anging environment or its own choices. For the purpose of this work, the concern is in mobile robotics. Robots are used for civilian and military applications, especially those that ar e dull, dirty or dangerous. They can come in many shapes and sizes and can be classified into 3 groups: unmanned ground vehicles (UGVs); unmanned air vehicles (UAVs); and unmanned underwater vehicles (UUVs). Figure 2.1a and Figure 2.1b are examples of robots used in military applications. Figure 2.1a depicts the Army Research Lab (ARL) UGV Lynchbot which is used for detection of improvised explosive devices (IEDs) in war zones. In Figure 2.1b, the ARL UAV fixed wing drone target is shown. Figure 2.1c is an example of a domestic robot used fo r autonomous vacuuming, and Figure 2.1d is the Mitsubishi service robot, Wakamaru, which can provide services such as care-taking, managing schedules, and reporting unusual activities.

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6 Figure 2.1. Examples of robots. (a) Army Research Lab (ARL) Lynchbot; (b) U.S. Army fixed wing target drone; (c) iRobot Roomba robotic vacuum cleaner; (d) Mitsubishi service robot. Single mobile robot systems have been used fo r security [8] medicine [9], and domestic tasks [10] [11], but in some cases a single robot is not sufficient. For example, in the case of planet exploration or pushing objects [12], the u se of a single robot would be both unrealistic and inefficient. For these tasks, several mobile robot s can be utilized to accomplish a task that would otherwise be very difficult or impossible for a single robot.

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72.2 Multirobot Systems Multirobot systems are of mounting importance as robotics research progresses. Many new systems and experimental platforms have been developed. Recently, several large scale multirobot systems have appeared in the literature Multirobot systems are of interest for tasks that are inherently too complex for a single robot or simply because usi ng several simple robots can be better than having a single pow erful robot for each task [13]. Groups of mobile robots are used for studyi ng issues such as group behavior, resource conflict, and distributed learning. These systems ha ve been used for a vari ety of tasks including but not limited to Robocup soccer [14]; search a nd rescue [15]; terrain coverage [16]; foraging [17]; and cooperative manipulation [12]. Since these application domains and tasks are of increasing complexity, the ability of the robots to cooperate and coor dinate is sometimes necessary. The coordination of multiple robot systems remains a challenging problem. A multirobot system is any robotic system of two or more robots. These robots may be identical or they may contain a multitude of varying systems ranging from slightly different sensors to entirely distinct hardware and/or software platforms. Multirobot systems can further be classified into two groups shown in Figure 2.2: (i) cooperative robot teams and (ii) swarms of robots [18]. Swarms Cooperative Robot TeamsMultirobot Systems Swarms Cooperative Robot TeamsMultirobot Systems Figure 2.2. Multirobot system groups. In the first group, cooperative robot teams, e ach robot generally has different capabilities and control algorithms which when combined can be used to complete a task. In a swarm of robots each robot generally has identical function and capabilities with the goal being the overall group behavior. The focus here is on swarms of robots including UAV-UGV swarm coordination.

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82.3 Multirobot Architectures In order to give the full background necessary, a brief discussion of group architectures in relation to multirobot systems is necessary. The group architecture of a multirobot system provides the framework upon which missions are implemented, and determines the system functionality and boundaries. The key architectural aspects of a group system include: (i) type of control, (ii) team composition, and (iii) communication structures. The most basic decision that is made when d esigning group architectures is defining the type of control the system will utilize. Group control techniques of autonomous robots include: i) centralized-control in which individuals recei ve commands from a central controller and ii) decentralized control where local control laws operating in individual robots produce a desired global, emergent behavior In centralized control methods [19-24], a singl e computational unit oversees the whole group and plans the motion control of the group accordi ngly. Figure 2.3 shows a simplified model of a centralized architecture with the block in the middle being the main control block, all robots communicating through this block. This central c ontrol unit might be an external computer or it might be one of the robots in the group. In centralized control methods, the entire multirobot system is dependent on one controller, so th ese methods are not very tolerant to failure. Figure 2.3. Centralized control model. Decentralized control methods [25-35] lack a central control unit and follow two forms: (i) distributed control and (ii) hierarchical control. In distributed control, all robots are equal with

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9respect to control; in the hierarchical method, control is locally centralized. In distributed, decentralized control methods, the desired behavi or is produced using only local control laws operating on individual robot members. These local control laws depend on the specific model and methodology used. A key feature of centralized control is that each of the robot members communicates directly with each other as shown in Figure 2.4. Decentralized control methods are advantageous over centralized ones in that they are more fault tolerant, scalable, and reliable. Figure 2.4. Distributed, decentralized control model. Conventionally, most systems do not conform exactly to either a centralized or decentralized architecture. Instead, the emergent architecture tends to be a hybrid of the two, utilizing, for example, a hierarchical leader structure or even virtual leaders. In addition to the type of control used in a multirobot system, the team composition and size of the system is also important. A multirobot sy stem may either be made up of homogeneous or heterogeneous units. A group of robots is said to be homogeneous if the capabilities of the individual robots are identical and heterogeneous otherwise. Any difference in software or hardware can make a robot differe nt from another. Higher levels of heterogeneity introduce more complexity in task allocation since robots have di fferent capabilities. This requires agents to have a greater knowledge about each other. In a hete rogeneous system, it is necessary to prioritize a robot’s tasks based on its capabilities, whereas in a completely homogeneous system all robots have equal capabilities and priorities. The team si ze can either hinder or help depending on the task and team composition.

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10Within a multirobot system, robots may communicate following several information structures, including: (i) interaction via the envir onment, (ii) interaction via sensing, and (iii) interaction via communications [13]. Interaction by means of the environment involves using the surrounding features as the communication medium. An example of interaction via the environment would be some type of landmark na vigation [36]. Interaction via sensing involves using sensory data such as range measurements to sense other robots. Finally, the third form of interaction is explicit communication through eith er directed or broadcast messages. This communicated information could be any necessary data such as position information or images necessary to the mission. All of these architectural aspects will determ ine the abilities of both the robots and the entire multirobot system. The type of control and leve l of communication in the system will determine how cohesive and coupled the system is, and how dependent each member is on the other team members. Only in a fully distributed system can you have the lowest level of agent dependency and highest level of fault tolerance. 2.4 Robot Swarms The swarm-type approach to multirobot cooperati on traditionally deals with large numbers of homogeneous robots with limited sen sors. Swarm robot systems have been defined to include ten or more robots, but this bound is relaxed in this work to include any system with two or more robots. Swarms are usually made up of robot s with similar controllers, and instead of a centralized controller, they employ one or more su pervisors or leaders. Further, swarm control systems are usually focused around decentralized control methods which make them more robust to loss of team members. For the purpose of th is work, the definition of a swarm is relaxed and extended to include any group of robots, homoge neous or heterogeneous, in which the goal is some global emergent group behavior Research on robotic swarms [37] typically involves mimicking nature [38], [39] and observing animal groups accomplishing tasks an individual cannot accomplish alone [40], [41]. Figure 2.5 represents examples of swarms in nature a school of fish, a flock of birds and a swarm of bees.

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11 Figure 2.5. Examples of swarms in nature. When studying swarms of robots, many factors must be considered [37] including: (i) swarm composition and size; (ii) communication issues; and (iii) swarm reconfigurability. When a swarm system is designe d, swarm composition and size must be taken into account. Both the heterogeneity of the swarm system and the number of robots that will be in the environment are important issues which are dependent on the task and operating environment. A larger swarm is not always better and could actua lly hinder task achievement, and vice versa. Determining the optimal number of swarm members fo r a particular task or mission is a problem by itself. Communication issues are a huge concern especia lly as the number of robots in the swarm increases (N ). Communication should be used conser vatively. The idea is to use a minimal amount communication given the mission. The robots do not necessarily have to obtain all information through explicit communication, becau se it is possible robots to obtain information about the swarm via other sources of information su ch as sensory data or the environment. The following aspects of communication must be considered in swarm communication design: The communication range of the system is finite. In a swarm of robots, the range of direct communication to any single robot is limited. Both the communication medium and the spatial distribution of the sw arm will affect the communication range.

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12 The communication topology of the system will determine how robots can communicate with each other regardless of their proximity. Depending on the communication topology chosen robots may not be able to communicate directly with any arbitrary member of the swarm; or they may be able to communicate directly with any member of the swarm. The communication bandwidth of the system is also finite. Communication can be cheap in terms of a robot’s processing time, if there is a dedicated channel, or it could be computationally expensive keeping the robot from doing other necessary work. Swarm reconfigurability is the rate at which the swarm is able to spatially reorganize itself. Depending on the method, the swarm can have a st atic arrangement or a dynamic arrangement. How members move among each other and spatially distribute is a problem alone and is the main focus of this work. This aspect will further be discussed in next section. 2.5 Swarm Formation Control The problem of formation control of multi-robo t systems performing a coordinated task has become a challenging research field. The pattern formation of a multirobot system is the organization of the robots into a particular shap e such as a wedge or circle. In addition to a specific geometric pattern formation, an approxima te or loose geometric pattern is referred to flocking. In this work, the focus is both on speci fic and loose geometric formations. The shape or formation of the swarms is highly task and e nvironment dependent. Current applications of pattern formation include convoy support [4245], chemical source localization [46], and unmanned aerial vehicles [20, 47, 48]. Pattern formation can be observed directly in biol ogical systems. Flocks of birds, schools of fish, and swarms of bees form complex dynamical spatial patterns that emer ge when each animal in the group follows a simple set of rules exhibiting emergent swar m behaviors [49]. Ants are a particularly good example of emergent swarm beha viors. In coordination between groups of ants, the control is not hierarchical or central, but the ants coordinate locally to achieve colony level goals [49]. Various approaches to ground-based formation c ontrol are behavior-based control [25, 50, 51], graph theoretic [52, 53], algorithmic [35] and virtual structures [54]. In Chapter 3, a detailed survey and comparative study of formation control and flocking methodologies are presented.

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13The particular type of methodology used in formation control is highly dependent on many factors including [55]: The formation stability or accuracy of the system will determine if a very tight formation control methodology or a looser formation control method will suffice. The stability of a formation refers to the propagation of errors th rough the system. In some applications, it is necessary to have a very low error bound so a very tight, stable formation control method is necessary. The scalability of a formation refers to a swarm’s ability to reconfigure in the event of removal or addition of a swarm member. In anonymous robot swarms where specific robots are not distinguishable, scalability is mu ch better. In swarms utilizing a leaderfollower based methodology, the positions of th e added robots are in relative to one or more other members. In the case of robot rem oval, reorganization needs to occur. This type of system needs to be very well d esigned to tackle the scalability problem. The formation control task can be divided into establishing a formation and maintaining a formation While the task of establishing the initia l formation may be trivial, the act of maintaining a formation while traversing a terr itory or performing a particular mission is a large challenge. The ability to dynamically modify a formation from one shape or another for any reason such as obstacle avoidance increases the difficult y of formation control. In the event of anonymous swarms, this task is much simp ler than in the leader-follower formation structures. All of these factors will contribute to the design of the swarm architecture and formation control mechanism. The operating environment and types of missions will factor in when making design decisions.

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14 Chapter 3 Literature Review In this section, current formation control met hodologies will be evaluated and compared with the proposed approach. A comparative stud y on existing multirobot and swarm formation methodologies is presented. Formations discussed and compared include both specific geometric formations and flocking formations. Only mu ltirobot systems focusing on formation control are included in this comparison. The formation cont rol strategies are analyzed from their control methods and shape representations. The most fundamental and key aspect of formation control is the method of control used in the multirobot or swarm system. The control method follows three different types, including (i) centralized, (ii) completely decentralized, and ( iii) hybrid. The majority of the approaches are hybrid. The robots need to maintain a specific formation shape while maintaining the correct formation position among other r obots. This formation position is determined based on the particular reference method used. Robotic control is used to get to the correct formation position. In Section 3.1 formation control and shape repre sentations are discussed. Section 3.2 discusses the foundation and basis for the approach in this research, and finally Section 3.2 summarizes the literature review. 3.1 Formation Control Methods In this section different control and shape repr esentation methods are surveyed. In formation control for a group of robots, different contro l topologies can be adopted depending on the specific scenarios and/or missions. There may be one or more leaders in the group, while the other robots follow one or more leaders in a specified way. Each robot has onboard sensing and computation ability. In some applications, robot s only have limited communication ability. In general, global knowledge about the system is not available to each robot. A centralized

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15controller is not utilized, and in this case the d esign of each robot controller has to be based on local information. If there is no assigned leader, th en each robot must coordinate with the others by relying on some global consensus to achieve the common goal. Various types of shapes have been employed in formation control. The specific shape might be scenario or mission dependent. The more common formation shapes are column, line, wedge, triangle, and circle. Some of these shapes are shown in Figure 3.1 where individual robots are represented by the circles. Figure 3.1. Examples of formation shapes with three robots. The concept of formation control has been studied extensively in the literature with applications to the coordination of multiple r obots [56-63] unmanned air vehicles (UAVs) [20, 47, 64, 65], autonomous underwater vehicles (AUV s) [66, 67], and spacecraft [68-70]. Four main control frameworks have emerged to address th e multi-agent formation problem including the behavior-based and potential fields, leader-follo wing, graph-theoretic, and virtual structure approaches. Tables 3.1, 3.2 and 3.3 and the following sections summarize relevant work in formation control. Table 3.1 shows forma tion control methodologies with implicit robotic control. Table 3.2 shows formation control methodologies where control is explicit and leaderfollower based approach. Table 3.3 shows the re mainder of explicit robotic control formation methodologies. In Section 3.1.1, behavior-based and potentia l field formation control strategies will be discussed, followed by leader-follower and graphtheoretic techniques in Section 3.1.2, and finally virtual structures in Section 3.1.3. In S ection 3.1.4, other formation control strategies are discussed.

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16Table 3.1. Formations with implicit robotic control. System Shape RepresentationFormations Reference Type Robotic Control Sugihara [35] Implicit geometric shortest or farthest neighbors algorithmic Yun [71] Implicit line, circle neighbor-based algorithmic Balch [25] Implicit column, line, diamond, square Attachment sites behavior-based Table 3.2. Formations with explicit robotic control utilizing leader-follower reference. System Shape RepresentationFormations Reference Type Robotic Control Balch [50] Hardcoding line, wedge, column unit-center, leader, or neighbor behavior-based Egerstedt [22, 72] formation constraint function triangular virtual leader and reference points tracking Fredslund [28] chain of friendships line, column, diamond, wedge friend robot (leaderfollower) desired angle keeping Das [52] Graph triangle, line, circular arc directed edge (leaderfollower) control algorithms Hsu [73] Graph line, column, wedge directed edge (leaderfollower) potential field and behaviorbased Gazi [74] potential function aggregati ons local interactions sliding-mode Ren [75] virtual structure geometric configurations virtual center, neighbors trajectory tracking Elkaim [76] predefined points ring, line, pyramid, box virtual leader potential functions Chuang [63] Ball flocking virtual leader pairwise potentials

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17Table 3.3. Formations with explicit robotic control utilizing other reference types. System Shape RepresentationFormations Reference Type Robotic Control Barfoot [58] formation offsets geom etric point-referenced motion planning Lewis [54] virtual structure geometric configurations virtual structure points bidirectional Chaimowicz [62] shape functions convergence to a given 2D curve local sensing (closestneighbors) gradients Hsieh [77] shape functions convergence to the boundary of a specified shape relative neighbors artificial potential functions Ji [78] Graph not specified interaction graph nonlinear control Ge [79] queues and formation verticies wedge, column, line, circle queue status potential trenches 3.1.1 Behavior-Based and Potentia l Field Formation Control Strategies Behavior-based systems integrate several goal or iented behaviors concurrently in order to reach a goal. In the behavioral approach to fo rmation control [19, 27, 50, 80-85], each agent has several desired behaviors, and the control action for each swarm member is defined by a weighting of the relative importance of each beha vior. In addition there are many formation control strategies which utilize potential fields [ 25, 74, 76, 86-90]. Behavior-based methods and potential fields are often combined in formation control of mobile robot systems. In [19], a behavior based formation control method is used in which a lead er is referenced to determine formation position. Each of the robots is equipped with some primitive motor behaviors. The behaviors have control paramete rs which are tuned usi ng a genetic algorithm. During the motion, the leader decides its next position based on its knowledge about the goal and environment and then broadcasts its anticipated position to the followers. The use of genetic algorithms for optimizing the formation control is inte resting, but the drawback is that the system

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18requires almost global knowledge about the enviro nment and is dependent on receiving this via broadcast communication. In [50], the behavioral approach is applied to formation-keeping for mobile robots, where control strategies are consequent of several simu ltaneous behaviors. In this approach, line, column, diamond and wedge formations are presented. For each formation, each robot has a specified position based on an identification number. In [91], complex formation maneuvers are br oken down into a sequence of behaviors to achieve formation patterns. A bidirectional ring topology is used to ma intain the formation of the system. The advantage of this approach is that it can be implemented when only neighbor position information is available. Because of the way formation patterns are defined, this approach is limited in directing ro tational maneuvers for the group. In [69], a behavioral-based approach is used to obtain formation control laws to maintain attitude alignment among a group of spacecrafts. Th e approach utilizes velocity feedback and the other passivity-based damping. Behavior-based methods and potential fields ar e often combined in formation control of mobile robot systems [50, 82]. In these appr oaches, each robot has basic motor schemas which generate a vector representing the desired behavi oral response to sensory input. These motor schemas include behaviors such as obstacl e avoidance and formation keeping. In [25], a strategy to arrange a large scale, homogeneous team in a geometric formation utilizing potential functions is presented. The meth od is inspired by and is similar to the process of molecular covalent bonding. Various robot formations resu lt from the usage of different attachment sites. Attachment sites are construc ted relative to the other agents in the team. Formation is maintained in the presence of obstacl es. Local sensing is sufficient to generate and maintain formation. Robots are not assigned sp ecific locations, but attracted to the closest teammates or attachment sites. Behaviors such as “move to a goal” and “avoid an obstacle” are utilized for robotic control. In [27], formation control is achieved vi a a group formation behavior based on social potential fields. The robot’s behavior is base d on a subsumption architecture where individual behaviors are prioritized with respect to others. This work extends the work in [85] using the social potential fields method by integrating ag ent failure and imperfect sensory input. This method uses only local information and is scal able to very large groups of robots. In [92], the behavior-based formation control is modeled by a non-linear dynamic systems for trajectory generation and obstacle avoidance in unknown environments. The desired formation

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19pattern is given through a matrix which includes pa rameters to define the leader, desired distance, and relative orientation to the leader. These paramete rs are then used to shape the vector field of the dynamical system that generates the control variables. In [79, 93], the desired formation pattern is represented in terms of queues and formation vertices. The desired pattern and trajectory for the group of robots is represented by artificial potential trenches. Each robot is attracted to and moves along the bottom of the potential trench, automatically distributing with respect to each other. Although the behavior-based approach has the advantage of formation feedback through neighbor-based communication and it is highly decentralized; it is extremely difficult to analyze mathematically and has limited ability in precise ge ometric formation keeping. If a very precise formation is required, another method, such as a virtual structure method, should be used. 3.1.2 Leader-Follower and Graph Theoretic Formation Control Strategies The leader-follower [94, 95] and graph-based [ 26, 34, 53, 96] approaches are decentralized, but each robot is assigned a unique name. Also, the robots typically try to maintain some desired distance to some of their neighbors and/or virtual points. Some robot members are designated as leaders while other robots are designated as follo wers. The structure is dependent on the architecture utilized. There can be as few as one leader, or there can be several leaders with a hierarchical structure. The leaders are gene rally tracking a predefined trajectory, and the followers are tracking their leader(s) in some ma nner dependent on the approach used. There are many approaches for maintaining formations utiliz ing a leader-follower or graph-based method. The objective could be to maintain a desired le ngth and/or desired relative angle from leader robot(s). In [28, 97], local sensing and minimal communicat ion between agents is used to maintain a predetermined formation. Each robot in the group keeps a single ‘frie nd’ robot which it knows via a special sensor, and maintains a specific angle at all times in relation to this ‘friend’. The angle is calculated locally per robot. Broadcast communication is utilized but is minimal, only passing robot IDs, directional changes, and fo rmation messages. Each robot has access to the number of robots in the system as well as the type of formation. With each formation, each robot has a specified angle to keep between its friend and the frontal direction. This algorithm is limited to only formations which can adhere to th e chain of friendship which limits it to no more than two loose ends or frontally concave formations.

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20In [22, 72], a coordination strategy for main taining formation over a given trajectory is presented. The formation control is achieved thro ugh the tracking of virt ual reference points. The leader of the path acts as a reference point for the robots to follow. The robots move in a triangular formation avoiding obstacles, and if th e tracking errors are bounded then the formation error is stabilized. In [32], artificial potentials define the inter action control forces between adjacent vehicles and define the desired inter-vehicle spacing. Th e approach is inspired by biology considering attraction and repulsion to neighbors as well as ve locity matching. Virtual leaders or beacons (not an actual vehicle) are used to manipulate group geometry and motion direction. Constant prescribed formations of schooling and flocking are demonstrated, but the approach is only applicable to homogeneous formations. Closed -loop stability is proven with Lyapunov function using kinetic and potential energy of the robot system. In [31], an approach is provided for multirobot formations including obstacle avoidance using local communication and sensing. The approach is behavior-based but integrates social roles representing positions within the formation using local communication to improve performance. New agents are allowed to join the formation by role changes when necessary. The local communication is fixed, and the locally informa tion travels to the leader which knows the entire shape of the current formation and decides on neces sary role changes. This information is then passed back to the necessa ry followers, updating the information. The roles or positions for the robots are decided dynamically and changed as the formation grows. In [98], leader-follower patterns are used for formation control. In this approach, it is assumed that only local sensor based information is available for each robot. In [99], a fuzzylogic based leader-follower appro ach is presented for formation control. Maintaining correct formation position while avoid collis ions is investigated here. Se parate fuzzy-logic controllers are developed for formation position control and internal collision avoidance. Circle, wedge, line, and column formations are presented. In [34], a graph theoretic approach is described which allows multiple vehicle formations to be defined as rigid graphs. In [89], the authors also show a graph theory called graph rigidity which is very helpful in representation and contro l of formations of multiple vehicles. These rigid graphs identify the shape of the formati on and the interconnections lead to automatic generation of potential functions. The basis is that performing graph operations allows the creation of larger rigid graphs to be formed by combining smaller rigid sub-graphs. This work

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21has specific applicability in the area of dynamic reformation as well as splitting and merging of vehicles in a distributed manner. In [26], a graph theoretic framework for form ation control of moving robots in an area containing obstacles is presented. Control grap hs are used to determine the behavior and transitions that are possible between different form ations or control graphs Each team’s model consists of a lead robot, shape variables contai ning relative positions of robots, and a control graph that describes the behaviors of the robots in the formation. This method is scalable to large groups despite the computational complexity of gr owing control graphs due to its decentralized design. In [100], another graph-based approach consisting of a four-layer hierarchical modular architecture for formation control is proposed. The group control is at the highest level layer generating a desired trajectory for the whole group to move. The next layer manages formation control implementing a physical network, a communication network, and a computational network (control graph). The formation is ma intained by using only local communication and relative position. Next, there are two layers, one to control robotic kinematics and one to handle robot dynamics. This system is very scalable to heterogeneous systems because of the layered approach with the adaptable kinematics and dynami c layers. This method also allows for various formations, and both centralized and decentra lized methods of control graph assignment are described. In [30], nearest neighbor rules are used to control the motion of the robots updating each robot’s heading based on the average of its head ing plus its neighbors’ headings. Undirected graphs are used to represent robot interactions. This method claims that all robots, despite the absence of a centralized coordina tion mechanism and the dynamic neighbor changes, that there will be an overall emergent coordinated motion. No particular formation is exhibited but overall robot motion is in the same direction. While the leader-follower and graph-theoretic a pproaches are logical and easily implemented, there are limitations. Each leader is a single point of failure for the formation so this makes these systems weaker than completely decentralized systems. Reassi gning leadership and information flow in the event of a failure can be difficult and computationally expensive. In addition, if there is no explicit response from the followers to the leader, and if the follower has a difficulty, the formation cannot be maintained.

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223.1.3 Virtual Structure Formation Control Strategies In the virtual structure approach [29, 54, 101], the entire formation is treated as a single rigid body. The concept of the virtual structure was first introduced in[101]. The virtual structure approach is typically used in spacecraft or sma ll satellite formation flying control [68]. The virtual structure can adapt its shape expanding in a specified direction while maintaining a rigid geometric relationship among multiple agents. These approaches were proposed to acquire high precision formations control for mobile robots. In [29], a virtual structure method is proposed for self-organizing formation control in which it is assumed that elements are not connected to each other and can move in a continuous space. The goal is to arrange the elements into the spa tial pattern of a crystal using virtual springs to keep neighboring elements within close proximity. Each pair of elements within a certain range is connected with a virtual spring. The elements form triangular lattices with random outlines. In order to determine the desired outline, virtual sp rings are broken with a certain probability. The candidate springs for breaking are chosen based on the connections of its neighbors. Elements interact locally and have no global information, but the tuning of parameters for different formations and number of robots is computationally expensive. The main advantage of the virtual structure and graph theoretic approaches to formation control is that it is simple to prescribe the beha vior for the entire group. The formation structure is generally very tight and precise in these methods during tasks. The main disadvantage is in the computation complexity of some these methodol ogies, as well as the centralized nature which make these systems less robust to failure. 3.1.4 Other Control Strategies in Formation Control There are also many other formation contro l strategies which do not easily fit into the categories previously discussed. In [33], a di stributed coordination algorithm is presented for multirobot systems in which a particular method of navigation function with Vornoi partitions is used. The robots navigate, maintaining a flocki ng formation, while avoiding obstacles. Intervehicle communication is achieved by using a gl obal list of positions where every single vehicle can only get a list of its neighbors within a specified radius. In [24], the formation problem is solved for a group of autonomous vehicles by providing inter and intra vehicle constraints as well as a time limit for reconfiguration. The nominal input

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23trajectory for each vehicle is determined so that the group begins in the initial position and ends in the final position in the specified amount of time. The information is represented as a particular form of input signals so the formati on problem can be reformulated as an optimization problem and solve more efficiently especially for large groups of vehicles. This method suffers from single point failure, though, since it utilizes a central controller. In [102], the stabilization and maneuvering of vehicles is achieve through model predictive control. Each individual vehicle may be governed by nonlinear and constrained dynamics. The vehicles are stabilized to acceptable equilibriums rath er than precise locations for each individual. The individual trajectories of autonomous vehi cles moving in formation were generated by solving an optimal control problem at each time step. This is computationally demanding and hence not possible to perform in real-time. In [103], a distributed control scheme for multirobot systems is presented. Each robotic vehicle has its own coordinate system, and it senses its relative position and orientation in reference to others in order to create a group form ation. Despite the presence of a supervisor, the robot vehicles are stabilized. The stability of the vehicles is proven only for symmetrical formations. In [35], approximate pattern formation is ach ieved by sharing position information to other robots. In this method, it is assumed that robots have global position information. An algorithm is developed for each pattern formation which incl udes circles, polygons, lines, filled circles, and filled polygons. Robots can also be split into an arbitrary number of equal or near equal group size. Although this method is decentralized, th e information sharing of the global position of each group member to the whole group is a significant drawback. In [23], a target assignment strategy for form ation building of multiple robots scattered in the environment is presented. The algorithm first begi ns with assigning each robot a target point in the desired formation. Trajectories, including collision avoidance, are generated by a central planner. Priorities of areas around the robots are integrated so robots will avoid each other when in a certain threshold. Sensing is global a nd the method is dependent on a central controller. In [104], a formation control methodology b ased on generalized coordinate system is presented. The generalized coordinates characteriz e the vehicle’s location, orientation and its shape of the formation. This allows the group to be controlled as a single entity. Force-based and velocity-based controls are developed. Simila r ideas utilizing coordinated systems for shape representations are presented in [105, 106].

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24In [64], a hierarchical, centralized planning me thod to achieve a desired formation for a group of UAVs is presented. The desired flight traject ories for each UAV are determined by a leader which is more capable than the other team memb ers. Control laws are designed based on the desired flight trajectories. To achieve flight formation according to a given scenario, each UAV independently takes off towards its corresponding traj ectory and locks onto it in finite time. Only the leader is equipped with sensors, and it communicates to the other team members what trajectories to track via a communication channel. This method is very prone to failure, is very risky in dynamic environments, and scales ve ry poorly with growing team sizes. In [20, 21] swarms of unmanned ground vehicl es (UGVs) are coordinated with the use of unmanned aerial vehicles (UAVs). In [21], a hierarchy is formed between the UAV and the UGVs. The UAV is in charge of determining th e grouping and merging of swarms as well as the swarm distribution and motion of the group. The UGVs are at the lowest level of the hierarchy. At the second level, there is one UAV blimp for the tracking of each group of UGVs, and at the highest level there is a centralized planner for the whole system, a single UAV. This system is centralized with each robot communicating to its cen tral planner. The central planner broadcasts information to the robots as well as sending info rmation to the UAVs. The shape of the formation is determined by the central planner in the form of a directed graph. Due to the dependence on a central planner, this method is prone to failure, but the UGV swarm-UAV coordination proves interesting. There are also many other methods in the appli cation of formation control. In [107], genetic algorithm and reinforcement learning are used for robot formation control and obstacle avoidance. In [108], neural ne tworks and radial basis functions are used to achieve formation control. Vision is used for formation control in [52, 94, 109-112]. 3.2 Foundation of the Proposed Approach In this work, a new approach for swarm formation control is set forth. Many limitations that other methods suffer from can be solved utilizing this approach. This approach most closely falls into the potential field and leader-follower control for swarm formation. The architecture utilized is best classified as hybrid or hierarchical, ra ther than a purely decentralized. Most of the formation methods discussed assume that the swarm members begin there activities in the mission space, and do not address how the robots travel to that mission space. This assumption is not acceptable in scenarios where groups of unma nned vehicles need to travel autonomously from

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25one region to another of the mission space while re maining a cohesive unit. In this approach, no assumptions are made about where the swarm member s begin activities. This approach utilizes ellipsoid contours for shape representation and artif icial potential fields a nd limiting functions for control. Although this approach does not achieve the precision that is seen in virtual structure methods in [54, 75, 101], robots adhere to set formation parameters, including an approximate shape and dispersion. The robots adhere to a semi -static formation while moving. They are also able to change formation dynamically to respond to the loss of a team member or environmental change. The contribution of this work is a swarm formation control methodology which is scalable to varying swarm sizes, computationally efficient, supports multiple formations, dynamic formation switching, decentralized and centra lized control techniques, and heterogeneous swarms. This framework and methodology for swarm form ation control allows heterogeneous robots of ground vehicles to maintain formation while avoiding collisions and handling dynamic changes in the environment. The design is not de pendent on the size of the swarm or the type of platform and is transferable to multiple form ation types including line, wedge, circles, and ellipses. Two types of formation reference are u tilized in this work. On utilizes unit centers to define the formation. The other utilizes a hierarchical structure of leaders. Artificial potential fields are the basis for the swarm formation control, obstacle avoidance, and overall swarm formation movement. This wo rk is based on the troop movement models discussed in [1, 2]. In [1], reaction diffusion equa tions (RDEs) are used to describe behavior and control the troop as a group. Troop behavior is described with RDEs, also describing motion and diffusion of the troop. In [2] troop movement model is extended by combining Variable Resolution Terrain (VRT) model [113] and RD Es. VRT was developed to represent the battlefield as a continually differentiable surface. When VRT is combined with RDE, it creates a simulation tool to model troop move ment on simulated battlefields. 3.3 Summary This chapter has provided a survey of the resear ch in multirobot formation control. There are a variety of approaches to the multi-robot forma tion control problem. Depending on the task or mission, one method might be better than another. Th is work is most similar to [77, 79, 93] but it is much more applicable across control platfo rms. It can be utilized with a centralized

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26architecture design, hierarchical architecture, or purely decentralized depending on communication requirements for the group. Obstacle information can either be locally sensed or broadcast through the group. Unlike the approach in [51], this approach is also platform independent. In this work, [45] is expanded to achieve tighter formation control with fewer potential fields utilized. In a static formation, the stability and convergence of all the robots to the boundary of a specified ellipse is guaranteed and can be shown mathematically. Swarm member can traverse a trajectory while avoiding other swarm members and static and dynamic obstacles. The main contributions of this work are the concept of u tilizing a sum of multiple vector fields based on a simple surface, the bivariate normal, with few pa rameters for formation control for a swarm of arbitrary size. The ability to change formation, and redistribute the robots makes this approach very dynamic. Conventional methods which utilize potential fields using predetermined points of attraction limit the formation’s scalability. Utilizing the bivariate normal function as a basis, a variety of formations can be achieved by robots dispersing about the contours.

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27 Chapter 4 Proposed Formation Control Approach 4.1 Swarm Surface The main objective the proposed approach is to attract elements of a swarm into a bounded formation and allow the swarm to stay in that formation as it moves around. A vector field is used to attract swarm members to an ellipse with desired para meters. The minimum distance between swarm members is controlled using an additional vector field. At any instant in time, the robots can be visu alized as particles moving in a potential field generated from a bivariate normal ‘hill’ that cont rols the velocity and heading of the swarm members. A bivariate normal function with form given in: 22(()()) (,)ccxxyy fxye (4.1) produces an oval/ellipsoid shaped function. Assu ming that the current robot location is at (x, y) the center of the function in (4.1) is represented by (xc, yc) with respect to the world reference frame. The control variable determines the ratio of the minor axis (y-direction) to the major axis (x-direction) affecting the eccentricity of the swarm. Note that the center (xc, yc) could be a function of time allowing the swarm to move along a path. The x and y partial derivatives create the velocity vectors that are used to determine the heading and velocity of each member of the swarm as shown in: 2(,)() 2(,)()xc ycdfxyxx dfxyyy (4.2)

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28Just as with a single robot, the swarm formation, treated as a single shape, has both a local reference and a world reference frame. For the sw arm to follow a trajectory in the world reference frame, an axis rotation is required. The heading, between the swarm formation’s x -axis and the center ( xc, yc) must be found; the rotated coordinates for ( x, y ) and ( xc, yc) can be found using: cos()()sin()() sin()()cos()()rotcc rotcc x xxyy yxxyy (4.3) The rotated coordinates are then substituted to find dx and dy. 4.2 Formation Problem In order to describe the general formation pr oblem, it is discussed in reference to convoy protection. Suppose that a swarm of robots needs to accompany a convoy of vehicles and surrounding them in a particular formation. In the general case, the convoy can be enclosed in some geometric shape, defined loosely by dimensio ns, direction of travel, and the center of mass as shown in Figure 4.1. The length of the convoy al ong the axis of travel is 2A. The width of the convoy with respect to the axis of travel is 2B. A field needs to be designed to attract th e swarm members to surround the convoy in a designated formation. The swarm members need to be close enough to the convoy to offer protection, but far enough to allow the convoy to move safely. Direction of Travel: 2 B 2 A Direction of Travel: 2 B 2 A 2 B 2 A Figure 4.1. Convoy description.

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29Suppose the positions of each of the convoy vehicl es are known and that the centroid of the convoy is (xc, yc) It is possible to enclose the convoy w ithin a sequence of concentric ellipses with center (xc, yc) Figure 4.2 depicts three elliptical rings with center (xc, yc) semi-major axis A and semi-minor axis B surrounding a convoy of vehicles. By attracting swarm members to the center elliptical ring described as the set of points (x,y) 2 satisfying: *222()()cc R xxyy (4.4) where (xc, yc) is the center and is the axis ratio B/A the swarm can be closely associated with the convoy without endangering the convoy vehicles. For a fixed value of we will refer to the set of points (x, y) satisfying (4.4) as the R* ellipse. The general form of the swarm controller is described by: 1(,,)(,,)(,,)N iiVxytwxytVxyt (4.5) where V(x,y,t) gives the velocity of the swarm at a partic ular time and place. Each of the vectors Vi(x,y,t) is associated with different fields and wi(x,y,t) are weights of the overall contribution of the ith vector. In general, the field V(x,y,t) is the weighted sum of N different vectors, each of which is acting on the swarm. In this case, three different vector fields ar e utilized: one attracts robots to the elliptical band from points outside th e elliptical region; one pushes robots away from the center towards the desired band; and one cont rols the movements of the robots within the band). AB(xc,yc) AB(xc,yc) Figure 4.2. Convoy of vehicles surrounded by concentric ellipses.

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30The challenge is to develop a potential fi eld based controller using a small number of physically relevant weights, wi, and vectors vi that attract particles to a neighborhood of the R* ellipse. This neighborhood is shown in Figure 4.3. The parameters Rin and Rout denote the inside and outside boundaries of the R* neighborhood, respectively, as shown in Figure 4.3. The desired vector fields will ‘trap’ the robots in thes e bands. Typically, this is a very narrow band of allowable space for the robots with a controllable width of Rin+ Rout where: inin R RR (4.6) outout R RR (4.7) R* R*RinR*+ Rout (xc,yc) R* R*RinR*+ Rout (xc,yc) Figure 4.3. Elliptical attracti on band for the swarm robots. The vector field will be constructed utilizing th e normalized gradient from Equation (4.2). For every (x, y), let the gradient field vector have the form: () 1 (,)(,)(,) () (,) (,) 0 (,)(,) 0c icc c i ccxx Wxyforxyxy yy Lxy Vxy forxyxy (4.8)

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31where: 222(,)()()ccLxyxxyy (4.9) The vector () 1 ()(, c cxx yyxy L) is a unit vector that provides the direction of the vector at (x, y). The function w(x, y) provides the magnitude of the vector at that point. Notice that for any (x, y), this vector points away from the center of the ellipse. In the defined vector field, particles starting within the R* R ellipse with: *222()()cc R xxyy (4.10) move out from the center until they reach the R* neighborhood. Particles starting outside the R* +R ellipse move toward the center until they reach the R* neighborhood. Eventually all the robots will be trapped within the neighborhood given by: **()()inout R RRRR (4.11) 4.3 Generation of Vectors and Vector Field 4.3.1 Description of Vector Fields In order to generate the desired vector fields to hold the robots inside the R* neighborhood, three fields are needed. One attr acts robots to the elliptical band from points outside the elliptical region. One pushes robots away from the center towards the desired band. One controls the movements of the robots within the band. The firs t two fields utilize the gradient vector field discussed in the previous section, G= -(dx ,dy) points away from the center as shown in Figure 4.4. Vector calculus dictates th at the gradient vector field, G+ = (dx, dy) points in the direction of greatest increase of the function f(x,y), which is towards the center as illustrated in Figure 4.5. The third vector field utilizes a vector that is pe rpendicular to the gradient vector. The vectors

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32(dx, -dy) and (-dx, dy) are perpendicular to the gradient; Figure 4.6 shows such a perpendicular field. Figure 4.4. Vector fields directed away from the center (G-). Figure 4.5. Vector fields directed towards the center (G+).

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33 Figure 4.6. Vector fields direct ed perpendicular to the center (G). 4.3.2 Description of Limiting Functions Tighter swarm control may be accomplished when restricting the influence of the vector fields to a small region of the x-y plane by multiplying each of the fields by a limiting function. This limiting function controls the influence of the vector field in various regions of R2. For instance, the limiting function can determine the di stance from the center at which the vectors in the field ‘die out’ or become smaller than some number 4.3.2.1 Sin and Sout Sigmoid Limiting Functions In order to create the desired field, the Gand G+ fields as shown in Figure 4.4 and Figure 4.5 must be limited to end at the appropriate boundaries. These fields will be limited with sigmoid functions or S-shaped functions of the form: 1 () 1tSt e (4.12)

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34 Figure 4.7. General sigmoid function. Figure 4.7 shows the general case of a sigmoid function. The value of the sigmoid function ranges between 0 and 1 and has one inflection point at 0.5. Vector fields ‘moving away’ from the center (the vectors inside of the ellipse) require a limiting function that approaches zero as the di stance from the center increases; such a limiting function is given by: ** (()1 (,,,)1 1in ininininin rRRSrRR e (4.13) Gradient vector fields directed towards the center (those vectors outside of the ellipse) are required to approach zero as the vectors ‘move to wards’ the center; this is achieved using the limiting function by: ((*)1 (,,,)1 1outoutoutoutout rRRSrRR e (4.14) The Gfield should die out at R*-Rin, and the G+ field should die out at R*+Rout. This creates the field shown in Figure 4.8.

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35 Figure 4.8. Combined in (G+) and out (G-) fields. Although Figure 4.8 illustrates a symmetric case where Rout = Rin, the weight, W(x,y), can be written as: (,)(,)(,)inoutWxySxySxy (4.15) so that the inside and the outside of the R* ellipse can be considered separately. As a simplification, a modified distance function, r, will be used where: 222()()rotrotrxy (4.16) which can be simplified (using basic trigonometry) to:

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36 222()()ccrxxyy (4.17) So W(x,y) becomes: ()()()inoutWrSrSr (4.18) Notice that r is never negative. The plot of the functions Sin and Sout as a function of r is provided in Figure 4.9. Sout has its largest influence at points whos e distance from the center of the ellipse is small. Sin has its greatest influence at points whose di stance from the center is large. Neither function has much influence within the R* band. Convergence of the Sin and Sout limiting functions to the R* band is shown mathematica lly in Section 4.3.2.1.3. Figure 4.9. The weighting functions Sin and Sout as a function of the weighted distance r defined in (4.17).

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37Each of the limiting functions in (4.13) and (4.14) contains tuning parameters that may be used as vector field control variables. For each function, one tuning pa rameter determines how quickly the function approaches zero. The parameters in and out control the slope of Sin(r) and Sout(r), respectively, for r in the set R – Rin < r < R + Rout. These parameters will be defined so that the value of Sin(R*) and Sout(R*) can be made arbitrarily small. Derivation of the -control variables from the Sin and Sout limiting functions is discussed in Section 4.3.2.1.1 and Section 4.3.2.1.2. Selection and tuning of the R-values is discussed in Section 4.4. 4.3.2.1.1 Mathematical Solution for in-Control Variable In the designed vector field, particles starting within the R* ellipse will move out from the center until they reach the boundary of the R* ellipse. Particles starting outside the ellipse will move toward the center until they reach the boundary of the R* ellipse. The weights will be in the form of Equation (4.18). For particles within the R* ellipse, the magnitude of the field vector is dominated by: () ()()1 1inin ininr in re Sr e (4.19) Equation (4.19) has two parameters in and in. The function Sin(r) is monotonically decreasing with a limit value of 0. The function in is the value of r such that Sin(r) = 1/2. The function should have a small value at r = R*, so in < R*. Since the area of interest is the behavior of the vector field near R*, set *inin R R where 0 < Rin < R* then Sin(r) can be written as shown in: *(()) (())()1 1 in inrRR in in rRR ine Sr e (4.20) which can also be rewritte n as in Equation (4.13). The parameter in controls the slope of Sin(r) for r in the set R* Rin < r < R* + Rin. Since the desired value of Sin(R*) is very small, let be an arbitrarily small number greater than 0 such

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38that Sin(R*) = The control parameter in can be determined as s hown in Equations (4.21) through 4.26. ** (())11 1 1inin ininR RRRe e (4.21) 1ininRe (4.22) 1ininRe (4.23) 1ininRe (4.24) 1 lninin R (4.25) 11 lnin inR (4.26) 4.3.2.1.2 Mathematical Solution for out-Control Variable In the designed vector field, particles starti ng outside the ellipse will move toward the center until they reach the boundary of the R* ellipse. The weights will be in the form of Equation (4.18). For particles outside the R* ellipse, the magnitude of the field vector is dominated by: () ()() 1 outout outr out re Sr eout (4.27) which can also be rewritten as in Equation (4.14) Equation (4.27) is a monotonically increasing function with a limit value of 1. Picking out > R*, let out = R* + Rout so Sout(r) is:

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39 *(()) (())() 1 outout outoutrRR out rRRe Sr e (4.28) The parameter out controls the slope of Sout(r) for r in the set R* Rout < r < R* + Rout. Since the desired value of Sout(R*) is very small, let be an arbitrarily small number greater than 0 such that Sin(R*) = The control parameter out can then be determined as shown in Equations (4.29) through (4.37). ** **(()) (())1 outout outRRR RRR oute e (4.29) 1 outout outoutR Re e (4.30) outoutoutoutRRee (4.31) (1) outoutRe (4.32) 1outoutRe (4.33) ln 1 outoutR (4.34) 1 ln 1out outR (4.35) 1 (ln1ln)out outR (4.36)

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40 11 lnout outR (4.37) 4.3.2.1.3 Mathematical Proof for Convergence of Sin and Sout Limiting Functions Recall Equation (4.14), so W(r) can be written as: *(()) ((*)) (())1 () 1 1 inin inin outoutrRR rRR rRRe Wr e e (4.38) Since Sin(R*) = and Sout(R*) = then W(R*) = 0. Further it can be shown that for r < R*, W(r) > 0 and that for r > R*, W(r) < 0. *** *(())(())(()) (()) (())11 () 11 ininoutoutinin out ininrRRrRRrRR rRR rRR outeee Wr ee (4.39) The denominator is always positive so the sign is controlled by the numerator. Simplifying the numerator results in: ***(())(())(())11ininoutoutininrRRrRRrRReee (4.40) ****(())(())(())(())1ininininoutoutininrRRrRRrRRrRReeee (4.41) *(())(())1ininoutoutrRRrRRee (4.42) **(())(()1ininoutoutrRRrRRe (4.43) Since e0 = 1, the simplified numerator (and W(R)) will be positive if the argument in the exponent is positive. Similarly W(R) will be negative if the argument in the exponent is negative.

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41Substituting from Equation (4.26) and Equation (4.37) into the expression of the exponent: **(())(()ininoutoutrRRrRR (4.44) **11 ln(())ln(() (1)1 inout inoutrRRrRR RR (4.45) *11 ln(()()) (1) inout inoutinoutRR rR RRRR (4.46) *11 ln()() (1) inoutrR RR (4.47) The expression in Equation (4.47) is positive for r > R* and negative for r < R*. Figure 4.10 and Figure 4.11 show symmetric ( Rin = Rout) and asymmetric ( Rout > Rin) cases respectively. Figure 4.10. Weighting function W(r) (shown in green) when Rin = Rou t

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42 Figure 4.11. Weighting function W(r) (shown in green) when Rou t > Rin. 4.3.2.2 N Normal Limiting Function Attracting the robot to the R* neighborhood specified in Equation (4.11) is the first step in the construction of the final vector field. Another vector field is needed to control the robots once they are in the elliptical band. In this field, the robots need to move along the ellipse in a field perpendicular to the previously described gradient fields. The influence of these perpendicular fields must be restricted to a narrow band, simila r to that described by Equation (4.11). Vectors in this field must die off outside this narrow band. A limiting function accomplishes this is given by: *2() *(,,)rRNrRe (4.48) Figure 4.12 graphs the N limiting function.

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43 Figure 4.12. The weighting function N as a function of the weighted distance r defined in (4.17). In addition, another multiplier to the perpendicu lar field must be added so the robots do not circle around the elliptical bands as in Figure 4.6. In order for the perpendicular field to change directions, the field perpendicular to the grad ient is multiplied by a function which changes the direction of the perpendicular field about the x-axis: ()1 (,)12.0 1rotrot ySGNy e (4.49) Function N in Equation (4.48) includes one tuning parameter, The parameter controls the slope of N (r) for r in the set R – Rin < r < R + Rout. In this case, the parameter will be defined so that the value of N (R*+ Rout) and N (R*– Rin) can be made arbitrarily small. The resulting formula for is shown in Equation (4.52). The sa me technique is used in the other limiting functions. For the symmetric case ( Rin = Rout), solving for :

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44 2(*)*outRRRe (4.50) 2ln()outR (4.51) 21 ln ()outR (4.52) The vector field is depicted in Figure 4.13 is th e sum of the three vector fields discussed in this section. Functions Sin, Sout and N impose additional restrictions and constraints on top of and in addition to the initial swarm function f(x, y) These limiting functions provide a much tighter level of control by limiting and restricting where the vector fields begin and end. The limiting functions, along with vector fields created by the bivariate normal function, may be summed to create swarm movement in formation as a group. When combined, these equations form the velocity and direction of the swarm movement with respect to the center of the swarm, as shown in: ()*xxx inout yyyvdd SSSGNN vdd (4.53)

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45 Figure 4.13. Vector field with Sin, Sout and N limiting functions. 4.3.3 Controlling Swarm Member Dispersion within Bands Vector fields weighted with sigmoid functions may be used for obstacle avoidance as well as controlling member spacing by creating vectors m oving away from the center of the obstacle’s or other swarm member’s location ( xco, yco). For the purposes of this work, the concern is formation including member spacing, so for the purposes of describing the formation control methodology, it is assumed that the only obstacles are other memb ers of the swarm. The same form of limiting function as Sin may be used. Obstacle avoidance between members is accomplished using Equations (4.54) to (4.56): 22()()avoidcocorxxyy (4.54)

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46 ()1 (,,)1 1avoidavoidavoidavoidavoidavoidavoid rRSrR e (4.55) _() ()xavoid avoidco avoidco yavoidd Sxx Syy d (4.56) Notice that ravoid is similar to r from Equation (4.17) except that instead of distance from the center, the distance to the sw arm member is used. The Ravoid parameter determines the distance from other members. This parameter determines the dispersion of swarm members in formation. The Sout and Sin get the robots to the band, but do not control their dispersion. Avoidance of individual robot swarm members including their dispersion is controlled by the range of influence for the avoidance vector field. The avoid parameter in Equation (4.24) controls how quickly vector fields die out near obstacles. As avoid decreases, the influence range of the avoidance vector field increases. By controlling the avoid parameter, different types of formations can be made within the ellip se bands. Selection and tuning of the Ravoid parameter is discussed in Section 4.4.1. The avoid parameter is solved for in the same way as the other sigmoid limiting functions in Equations (4.13) and (4.14). The Ravoid parameter specifies the minimum distance between robots. Solving for Savoid( Ravoid)= gives: 11 lnavoid avoidR (4.57) The Savoid function can be used to prevent swarm members from colliding with each other. A combination of all of the above fields creates a static formation for the robots; shifting the center of the ellipse as a function of time, creates an overall movement of a group of swarm members as a whole. The swarm may move from one waypoint to a nother by moving the center of the ellipse (xc, yc) The general equation to create the vector to follow a trajectory with member avoidance by summing the vector fields is given by:

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47#1 _ 1(-)*size xavoid x xx inoutavoid yyy yavoidd vdd SSSSGNN vdd d (4.58) The computational requirements for an i ndividual swarm member are very low, O(n). The computational complexity of the vector genera tion depends on the number of obstacles because this is the only factor in the equations which has potential to be continuously growing. The complexity will grow in denser environments as well as when the size of the swarm increases because more avoidance vectors must be calculated. This complexity is due to the fact that at each time step, an avoidance vector for n obstacles and/ or robots within a certain range must be generated. It is important to note that swar m members do not compute the entire field. They compute a single vector from that field which depends on the center of the ellipse, ( xc,yc), and the four vectors (in, out, perpendicular, and avoidance) and their corresponding weights. 4.4 Parameter Selection 4.4.1 Logical and Static Parameter Selection for R -Parameters In order to select control parameters, some l ogic and basic mathematics must be used. The formation must be feasible given the swarm char acteristics. These swarm characteristics might include the number of team members; the desire d length of the minor and major axes; and the average or maximum length of the robot s. The precision at which these R -values are chosen will determine how tight and accurate the formation w ill be. The first step necessary is determining which formation is desired. It is important to note that there is some allowable margin of error when selecting parameters because the swarm me mbers can lie in the area described by Equation (4.11). Parameter selection guidelines for differe nt formations are discussed in the following sections. 4.4.1.1 Ellipse Formation If the desired formation is an ellipse, the v ectors are generated as described in Equation (4.58). The only necessary requirement is that the chosen major and minor axis fit the swarm characteristics. In order to create a circle formation, an equal minor and major axis must be

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48chosen. In addition, with ellip se and circle formations the Ravoid parameter must allow for equal dispersion along the ellipsoid perimeter to actuall y create an ellipse or circle figure with the swarm. If not, then the robots will tend towards th e front or back of the formation shape. A general estimate of the perime ter of the ellipse can be u tilized as guideline for choosing Ravoid. Given R*, and denoting the number swarm members, the ellipse perimeter can be used as the upper bound in estimating the Ravoid parameter. Ravoid should adhere to the following at the very least to achieve equal dispersion: *2*2*2*22()()/2/avoidRRRRR (4.59) In addition, it is also necessary to make sure that Ravoid is chosen large enough to avoid the other swarm members. This factor is highly depe ndent on the obstacle avoidance sensor used. 4.4.1.2 Arc Formation If the desired formation is an arc or wedge, the formation is as describe d in Equation (4.27). The parameters are chosen in th e same way as the ellipse formation but in order to force the swarm members to the front of the form ation it is necessary to choose R* and Ravoid so approximately half of th e perimeter is empty. 4.4.1.3 Line Formation 4.4.1.3.1 Line Formation with Skinny Ellipse The line formation still uses the ellipse as the basis but with a slight modification. The Sin and N limiting functions are removed in order to trap the robots inside a narrow or skinny ellipse as shown in Figure 4.14 and described by: #1 _ 1(-)size x avoid xx outavoid yy yavoidd vd SS vd d (4.60)

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49The length or major axis of the ellipse needs to be long enough to hold all the swarm members, and the width or minor axis needs to be wide enough for the swarm member. If the parameter is chosen to small, the swarm memb ers will have a zigzag pattern as they continually overshoot the desired path. If the parameter is chosen to large, the swarm members will have an offset line pattern. Figure 4.14. Skinny ellipse with swarm members trapped inside. 4.4.1.3.2 Leader-Follower Line Formation Tighter line formation can be achieved by comb ining a similar method as in Section 4.4.3.1 with a hierarchical leader-follower approach. In this approach, each robot takes the roll of a leader with the exception of the robot with the ta il position in the line. The first robot or highest leader robot is in control of where the swarm travels. Robot 1 follows the same approach discussed for trajectory following in the other formation approaches. Robots 2 to n simply follow their leaders as depicted in Figure 4.15 by tracing their paths. Robot n-1 Robot n followsfollows …. Robot 2 Robot 1 Figure 4.15. Leader-follower line formation approach. 4.4.2 Fuzzy Speed Control and Parameter Selection Since the swarm member s follow the desired behavior to the bands of the ellipse, it is necessary to consider factors such as speed as well as member dispersion. Avoidance of individual robot swarm members and obstacles is accomplished in two ways: i) by controlling the speed at which robots move away when approach ing an avoidance vector field, and, ii) by controlling the range the vector fiel d is allowed to exist utilizing the Ravoid parameter. The

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50first method discussed in Section 4.4.2.1 can be achieved via a fuzzy speed controller based on relevant parameters. The sec ond method discussed in Section 4.4.2.2 can be achieved using a fuzzy method to make the static parameter se lection dynamic. This fuzzy method makes the Ravoid parameter ‘tunable’. It is important to note that the swarming methodology discussed above still works without these fuzzy methods, but tuning the parameters with a fuzzy method can result in more optimal swarm behavior over time. 4.4.2.1 Fuzzy Speed Control In addition to controlling the swarm formation, a desirable feature is speed control based upon distance from elliptical bands as well as distance from other robot members. In order control the speed of the members efficiently, it is only necessary to modify the magnitude of the vectors with a speed parameter which will be denoted by Sspeed. For this purpose, a Mamdani type fuzzy logic speed controller with two inputs was developed as shown in Figure 4.16. Figure 4.16. Fuzzy speed controller. Figure 4.16 is the fuzzy speed controller with two inputs: distance from center, dCenter and distance to nearest obstacle (including other members), dObst Each swarm member will have an identical but independent speed controller. The output of the speed controller will be a multiplier, the Sspeed variable, for the final vx and vy as shown by: # out 1(-S)Sobstacles xavoid xxx speedinavoid yyy yavoidd vdd SSN vdd d (4.61)

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51The first input, dCenter will be controlled based upon the defined rings of the elliptical bands, R*, Rin, and Rout. A generalized description of this input follows in Figure 4.17. The second input, desired distance from obstacles is a us er-defined characteristic. The user defines a reasonable range from Rshort to Rlong of acceptable distances from obstacles and/or other swarm members. Figure 4.18 describes this input. The values for a b c d and e are logically and arbitrarily chosen. Figure 4.17. Distance from center ( dCenter ) input. Figure 4.18. Distance from obstacles ( dObst ) input. In order to describe the control speed cont roller, the possible speeds are normalized to between 0 and 100 units. Figure 4.19 shows the fu zzy speed output. Figure 4.20 shows the fuzzy rules taken directly from Matlab. Figure 4. 21 depicts the surface of the fuzzy function.

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52 Figure 4.19. Fuzzy speed output. Figure 4.20. Fuzzy rules for speed controller. Figure 4.21. Surface function for speed controller.

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534.4.2.2 Fuzzy Parameter Selection of Ravoid In order to equally disperse th e robots along the swarm surface, the Ravoid parameter can be controlled and optimized via a fuzzy functi on. Although it is the same idea as the Sin limiting function, the Ravoid parameter needs to be controlled via fuzzy function to get the desired spacing. This parameter needs to be dynamica lly changing until the robots are at the desired space apart. The user can make a good estimate as to what this parameter should be a priori, but the vector field use in this work is a highly non-linear sum of four dynamically changing vectors so the behavior is not always what is expected. To alleviate this error, a simple fuzzy controller is designed. It is possible to tune the Ravoid parameter within reason and still hold to the desired formation. Only one input is needed for this fuzzy contro ller, and that is the distance to the nearest neighbor. In order to determine the membership values a user defined desired spacing must be given to the fuzzy controller. Since the desire d formation comes in some sort of ellipse, one possible method of selection is via the perimeter of the ellipse and number of robots as described before in Equation (4.59). A similar input to the dObst input is used except now instead of controlling the speed near the robots; only the disp ersion between the robots is to be controlled. This new variable is called dMembers. Figure 4.18 is a generalization of the dMembers input. The user again is supposed to give a reasonable range from Rbegin to Rend of acceptable distances from each robot. Figure 4.22. Distance to nearest neighbor ( dMembers ).

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54Figure 4.23 shows the fuzzy output function for the Ravoid parameter. Figure 4.24 shows the fuzzy rules taken directly from Matlab followed by the fuzzy surface depicted in Figure 4.25. Figure 4.23. Fuzzy output for the Ravoid parameter. Figure 4.24. Fuzzy rules for the Ravoid parameter selection. Figure 4.25. Surface plot for the Ravoid parameter.

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55 Chapter 5 Simulation Software In order to test the swarming theory and algorithms before trying them on the actual hardware, rigorous simulations are performed. These simulations allowed for the discovery of bugs in software, algorithms, and the mathematical theory. The robot swarm was simulated in Matlab version 7.4 utilizing the Simulink toolbox. In order to demonstrate the fuzzy speed control and parameter selection methods, the Fuzzy Logic toolbox was also utilized. 5.1 Matlab Simulink Model Simulink was used to model the robot swarm. Simulink is a toolbox developed by Mathworks for modeling, simulating, and analyzing multi-domain dynamic systems. Both actual robot models as well as models of par ticles are used as vehicles in the swarm model. Ten different robot models are used in combination with vect or generation modules to simulate the overall swarm behavior. The vehicle models are discussed in Section 5.1.2.2. Particles are also used to get more precise simulations whic h are discussed in Section 5.1.2.1. Four and ten particle/robot simulations are run for demonstration of the con cept. The swarm formation controller, which is identical for each robot/particle, is programmed in C. Each individual robot’s vector generating controller is implemented as a ME X S-function with the different control parameters fed in as well as a position vector with nearby member locations. MEX functions have a return type void and work as an interpreter be tween MATLAB functions and C code. The vector controller will be described in Section 5.1.1. Th e overall Simulink model of the swarm system is shown in Figure 5.1.

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56 Figure 5.1. Matlab Simulink swarm simulation with n robots / particles.

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575.1.1 Vector Generator Block Each of the n swarm members has an identical block of C-code for vector generation. Each of the C-MEX S-functions generate s the desired vector presented in Chapter 4 at each time step which is then fed into the vehicle model block. The formation parameters can either be set inside of C-MEX S-function or outside in an m-file These parameters can also dynamically change if necessary. Formation changes are easily made by de termining which fields will be included in the final vector summation equation and updating just a few tuning parameters. Parameters are selected based on the desired formation and disp ersion using methods described in Chapter 4. The center of the swarm, (xc, yc) is then broadcast into the vector generating S-functions at the appropriate time steps. In addition, nearby obstacles and swarm member locations are also broadcast in order to compute the avoidance v ector. How much information is transferred depends on what type of model is used. If it is assumed that all member locations are known, then global knowledge is broadcast. If it is assumed that only obstacles / swarm members at a reasonable ‘line of sight’ is known, then this is the only information known to each swarm member. It is important to note, that knowledge of th e other member locations are not a necessity except for the dispersion aspect of the swarm. The way this knowledge is shared could be done in numerous ways making a case for both centralized and decentralized robotic systems, but this is not the relevant point of this work. The r obots, assuming identical formation parameters, will hold to the bands of the ellipse rega rdless of knowledge of each other. 5.1.2 Vehicle Model Block 5.1.2.1 Particle Model Models of particles are used to simulate the robot swarm vehicles. The particles follow the vectors perfectly whereas robots are limited in thei r motion. The vectors are integrated to obtain position based only on vectors as shown in (5.1) and (5.2). 0() t x X tddt (5.1)

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580() t yYtddt (5.2) This allows for verification of the math theory with excellent accuracy because the particles will follow the generated vector fields exactly. 5.1.2.2 Robot Model A physical robot has much more limited movement than a particle; therefore, a robot model may be implemented with the vector field genera tor to validate the applicability of the proposed approach for swarm control. A working model of an RC-car with Ackerman steering has been derived to demonstrate simple controller design (see Fig. 5.2) used in conjunction with the vector fields. Model simplifications relate to neglecting both the losses in the drive train and motor, as well as slippage of the wheels in the kinematics model. MOTOR FORWARD DYNAMICSerror 1/s axs KINEMATICS vx RobotTm VELOCITY CONTROLLER Vact -+ Y Xerror HEADING CONTROLLER -+ VECTOR FIELD TO VELOCITY & HEADING SET POINTSVELOCITY SETPOINT HEADING SETPOINT VXVy Figure 5.2. Block diagram of car model with feedback. 5.1.2.2.1 Forward Body Reference Dynamics The primary force on the car is the forward motion due to the torque generated by the motor. Other forces acting on the car such as ground resi stance, wind or uneven gr ound, have not been included in the model. These forces can be overc ome by a well designed velocity controller and, therefore, may be neglected in the context of th is study. The equation used to calculate the velocity in the forward direction of the robot is given by Equation (5.3). This represents the mass on wheels portion of the model. The force acting on the vehicle is the torque, Tm(t) produced by

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59the motor increased by the gear ratio between the motor, Nmw, the wheels and decreased by the radius of the wheels, rw, and the mass of the vehicle which is the weight of the vehicle, W divided by the force of gravity, 32.2 ft s-2 0() () (/32.2)t mwm xrobot wNTt vtdt Wr (5.3) Unlike simulated models, robots with the exact same design will not behave identically. In order to reflect this in simulation, some of the robot’s constants relating to the physical characteristics were changed between robots. These are given in Table 5.1. Notice that the some of the characteristics have been changed to a large enough degree that the swarm could be considered a heterogeneous swarm. Table 5.1. Robot physical parameters. Robot Nmw r (m) W (kg) Vsp limit (m/s) dw (m) 1 10 0.01524 1.814 6.096 0.1524 2 15 0.02134 3.629 6.096 0.1829 3 18 0.0274 4.536 6.096 0.2134 4 20 0.0305 6.350 6.096 0.2438 5 23 0.0366 7.257 6.096 0.2743 6 26 0.0457 7.711 6.096 0.3048 7 29 0.0549 9.072 6.096 0.3353 8 32 0.0610 11.340 6.096 0.3658 9 35 0.0671 13.608 6.096 0.3962 10 40 0.0762 14.969 6.096 0.4267 5.1.2.2.2 Motor Model Equations (5.4), (5.5) and (5.6) are used to m odel the electric motor of the RC-car. The input variable (control variable) is the motor voltage. The output of the motor model is the torque applied to the drive train of the RC-car model. The constants related to motor specifications are listed in Table 5.2.

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60Table 5.2. Motor parameters. Symbol Description Value R Electrical resistance 0.26 mW L Electrical inductance 0.1 uH Kt Motor torque constant 0.000162 Nm/s B Motor dampening ratio 0.0005 Nms Kv Motor voltage constant 11.67 radian/Volt-s J Motor inertia 0.002 kg-m2/s2 The motor variables are the current i(t) the angular velocity wm(t) the input voltage Vin(t) and the output torque Tm(t). The output of the motor is converted to lbs-ft to match the units in the summation of forces in the x -direction. () () 1 ()() in m vVt dit R itwt dtLKLL (5.4) () ()() mt mdwtK b itwt dtJJ (5.5) ()() mtTtKit (5.6) 5.1.2.2.3 Kinematic Calculations Kinematic equations have been used to convert the motion along the x -body axis to car’s position in the world reference frame. Equations (5 .7), (5.8) and (5.9) give the kinematics, where vx_robot(t) is equal to the velocity in the body reference frame, dw is the distance between the center of the front and back wheels, value given in Table 5.1, s(t) is the steering angle of the front tires, and (t) is the heading in the world reference frame. In addition, the steering angle of the car has been limited to +/30 degrees due to the physical limitations of Ackerman steering:

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61_ 0()()cos(())cos(())t xrobots X tvtttdt (5.7) 0()()cos(())sin(())t xrobotsYtvtttdt (5.8) 0() ()sin(())t xrobot s wvt ttdt d (5.9) Control has been implemented by first converting the x and y vector inputs to velocity and heading set points, then implementing feedback wi th proportional controllers to maintain the set points. 22()()()spxyvtvtvt (5.10) Equation (5.10) is used to calculate the veloc ity set point. For simplicity of design, a scale factor has been set along with a limit given in Ta ble 5.2. The primary objective of calculating the velocity set point from the generated vectors is to slow the movement of the car as the length of the generated vectors decrease. As the robots approach the way point, vx(t) and vy(t) approach zero reducing the velocity set point. The scale fact or allows for further control over the velocity of the car without altering the vector field genera tion. For the simulations shown in this study, = 10. This parameter increased the velocity of th e robot by a factor of ten without the necessity of recalculating the vector field tuning parameters. The heading set point is controlled by calculating the angle between the x and y velocity vectors, vx(t) and vy(t) generated by the bivariate and normal functions shown in Equation (5.11): 1()tan(()/(())spyxtvtvt (5.11) It is important to note that, the mathematical valu e for the inverse tangent of infinity is ninety. Some programming languages will not allow this cond ition. If this is the case, then additional code is required to prevent division by zero.

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62The velocity control has been implemented with a proportional controller in the body reference frame. This is gi ven in Equation (5.12), where KP is the controller constant, Vx(t) is the velocity in the body reference frame and vx_robot(t) is the velocity set point calculated from the generated vector fields: _()()() actPxrobotxVtKvtVt (5.12) The steering angle, s(t) is set to the difference between the vehicles desired heading, sp(t) and the actual heading of the vehicle, (t) in the world coordinate frame: ()()() sspttt (5.13)

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63 Chapter 6 Hardware Architecture and Platform The hardware platform for the swarm of r obots is four custom-built (in-house) RC-cars equipped with a custom computer control system equipped with GPS and IMU sensors, stereo vision and encoders. The overall hardware system a nd interconnections is depicted in Figure 6.1. In addition a radio controlled helicopter is utili zed in several experiments to demonstrate UAVUGV coordination. These radio-controlled vehicles will be discussed in 6.1. The sensors used for the swarm experiments will be described in Sect ion 6.2. In Section 6.3 the computer system will be described. Figure 6.1. Overall hardware system for UGVs.

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646.1 Radio-Controlled Vehicles 6.1.1 Radio-Controlled Ground Vehicles The RC-cars chosen for the swarm vehicles are TRAXXAS E-Maxx Cars. The Emaxx vehicles are Ackerman steered. Figure 6.2 shows the fully upgraded USL unmanned ground vehicles. The vehicles were painted different co lors in order to run vision experiments from an aerial camera. These vehicles are also equi pped with the USL second generation controller box described in [114, 115] and sensors including a GPS (global positioning system) an IMU(inertial measurement unit). The vehicles are powered by two 7.4V 4200 mAh lithium polymer batteries while the control box and sensors are powered th rough an 11.1V 4200 mAh battery. The vehicle can run anywhere from 45 minutes to 2 hours depending on the level of usage. Each vehicle platform includes upgraded brushless motors a nd an upgraded suspension system capable of handling the weight of the control box, sensor s, cameras, and pan/tilt unit. The vehicle also includes a stereo pair of Sony block cameras. Figure 6.3 shows the vehicles with labeled components. Figure 6.2. Custom-built RC-cars. The RC–car is controlled via Pulse Width Modula tion (PWM) servos. In addition, the swarm vehicles are equipped with the Microbotics Servo/ Switch Controller (SSC). Since it is expected that software and/or hardware systems may fail during the development process, the vehicles are equipped with a safety switch. This is a very important safety feature especially when you are

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65working with several vehicles at a time and need to be able to control them all with a single switch. The hardware component allows the gr ound vehicles to be taken out of autonomous operation any time for any reason. When the switch is reset, control is transferred back to the user. The radio control receiver transmits c ontrol signals from the operator to the SSC and control whether a vehicle is in autonomous or manual operation. IMU IMU Figure 6.3. RC-car components. 6.1.2 Radio-Controlled Helicopter In order to demonstrate UAVUGV swarm coordination an autonomous RC helicopter is also utilized. The Maxi Joker 2 shown in Figure 6. 4 is an electric helicopter capable of lifting approximately 10 pounds of payload and flight s of between 10 and 20 minutes. The Joker is powered by lithium polymer batteries with se parate batteries powering the same second generation controller box and safety switch that is on the ground vehicles. The Joker utilizes a custom set of skids with an incorporated pan tilt unit for a Sony block camera. Sensors include a GPS unit, an IMU, and laser.

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66 Figure 6.4. Maxi Joker 2 helicopter. 6.2 Sensors In order to obtain vehicle state data, partic ularly orientation and position, two sensors are utilized. Orientation or the vehicle heading is collect with an inertial measurement unit. Positional Information is gathered utilizing a global positioning system to get latitude and longitude coordinates. In Figure 5.5a the Mi crostrain 3DMG-X1 IMU is shown, and in Figure 5.5b the Superstar II GPS receiver is shown. The IMU collects data at the rate of 100 Hz and the GPS at 5 Hz. Figure 6.5. Sensors. (a) Microstrain 3DMG-X1 IMU and (b) Superstar II GPS.

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676.3 Computer System The USL generation II control box (Figure 6.6) incorporates features to allow autonomous operation on aerial and ground platforms. The system, weighing 2.5 pounds, is composed of a 2 GHz Pentium mobile chip, mini-ITX motherboard, 2 GB of memory, Superstar II GPS receiver unit, Microbotics safety switch, Intel wireless mini PCI card, and 4 port video capture card. The control box is power by a single 11.1 V, 4200 mAh lithium polymer battery. At full processing power, this battery will power th e control box for approximately 45 minutes. The position sensors can be easily interfaced to the box on any platform by integrating the IMU and attaching the GPS antenna. The G PS and IMU both have a serial interface. The system is booted from a USB memory stic k, which can be removed after the operating system is loaded into memory. This USB stick has a compressed version of Slackware Linux. Figure 6.6. On-board computer processing system.

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68 Chapter 7 Software System Architecture In addition to having a custom built hardware plat form, this robotic system is equipped with a highly customizable and modular software system. Most of the components can also be utilized on the UAV described in Chapter 6. This chap ter will cover the software from the ground up. First, the operating system will be briefly descri bed. Then the software architecture will be described including robotic controllers, sensor modules, the communication module, and finally the swarm formation controllers. 7.1 Operating System The operating system provides the basis for any software architecture and can determine how well software runs or how it is written. For th is reason, a Linux platform was chosen for all vehicles. The particular distribution is Slackware Linux version 10.0 with the 2.6 kernel. The actual setup of the operating system was perform ed on a desktop development system. After development was complete, the software was ported to the USB drive that is in the vehicle. The Linux installation was minimized as much as possible to include only the necessary support for devices and needed software. This is because the operating system is booted into RAM from the USB drive discussed in Chapter 6, and there must be adequate space left for operating processes. In addition, the kernel also had to be recompiled to allow support for the onboard computer’s wireless network card. The software also needs to be specifically configured for communication. An ad-hoc communication scheme was chosen. Mobile ad-h oc networking allows the network nodes or vehicles to exchange information in a wirele ss environment without the need for a fixed infrastructure. An open source package called Mobile Mesh was selected. This software automatically determines the best route to an y node on a dynamic network as shown in Figure 7.1. More information on the operating syst em and installation can be found in [115].

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69 Figure 7.1. Ad-hoc communication network utilizing Mobile Mesh. 7.2 Software Architecture The section describes the structure of all th e software used to interface with the swarm vehicles. All software was written by USF pers onnel. A high level depiction of all software components and their interactions is shown in Fi gure 7.2. All components will be discussed in more detail in the following sections.

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70 Figure 7.2. Overall software system architecture.

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71All of the source code is developed in th e C programming language. The software is designed to be modular so it can be easily ported to other types of operating systems and vehicles. The software modules are easily reused and integr ated. Many of the components have also been used the UAV as well as other UGVs. The software is a set of processes that run concurrently in the background. Information is passed via shared memory structures. Since each process is a single entity, it can be started and stopped as need ed. This modular design is also more failsafe than a single module design. If a single process fails, it does not cause all processes to fail. The actual source code for the unmanned vehi cles is created and compiled on the ground station laptop, a Dell Latitude D820 This laptop has Fedora Core 6 Linux distribution installed. Once the source code is compiled, th e executables are uploaded with the secure copy function ( scp ) to the unmanned vehicles. Figure 7.3 shows a detailed diagram of the source code. Each box denotes a different folder of s ource. Level 1 contains the root folder, the compilation file and all executables. At Level 3, each folder is responsible for a single process. Figure 7.3. Source code directory and file structure.

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727.2.1 Sensor Suite 7.2.1.1 GPS Sensor The GPS process is responsible for reading a nd parsing data from the GPS receiver and placing the data in shared memory. The GPS pr ocess creates a serial connection with the GPS receiver. After the connection is established, the GPS process creates a shared memory location for the GPS data structure. In addition, a corresponding semaphore for controlling mutual exclusion must be created. A semaphore is a pr otected variable which restricts access to shared resources. The shared resource in this case is the GPS data. The GPS process then enters an infinite loop which will continuous ly read, parse, and store the GPS data. The GPS receiver is configured with StarView (a setup tool that came with the GPS unit) to continuously output the NMEA GPGGA message at 5 Hz to the serial port. The GPS process continually updates shared memory. Table 7.1 shows the GPS structure stored in shared memory. The fina l value recorded to shared memory, Count is an integer variable which increments every time it receives a new string of data from the GPS receiver. The counter variable allows all processes accessing th e GPS shared memory structure to determine if the information available is older or newer than the data they already have. Table 7.1. GPS shared memory data structure. Shared Memory Variable Type Latitude double Longitude double Direction Latitude char Direction Longitude char Altitude Float Satellites Integer Lock Integer Count Integer

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73 The GPS process is also responsible for inform ing the operator about the state changes of the GPS positional data. The state of the GPS data is determined by the type of lock acquired. The lock refers to the level of accuracy of the data be ing received. The state of lock can be (1) no lock, (2) lock without WAAS correction, or (3) lo ck with WAAS correction. This notification is simply printed to the screen. 7.2.1.2 IMU Sensor The IMU process is responsible for accessing the IMU sensor. The IMU process creates a serial connection to the IMU. After the connection is established, the IMU process creates a shared memory location for the IMU data struct ure. In addition, a corresponding semaphore for controlling mutual exclusion must be created. Th en, the process then enters and infinite loop which is responsible for gathering stabilized Euler angles, angular rates, a nd accelerations. These values are gathered at the rate of 80 Hz and wr itten to shared memory. Table 7.2 shows the IMU structure stored in shared memory. Table 7.2. IMU shared memory data structure. Shared Memory Variable Type angles [3] roll,pitch,yaw Float accel [3] xyz Float angRate[3] xyz Float Count Integer

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747.2.2 Navigation and Obstacle Avoidance Navigation and obstacle avoidance are achieve d by taking the swarm formation control methodology described in by taking the simula tion source code discussed in Chapter 5 and making slight modifications to make it run on the actual robot. As shown in Figure 7.2, the formation parame ters, sensor data, and communicated data are input into the navigation and obstacle avoidance block. After all the shared memory is created and all necessary connections are made the main navigation loop for formation control runs. Pseudo code for the navigation and obstacle a voidance block is shown in Figure 7.4. Figure 7.4. Pseudo-code for navigation of a single swarm member.

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757.2.2.1 Swarm Formation Controller While the vehicles have not reached the goal, th e navigation loop will c ontinue to run. Each loop iteration checks to see if there ha s been a failure with the function check_dead_robots The failure of a robot in these experiments is signi fied by loss of communication. A failure is simulated by killing a swarm member’s communicati on server. After checking for failure, the weights are computed from the input formation para meters and sensor data is read from shared memory. GPS coordinates are converted from Latitude/Longi tude to Universal Transverse Mercator (UTM) coordinates. UTM is a rectilinear mapping system in map coordinates are represented as Cartesian coordinates and distance is calculated using Euclidian distance measures. Once this conversion has been made, the UTM values are u tilized in the vector field generation. The mathematical equations for the conversion from GPS to UTM can be found in [116]. After the sensor data is read and converted, the swarm center is read, and the vectors are computed. For obstacle avoidance in all experiments it is assumed that the only obstacles are the other swarm members. Each swarm me mber location is utilized to create the obstacle avoidance vector. Global knowledge of other swarm members’ positions is not a necessity but since GPS is the only sensor available for obstacle avoidance, it was necessary for the field experiments. At the end of the loop iteration, the vectors dx and dy are fed into the robot motion controller. This robot motion controller is described next. 7.2.2.2 Robot Motion Controller The robot motion controller block is respons ible for converting the generated vector, dx and dy, from the navigation and obstacle avoidance block and converting it into servo values that will perform the desired motion on the robot. Each robot has different pulse width limits for the servos which define the minimum, maximum, and neutral values for the throttle and turn servos. These values are set in a definitions file and loaded at run-time. The generated vector is converted into a value in the allowable range. Once the throttle and turn servo values are genera ted, a command is sent to the robot servos.

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767.2.3 Communication Server / Client Model In order to exchange data between the sw arm members, a communication protocol is necessary. Mobile Mesh takes care of the routing of the data, but a server and a client still need to be setup to send and receive data. The communication model for the swarm of unmanned vehicles is a simple server / client model utilizing the UDP protocol. The communication module runs in the background like the IMU and GPS processes. Each vehicle runs a single server. The server is a single process which listens, sends, and receives all incoming data from the other swarm members. A si ngle thread is created inside the server which is responsible for sending the othe r swarm members its positional data. Data storage is handled in the same was at it is for the GPS and IMU. Each robot has a shared memory storage location for each of the othe r swarm member. When a data packet is sent, an identification tag denoting which vehicle it came from is attached. When this identification tag is read, the data will be stored in the appropriate shared memory location. In addition to listening, sending, and receiving data, the server also keeps track of how long it has been since it has received data from each swar m member. In the shared memory structure, each swarm member has an integer bit denoting whether that particular member is dead or alive This bit can carry the value 0 or 1. A value of 0 denotes that that particular member is alive and 1 denotes that it is dead If the server has not received data in a specified amount of time, then it marks this value as 1. This bit allows the othe r swarm member to determine if that vehicle has failed, and the swarm can m ove on without this member.

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77 Chapter 8 Simulation Results The proposed method is demonstrated by simula tion using both robot vehicles modeled after an RC-car with Ackerman steering and a simple particle model. The simulation method is described in Chapter 5. Simulations are performed with up to ten robots. Ellipse, circle, line, and arc formations are demonstrated. 8.1 Simulations with Robot Model A set of simulations are run on homogenous and heterogeneous swarms to demonstrate the validity of the approach including the effects of the limiting functions. In addition, the diversity of the swarm demonstrates that the proposed me thod is platform independent. For experiments focusing on a heterogeneous swarm of robots, the ro bot parameters are listed in Chapter 5, Table 5.1. 8.1.1 Ten Heterogeneous Robots Circling a Point Figure 8.1 demonstrates a swarm of ten hete rogeneous robots circling around a stationary point, with the robots starting at points close to the center and moving to achieve the desired trajectory. Table 8.1 summarizes the values used for the control variables. Figure 8.1 demonstrates that all robots eventually follow ed the same circular path, but because of the obstacle avoidance fields, they were at different points of the circular path at the same time instant.

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78 Table 8.1. Control variables with ten robots. Control Variables Circling Center Point Following Trajectory: No Limiting Functions R* 30 30 1 0.5 Rout 0.2 Rin 0.001 Ravoid 5 5 0.001 .001 Figure 8.1. Ten heterogeneous robo ts circling a fixed center point.

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798.1.2 Ten Homogeneous Robots Following a Strai ght Trajectory without Limiting Functions Another experiment relates to implementing control of ten homogeneous robots following a straight trajectory without utilizing the limiting func tions to control the swar m. This is essential to show the importance of the limiting functions in formation control and the necessary change in obstacle avoidance parameters wh en swarm members are closer. The robots start initially at spread distances and approach the center, coming to gether into a cluster with different distances apart depending on the avoid parameter. Figure 8.2 shows the ten robots following a st raight trajectory with time plotted on the z-axis The robots avoid each other and follow the moving bivariate function with center xc(t) and yc(t) given at different time steps. Figure 8.3 shows the emergent flocking behavior that results when the limiting functions are not used. Figure 8.4 show s the robots’ paths with stationary obstacles placed throughout the mission. The robots avoid each other as well as the obstacles throughout navigation. Figure 8.2. Ten robot swarm following a trajectory with time on the z -axis without limiting functions.

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80 Figure 8.3. Ten robot swarm at beginning ( tb), middle ( tm), and end ( tf) of mission without using limiting functions. Figure 8.4. Ten robot swarm following a trajectory avoiding fixed obstacles without limiting functions.

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818.1.3 Ten Heterogeneous Robots in a Line Formation Simulations demonstrating a swarm of ten heter ogeneous robots moving into a line formation are presented. In order to for ce the robots into a line formation must be very small so the surface of the ellipse function from Equation (4.1) is long and skinny. In order to force this formation Equation (4.60) is utilized. The fields th en force the robots to line up to adhere to the function parameters. The parameters for the swar m function as well as the limiting functions are given in Table 8.2. The robots form a line avoiding each other and su ccessfully followed the desired trajectory. All ten robots were slightly different but used the identical vector generation code. The other parameters were fixed. Figure 8.5 demonstrat es the swarm moving into line formation. Table 8.2. Control variables with ten heterogeneous robots. Control Variables Line Formation Ellipse Formation R* 100 30 0.1 0.5 Rout 30 30 Rin 20 20 Ravoid 10 10 0.001 .001

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82 Figure 8.5. Line formation with ten robots at different time steps. (a) t=1, (b) t=25, (c) t=50, and (d) t=100. 8.1.4 Ten Heterogeneous Robots in an Ellipse Formation Another simulation demonstrates control of te n robots following a straight trajectory and a sine trajectory. In this case, <1 in order to force a narrower e llipse configuration along the path. Each robot avoided other robots in the swarm a nd generated an avoidance field at the correct location to avoid a collision between swarm member s. The center of the swarm is a function of time and is incremented as the members move. All ten robots used the same vector generation code and the control variables as in Table 8.2. Figure 8.6 illustrates the swarm following a straight trajectory along the x -axis at different time steps. The robots start in random positions and align themselves onto the band of the ellipse. The robots take longer to get into the elliptical formation versus the line presented in section 8.1.3 but successfully maintain the formation while following a trajectory. Figure 8.7a illustrates the robots making an e lliptical formation while following a sine wave trajectory. The formation is still tight even when the robots are forced to reorient constantly.

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83Figure 8.7b shows the trajectories of each of the robots along the center. The robots exhibit the oscillating pattern because they do not all reach the center at the same time, and they rotate around the center area until the center is incremented. Figure 8.6. Ellipse formation with ten robots at different time steps. (a) t=1, (b) t=50, (c) t=100, and (d) t=200. Figure 8.7. Ellipse formation with sine wave trajectory. (a) robots at different time steps, (b) trajectories.

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848.2 Simulations with Particle Model In order to demonstrate the precision of this method, simulations are run with four particle models. Parameters are selected based on de sired formation and dispersion using methods described in Section 4.4. The center of the sw arm is broadcast into the vector generating Sfunctions. The swarm center, ( xc,yc), is left fixed in order to show the formation shape. 8.2.1 Simulations with Particle Model and Static Variable Selection 8.2.1.1 Four Particles in Arc and Circle Formations Simulations are run with four particles to make an arc and a circle formation. Table 8.3 shows the parameter values used for each formati on. Notice that the only factor to change the formation from an arc to a circle is a modification of the Ravoid parameter. Table 8.3. Control variables with four particles. Control Variables Arc Formation Circle Formation R* 100 100 1 1 Rin 30 30 Rout 20 20 Ravoid 50 75 0.001 0.001 Figure 8.8 shows the paths from initial positions into formation. Figure 8.9 shows the beginning swarm formation to the final swarm form ation in the arc. Figure 8.10 shows the final swarm formation in circle/square.

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85 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 X Y ROBOT 1 ROBOT 2 ROBOT 3 ROBOT 4 Figure 8.8. Particle paths from initial position into arc formation. -1000 -800 -600 -400 -200 0 200 400 600 800 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 XY ROBOTS tb ROBOTS at tm ROBOTS at tf Figure 8.9. Particle arc formation at beginning ( tb), middle ( tm), and end ( tf).

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86 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 XY ROBOTS tb ROBOTS at tm ROBOTS at tf Figure 8.10. Particle circle formation at beginning ( tb), middle (tm), and end ( tf). 8.2.1.2 Ten Particles in Circle and Ellipse Formations Simulations with ten particles in circle and e llipse formation are also run. Table 8.4 shows the parameter values used for each formation. No tice that the only factor to change the formation from a circle to an ellipse is a modification of the parameter to make the y-axis skinnier. Table 8.4. Control variables with ten particles. Control Variables Circle Formation Ellipse Formation R* 200 200 1 0.5 Rin 60 30 Rout 40 20 Ravoid 75 75 0.001 0.001

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87Figure 8.11 shows the particles in circle formation. In addition, the ellipse bands are plotted. The central band is R* The outer band is R*Rin and the inner band is R* + Rout. The particles stay within the acceptable bands and disperse them selves. Figure 8.12 is similar to Figure 8.11 except it is in ellipse formation. The robots ar e on the outer band in order to adhere to the Ravoid parameter which remained the same between th e circle and the ellipse although the area for formation decreased. -400 -300 -200 -100 0 100 200 300 400 -400 -300 -200 -100 0 100 200 300 400 XY Figure 8.11. Particle circle formation. -400 -300 -200 -100 0 100 200 300 400 -400 -300 -200 -100 0 100 200 300 400 XY Figure 8.12. Particle ellipse formation.

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888.2.2 Simulations with Particle Mode l and Fuzzy Variable Selection Simulations are run with particles utilizing the fuzzy parameter selection method discussed in Section 4.4.2 to get an arc or wedge formation. These experiments demonstrate that tuning can help with dispersing the members about the formation function. 8.2.2.1 Four Particles in Arc / Wedge Formation Simulations are run with swarm members to get an arc or wedge formation. Table 8.5 shows the control parameter used for the two experime nts. Figure 8.13 and 8.14 show the plot from initial to final formation for the first and sec ond experiment respectively. The only change between the two experiments is the Rbegin and Rend ranges. In the first experiment the particles stay approximately 71 units apart. In the second experi ment they stay approximately 88 units apart. Table 8.5. Control variables for arc form ation utilizing fuzzy parameter selection. Control Variables Value Experiment 1 Value Experiment 2 R* 50 50 1 1 Rin 70 70 Rout 30 30 Rshort-Rlong 10-20 10-20 Rbegin-Rend 50-70 60-90 0.001 0.001

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89 -1000 -500 0 500 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 XY Figure 8.13. Experiment 1: Particle arc formation at beginning, middle, and end. -1000 -500 0 500 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 XY Figure 8.14. Experiment 2: Particle arc formation at beginning, middle, and end.

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908.2.2.2 Four Particles in Circle / Square Formation Simulations are run with swarm members to obtai n a circle or square formation. Table 8.6 shows the control parameters used for the two e xperiments. Figure 8.15 and 8.16 show the plot from initial to final formation for the first and s econd experiment respectively. The only change between the two experiments is the Rbegin and Rend ranges. In the first experiment the particles stay approximately 97 units apart. In the second experi ment they stay approximately 117 units apart. Table 8.6. Control variables for circle fo rmation utilizing fuzzy parameter selection. Control Variables Value Experiment 1 Value Experiment 2 R* 50 50 1 1 Rin 70 70 Rout 30 30 Rshort-Rlong 10-20 10-20 Rbegin-Rend 70-100 80-120 0.001 0.001

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91 -1000 -500 0 500 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 XY Figure 8.15. Experiment 1: Particle circ le formation at beginning, middle, and end. -1000 -500 0 500 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 XY Figure 8.16. Experiment 2: Particle circ le formation at beginning, middle, and end.

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928.2.2.3 Four Particles in Ellipse / Rectangle Formation Simulations are run with swarm members to get an ellipse or rectangular formation. Table 8.7 shows the control parameters used for the expe riment. Figure 8.17 shows the particle paths to the final formation and Figure 8.18 show the plot from initial to final formation. The particles stay approximately 108 units apart. Table 8.7. Control variables for ellipse fo rmation utilizing fuzzy parameter selection. Control Variables Value R* 100 0.5 Rin 60 Rout 140 Rshort-Rlong 10-20 Rbegin-Rend 80-120 0.001

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93 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 Figure 8.17. Particle paths to ellipse formation. -1000 -800 -600 -400 -200 0 200 400 600 800 1000 -1000 -800 -600 -400 -200 0 200 400 600 800 1000 XY Figure 8.18. Particles at different time steps to ellipse formation.

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94 Chapter 9 Field Experiments Six different sets of field experiments have b een performed. The test field is approximately 70 meters in width and 100 meters in length. Experiments we re performed with three and four UGVs. In addition, an experiment demonstrating UAV-UGV swarm coordination was performed. The hardware described in Chapter 6 was utilized for all experiments. In some experiments a virtual coordinate was used for the swarm center, ( xc,yc). This virtual coordinate is a function of time which all robots receive periodica lly. In the experiments not utilizing a virtual coordinate for the swarm center, another vehicle (either another UGV or a UAV) is defined as the center of the swarm. This vehicle has a traject ory to follow and the other swarm members receive its position periodically. At each time step, the robots compute their vectors based on the current position, the current center, and other swarm me mbers locations. Based on the output of the vector fields, a desired speed and a desired heading are computed. For all experiments, time is in seconds, coordinates are in UTMs, and distance measurements are in meters. 9.1 Experiment 1: Four Robots in an E llipse Formation with a Virtual Center In experiment one, four UGV vehicles traveled in an ellipse formation surrounding a virtual center. The four UGVs travel surrounding each center point and staying at a minimum specified distance away from one another. Table 9.1 s hows the control parameters used for this experiment. The units for R* Rin, Rout, and Ravoid are all in meters. Figure 9.1 shows each swarm member’s distance from the center over time. The jaggedness in these lines is due to GPS update rate. From this plot, the robots travel 3 to 4 meters outside the acceptable range from the center. This travel outside of range from E quation (4.11) could be because the formation parameters were slightly too small for the swarm si ze. Another possibility is that virtual center

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95was moving to quickly for the swarm members to keep up. This error is minor and could be improved with tuning. Table 9.1. Control variables for experiment 1. Control Variables Experiment 1 Parameters R* 7 1 Rin 3 Rout 4 Ravoid 5 0.001 Figure 9.2 shows the swarm formation at the beginning ( tb), middle ( tm), and end ( tf) of mission. The swarm members were started at rando m places at the beginning of the mission and moved into formation over time. The robots c ontinued to hold formation with only slight deviation from tm to tf. It is important to note that the center is guiding the robots and ‘pulls’ and ‘pushes’ them along in formation. Unfortunately on the actual robots the center tends to pull the robots along very well but the pushing ahead (to the front of the formation) does not work as it does in simulation and the robots fall behind the center ( xc,yc). In Figure 9.2, the robots are in formation but they are behind the center throughout the mission. Therefore, the most predictable control of the swarm is obtained when the moveme nt of the center ‘leads’ the movement of the swarm members. Chapter 10 proposes an alternat e approach which alleviat es this inconsistency and sharpens the formations. Figure 9.3 show the robot paths and the centers with respect to time. The robots avoid each other and follow the formation function with center xc and yc given at different time steps. Figure 9.4 demonstrates that the swarm members stay an acceptable distance away from one another throughout the mission. A video demonstrating the first experiment can be found at http://www.csee.usf.edu/USL/Videos/4bot-clip1.wmv.

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96 0 10 20 30 40 50 60 70 0 2 4 6 8 10 12 14 16 18 20 Swarm Member Distance from Center TimeDcenter Robot 1 Robot 2 Robot 3 Robot 4 Figure 9.1. Experiment 1: Robot distance from center of swarm ( xc, yc). 3.6079 3.608 3.6081 3.6082 3.6083 3.6084 3.6085 3.6086 3.6087 3.6088 3.6089 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 EastingNorthingSwarm Following a Straight Trajectory at Different Times Robots tb Robots at tm Robots at tf Center of Swarm Figure 9.2. Experiment 1: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission.

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97 3.6075 3.608 3.6085 3.609 x 105 3.1044 3.1044 3.1044 3.1044 x 106 0 20 40 60 80 Easting ROBOT PATHS WITH TIME ON Z-AXIS Northing Time Robot 1 Robot 2 Robot 3 Robot 4 Center of Swarm Figure 9.3. Experiment 1: Robot paths with respect to center ( xc, yc) with time on z-axis. 0 20 40 60 80 0 5 10 TimeDistanceDistance Robot 1 Robot 2 0 20 40 60 80 0 10 20 TimeDistanceDistance Robot 1 Robot 3 0 20 40 60 80 0 10 20 TimeDistanceDistance Robot 1 Robot 4 0 20 40 60 80 5 10 15 TimeDistanceDistance Robot 2 Robot 3 0 20 40 60 80 0 10 20 TimeDistanceDistance Robot 2 Robot 4 0 20 40 60 80 5 10 15 TimeDistanceDistance Robot 3 Robot 4 Figure 9.4. Experiment 1: Distance between swarm members.

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989.2 Experiment 2: Three Robots in an E llipse Formation with a Virtual Center In experiment two, three UGV vehicles traveled in an ellipse formation surrounding a virtual center. The three UGVs travel surrounding the center point at each time step and staying at a minimum specified distance away from one anothe r. Table 9.2 shows the control parameters used for this experiment. Figure 9.5 shows each swarm member’s distance from the center over time. From this plot, as in experiment 1, the robots travel 3 to 4 meters outside the acceptable range from the center. Table 9.2. Control variables for experiment 2. Control Variables Experiment 2 Parameters R* 10 1 Rin 4 Rout 6 Ravoid 5 0.001 Figure 9.6 shows the swarm formation at the beginning ( tb), middle ( tm), and end ( tf) of mission. The swarm members were started at rando m places at the beginning of the mission and moved into formation over time. The robots con tinued to hold a tight wedge formation with only slight deviation from tm to tf. Again, the center is guiding the robots and ‘pulls’ them along in formation. Figure 9.7 show the robot paths and the centers with respect to time. The robots avoid each other and follow the same formation function with center xc and yc given at different time steps. Figure 9.8 demonstrates that the swarm member s stay an acceptable distance away from one another throughout the mission. A video demonstrating experiment two can be found at http://www.csee.usf.edu/USL/Videos/3bot-clip1.wmv.

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99 0 10 20 30 40 50 60 70 80 90 100 5 10 15 20 25 Swarm Member Distance from Center TimeDcenter Robot 1 Robot 2 Robot 3 Figure 9.5. Experiment 2: Robot distance from center of swarm ( xc, yc). 3.608 3.6081 3.6082 3.6083 3.6084 3.6085 3.6086 3.6087 3.6088 3.6089 3.609 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 EastingNorthingSwarm Following a Straight Trajectory at Different Times Robots tb Robots at tm Robots at tf Center of Swarm Figure 9.6. Experiment 2: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission.

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100 3.608 3.6085 3.609 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 0 20 40 60 80 100 Easting Robot Paths with Time on Z-axis Northing Time Robot 1 Robot 2 Robot 3 Center of Swarm Figure 9.7. Experiment 2: Robot paths with respect to center ( xc, yc) with time on z-axis. 0 10 20 30 40 50 60 70 80 90 100 5 10 15 TimeDistanceDistance Robot 1 Robot 2 0 10 20 30 40 50 60 70 80 90 100 5 10 15 TimeDistanceDistance Robot 1 Robot 3 0 10 20 30 40 50 60 70 80 90 100 0 5 10 TimeDistanceDistance Robot 2 Robot 3 Figure 9.8. Experiment 2: Distance between swarm members.

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1019.3 Experiment 3: Three Robots in an Ellipse Formation with a Robot Center In experiment three, three UGV vehicles travel in an ellipse formation. One of these UGVs, the alpha robot, acts as the formation center ( xc,yc). Two UGVs, the beta robots, travel surrounding the alpha UGV and stay a minimum specified distance away from one another. Table 9.3 shows the control parameters used for this experiment. Figure 9.9 shows each beta swarm member’s distance from the center over time. From this plot, as in experiment 1, the robots travel between 7 and 11 meters from the center. The error is approximately 3 to 4 meters as in experiment 1 and experiment 2. Table 9.3. Control variables for experiment 3. Control Variables Experiment 3 Parameters R* 5 1 Rin 2 Rout 3 Ravoid 5 0.001 Figure 9.10 shows the swarm formation at the beginning ( tb), middle ( tm), and end ( tf) of mission. The swarm members were started at rando m places at the beginning of the mission and moved into formation following the leader robot. The robots continued to hold a tight wedge formation with only slight deviation from tm to tf. The alpha robot is guiding the robots along in formation. Figure 9.11 show the robot paths and the centers with respect to time. The black line is the path of the alpha robot. The robots avoid each other and follow the same formation function with center xc and yc given at different time steps. Figure 9.12 demonstrates that the two beta swarm members stay an acceptable distance away from one another throughout the mission.

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102 0 10 20 30 40 50 60 70 80 4 5 6 7 8 9 10 11 12 TimeDcenterSwarm Member Distance from Center Robot 1 Robot 2 Figure 9.9. Experiment 3: Robot distance from center of swarm ( xc, yc). 3.6082 3.6083 3.6084 3.6085 3.6086 3.6087 3.6088 3.6089 3.609 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 EastingNorthingSwarm Following a Straight Trajectory with Moving Robot Center at Different Times Robots tb Robots at tm Robots at tf Center of Swarm Figure 9.10. Experiment 3: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission.

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103 3.6082 3.6084 3.6086 3.6088 3.609 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 0 20 40 60 80 100 Easting Robot Paths with Time on Z-axis Northing Time Robot 3 Robot 4 Center of Swarm Figure 9.11. Experiment 3: Robot paths with respect to center ( xc, yc) with time on z-axis. 0 10 20 30 40 50 60 70 80 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 TimeDistanceDistance Robot 1 Robot 2 Figure 9.12. Experiment 3: Distance between swarm members.

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1049.4 Experiment 4: Three Robots in a Line Formation In experiment four, three UGV vehicles trav el in a line formation. The leader-follower method discussed in Section 4.4.1.3.2 is used. One of these UGVs, the alpha robot, follows a formation utilizing a virtual formation center ( xc,yc). The alpha robot is at the top of the line hierarchy. The next UGV, beta robot 1, follo ws the alpha UGV staying a minimum specified distance away. The next UGV, beta robot 2, foll ows beta robot 1. Table 9.4 shows the control parameters used for the alpha robot in this e xperiment. Figure 9.13 shows each swarm member’s distance from the other swarm members over tim e. The robots are evenly distributed approximately 10 meters apart in a line formation. Table 9.4. Control variables for experiment 4. Control Variables Experiment 4 Parameters R* 3 1 Rin 1 Rout 1 Ravoid 5 0.001 Figure 9.14 shows the swarm formation at the beginning ( tb), middle ( tm), and end ( tf) of mission. The swarm members were started at rando m places at the beginning of the mission and moved into formation following the alpha robot. The robots continued to hold a tight line formation from tm to tf. Figure 9.15 show the robot paths and the centers w ith respect to time. On the legend, Robot 1 is the path of the alpha robot. Robot 2 is the path of beta robot 1, and Robot 3 is the path of beta robot 2. The dark black line is the center of the swarm which the alpha robot follows.

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105 0 5 10 15 20 25 30 35 40 45 0 10 20 TimeDistanceDistance Robot 1 Robot 2 0 5 10 15 20 25 30 35 40 45 0 20 40 TimeDistanceDistance Robot 1 Robot 3 0 5 10 15 20 25 30 35 40 45 0 10 20 TimeDistanceDistance Robot 2 Robot 3 Figure 9.13. Experiment 4: Distance between swarm members. 3.608 3.6081 3.6082 3.6083 3.6084 3.6085 3.6086 3.6087 3.6088 3.6089 3.609 x 105 3.1043 3.1043 3.1043 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 EastingNorthingSwarm Following a Straight Trajectory at Different Times Robots tb Robots at tm Robots at tf Center of Swarm Figure 9.14. Experiment 4: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission.

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106 3.608 3.6085 3.609 x 105 3.1044 3.1044 3.1044 3.1044 x 106 0 10 20 30 40 50 Easting Robot Paths with Time on Z-axis Northing Robot 1 Robot 2 Robot 3 Center of Swarm Figure 9.15. Experiment 4: Robot paths with respect to center ( xc, yc) with time on z-axis. 9.5 Experiment 5: Three Robots in an Ellipse Formation with a Failure In this experiment, as in experiment three, UGV vehicles travel in an ellipse formation. One of these UGVs, the alpha robot, acts as the formation center ( xc,yc). Two UGVs, the beta robots, travel surrounding the alpha UGV and stay a minimu m specified distance away from one another. A UGV failure is integrated into this experime nt. One of the beta robots will fail during the mission. This UGV does not actually fail, but the communication server dies, and the robot’s navigation function is suspended. When the two other UGVs recognize that the communication link is broken, they dynamically change their pa rameters from those in column one of Table 9.5 to those in column two. This dynamic formation ch ange results in a circle with a smaller radius. The UGVs seamlessly make the transition and continue after the failure.

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107Table 9.5. Control variables for experiment 5. Control Variables Experiment 5 Parameters Before Failure Experiment 5 Parameters After Failure R* 7 4 1 1 Rin 3 1 Rout 4 2 Ravoid 5 5 0.001 0.001 Figure 9.16 shows the swarm formation at the beginning ( tb), middle ( tm), and end ( tf) of mission. The swarm members were started at rando m places at the beginning of the mission and moved into formation following the alpha robot. After tm, one of the beta robots fails. When the other robots realize there has been a failure, they modify their formation and continue to tf. Figure 9.17 show the robot paths over time. The failure can be seen on the graph. Figure 9.18 shows each swarm member’s dist ance from the other swarm members. Robot 2 is the failed robot which can be seen on the plots when the distance from the other robots begins to grow.

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108 3.6082 3.6083 3.6084 3.6085 3.6086 3.6087 3.6088 3.6089 3.609 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 EastingNorthingSwarm Following a Straight Trajectory at Different Times Robots tb Robots at tm Robots at tf Center of Swarm / Robot failed robot Figure 9.16. Experiment 5: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission. 3.6082 3.6083 3.6084 3.6085 3.6086 3.6087 3.6088 3.6089 3.609 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 EastingNorthingSwarm Following a Trajectory in Formation Robot 1 Path Robot 2 Path Robot 3 Path failure occurs Figure 9.17. Experiment 5: Robot paths with respect to center ( xc, yc).

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109 0 50 100 150 0 50 TimeDistanceDistance Robot 1 Robot 2 0 50 100 150 0 10 20 TimeDistanceDistance Robot 1 Robot 3 0 50 100 150 0 20 40 TimeDistanceDistance Robot 2 Robot 3 Figure 9.18. Experiment 5: Distance between swarm members. 9.6 Experiment 6: UAV-UGV Swarm Coordination In experiment six, a helicopter is utilized as the alpha robot. Controllers from [115] are used on the Maxi Joker 2 helicopter. Figure 9.19 shows a diagram of the experiment with the helicopter and the UGVs. Three UGV vehicles travel in an ellipse fo rmation surrounding the helicopter. The helicopter or the alpha robot acts as the formation center ( xc,yc). Three UGVs, the beta robots, travel surrounding the alpha UAV and stay a minimum specified distance away from one another. Table 9.6 shows the control parameters used for this experiment. Figure 9.20 shows each beta swarm member’s distance from the center over time.

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110 Figure 9.19. UAV-UGV swarm coordination. Table 9.6. Control variables for experiment 6. Control Variables Experiment 6 Parameters R* 7 1 Rin 3 Rout 4 Ravoid 5 0.001 Figure 9.21 shows the swarm formation at the beginning ( tb), middle ( tm), and end ( tf) of mission. The swarm members were started at rando m places at the beginning of the mission and moved into formation following the alpha robot. The robots continued to hold an inverted ‘vee’ like formation with only slight deviation from tm to tf. Figure 9.22 show the robot paths and the centers with respect to time. The black line is the path of the alpha robot. The robots avoid each other and follow the same formation function with center xc and yc given at different time steps. Figure 9. 23 demonstrates that the three beta swarm members stay an acceptable distance away from one another throughout the mission. A video demonstrating this e xperiment can be found at http://www.csee.usf.edu/USL/Videos/UAV-UGV-Swarm.wmv.

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111 0 10 20 30 40 50 60 70 80 90 100 5 10 15 20 25 30 35 Swarm Member Distance from Center TimeDcenter Robot 1 Robot 2 Robot 3 Figure 9.20. Experiment 6: Robot distance from center of swarm ( xc, yc). 3.608 3.6081 3.6082 3.6083 3.6084 3.6085 3.6086 3.6087 x 105 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 3.1044 x 106 EastingNorthingSwarm Following a Straight Trajectory at Different Times Robots tb Robots at tm Robots at tf Heli-Center of Swarm Figure 9.21. Experiment 6: Robot formation at beginning ( tb), middle ( tm), and end ( tf) of mission.

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112 3.608 3.6082 3.6084 3.6086 3.6088 x 105 3.1044 3.1044 3.1044 3.1044 x 106 0 20 40 60 80 100 Easting Robot Paths with Time on Z-axis Northing Robot 1 Robot 2 Robot 3 Helicopter Figure 9.22. Experiment 6: Robot paths with respect to center ( xc, yc) with time on z-axis. 0 10 20 30 40 50 60 70 80 90 100 0 10 20 TimeDistanceDistance Robot 1 Robot 2 0 10 20 30 40 50 60 70 80 90 100 0 10 20 TimeDistanceDistance Robot 1 Robot 3 0 10 20 30 40 50 60 70 80 90 100 0 10 20 TimeDistanceDistance Robot 2 Robot 3 Figure 9.23. Experiment 6: Distance between swarm members.

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113 Chapter 10 Future Approach Utilizing Deformable Ellipses 10.1 Technical Approach The objective of this expanded formation contro l methodology is to attract elements of a swarm into a bounded formation and allow the swar m to stay in that formation as it moves around the mission space. In Chapter 4, a vector field is designed to attract swarm members to an ellipse surrounding a convoy of vehicles. By modifying the weights on the vectors in the field, the minimum distance between the swarm member s and the convoy were controlled. The location of the center ( xc, yc), relative to the initial loca tion of the swarm members gives some control over the shape of the final formation. If the center of the ellipse is set below the initial locations of the swarm me mbers, the swarm members even tually form into a shallow wedge formation. By locating the center above the initial location of the swarm members, the same vector field produces a shallow “vee” formati on. All formations derived from the approach in Chapter 4 are subsets of elliptical curves – fo rmation vertices (if they exist) are rounded and the formations could be considered sloppy in s ome applications. In the next section, the formations are sharpened by changing the metric in Equation (4.1). 10.2 Improving Static Formations In order to generalize the formation control st rategy discussed in Chapter 4, Equation (4.1) will be generalized as: (,)(,) N xyzxye (10.1)

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114With 2(,)0(,) NxyforallxyR (10.2) In Chapter 4, a modified Euclidian distance metric as the function N was used. However any function satisfying the conditions in Equation (10.2) could be used. The vector field given in Chapter 4 can be generalized as: 22(,) ,(,)(0,0) xx out xy yy xyvN wxy forallNN vN NN (10.3) with the weight wout(x,y) given by: -((,)-())1 (,) 1 NxyRR outoutwxy e (10.4) The sharpness of the formations can be adjusted by using an alternate metric such as a modified absolute metric given in Equation (10.5): (,),0 ccNxyxxyy (10.5) In Figures 10.1a and 10.1b wedge formations based on two different norms are compared– the modified Euclidean metric (Figure 10.1a) as in Chapter 4 and the modified absolute metric (Figure 10.1b). The formation in Figure 10.1a does not have the crisp lines shown in Figure 10.1b. However, the absolute metric also has some undesirable characteristics. Following the paths of individual swarm members shown as bl ack lines in the figure, several members are attracted to the vertex of the formation. In practice, swarm members use both the formation vector field and an obstacle vector field to control their movements so they avoid each other and spread out along the formation.

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115 Figure 10.1. Examples of wedge formations. (a) the shallow wedge is produced by using a modified Euclidean norm, and (b) the Euclidean norm was replaced with the absolute norm to produce a sharper wedge. Using a large positive power of Equation (4.1) as N(x,y) results in the box-like surface shown as a contour map in Figure 10.2. In this case, swarm members are attracted to well defined, bounded line segments. Swarm members are attr acted to bounded vertical and horizontal line segments. A box distribution is defined by: 22(()())(,),1ccxxyyzxye (10.6) Figure 10.2. A box distribution formation.

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116If it can be assumed that swarm members are initially in some bounded neighborhood, the center of the surface defined by Equation (10.1) can be chosen to attract swarm members into “vee”, wedge, line or column formations. Unfortunat ely, some of these formations can be hard to control as the formation moves through a dynamic environment since the movement of the formation may push some of the individual swa rm members out of alignment. The most predictable control of the swarm is obtained when the movement of the center leads the movement of the swarm members. Examining the vector fields in the figures above, it is clear that swarm members initially located in the lower half of the graph will always stay below the center of the surface so formations based on the lower half of the surface can be lead through a dynamic environment. In Section 10.3, the surface described in Equa tion (10.1) will be transformed so that swarm members starting belo w the center can achieve and maintain any of the 4 formations of interest. 10.3 Bending the Ellipsoid Examining Figure 10.1a, it is easy to see that swarm members initially located in the lower half plane can be attracted into horizontal line or “vee” formations. Intuitiv ely, if the formation surface and the associated vector field could be “bent” along the y-axis, the swarm members would be attracted into additional formations including wedge and column formations. Mathematically, bending the surface involves changing the function N(x,y) from Equation (10.1). Using Equation (4.1) as a model, Let z(X,Y) be an ellipsoid is defined in a reference domainwith variablesXandY with a center ( 0, 0 ) : 22()(,)XYzXYe (10.7) The function z(X,Y) will be transformed it to the desired surface in the physical domain. In this section, it is assumed that the bent ellipse has a center (0, 0) In Section 10.4, translation and rotation about the axis and the center will be shown. Let Y(x) be the “bending” function in the x-y plan e. In this case the bending function is a parabola:

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117222L(x)XrxY (10.8) The bending function could have other forms dependi ng on the formations desired. In a sense, the function Y bends the X-axis of the original ellipse. Suppose that the variable X from Equation (10.7) is defined as: X x (10.9) and 222LYyXrx (10.10) Then Equation (10.7) can be rewritten as: 22222 22(()) ()(,) LxyXrx XYzxyee (10.11) The parameters XL and r control the bend of the ellipse. An example of a bent ellipsoid is given in Figure 10.3a. The surface gradient vector is given by: 2222 2222() 2(,) () x L y Lg xrxyXrx zxy g yXrx (10.12) As before, a weighted vector in the field of the form: ((,),(,)) (,) x out t y xyv g wXxyYxy x vg gg y (10.13) is used to attract swarm members to the surface. Here the weighting function w is defined in the reference domain by Equation (4.13) as a sigmoid f unction that dies off in the interior of a userspecified elliptical contour. In the phys ical domain, a substitution for the variables X and Y must

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118expressed in terms of the physical variable s x and y from Equations (10.9) and (10.10). Figure 10.3b shows a vector field for a bent ellipse. Figure 10.3. Bent ellipse. (a) without vector field, (b) with vector field. Following the arrows in the field, most swarm members, regardless of their initial location, are attracted to the lower edge of the surface and, in this case, into a wedge formation. Also swarm members initially located w ithin the parabolic arc described by Equation (10.8) maintain their initial separation in the x-direction. Figure 10.4 shows 2 addition examples of vector fields based on this surface. By changing the XL and r parameters, the distance between the 2 roots is controlled, L x X r (10.14) In Figure 10.4a, the value of r is small relative to the value of XL, resulting in a relatively flat parabola, so the swarm members will form a column. In Figure 10.4b, r is large relative to XL the roots are very close to each other, so the pa rabola grows rapidly, and the swarm members will form a wedge.

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119 Figure 10.4. Bent ellipse examples. (a ) column formation, (b) wedge formation 10.4 Translating and Rotating the Ellipsoid The goal is to move the swarm in formation along an arbitrary path which will require translation and rotation of the formation. Transl ation along a path is straightforward – the center of the surface described by Equation (4.1) (or Equation (10.1) in the more general case), (xc, yc) can be a function of time. However, to follow a path in formation, the formation must be rotated. The simplest case is considered first by rotati ng the ellipsoid described in Equation (4.1). Consider a reference domain, with variables X and Y in which the ellipsoid is centered at (0, 0) with an axis parallel to the X-axis. The equation of the surface in the X-Y domain is: 22()(), zeXYXY (10.15) A rotation matrix is used to map ( x,y ) from the physical domain into the corresponding point ( X Y ) from the reference domain.

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120 ()cos()sin cossin sincos ()sin()cos ttt tttcctct tt tt cctctxxxxyy yyxxyyX Y (10.16) Here the angle t and the center ( xct,yct) are functions of time. Substituting into Equation (10.14) yields: 22([()cos()sin]([()sin()cos])(,)tt ttttxxyyxxyy cctctczxye (10.17) where z is expressed in terms of the physical vari ables x and y. The gradient of Equation (10.17) can be found directly: 22 22()(cossin()sincos(1) 2(,) ()sincos(1)()(cossintt ttcttctt cttcttxx)yy zxy x xyy) (10.18) However, the gradient can better be expressed in terms of matrix operations that describe the translations and rotations from the reference to the physical domain. Returning to Equation (10.7), the gradient vector, in th e physical domain, for the surface z can be written as: (,)2(,)( zxyzxy(x,y)(x,y))XXYY (10.19) Substituting the values of X and Y from Equation (10.17) gives: cos (,)2(,)[[()cos()sin] sin sin [()sin()cos]] cos tt ttt ctct t t ctct tzxyzxyxxyy xxyy (10.20) An easier way to look at this is to employ the rotation matrix used in Equation (10.17):

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121cossin (,)2(,) sincos tt tt(x,y) zxyzxy (x,y)X Y (10.21) In this case, the gradient vector is computed in the reference domain a nd then rotated into the physical domain. Since the term 2z(x,y) > 0 for all (x,y) and the gradient vector is normalized when the final field vector is computed, this term can be dropped from Equation (10.21). The vector used to control the movement of the rotated swarm is: (,) (,),(,)0 (,) x out yv w(x,y)(x,y) zxyzxy v zxy XY (10.22) In practice, it is easiest to compute the gradient vector and the weighting function in the reference domain, then rotate it into the physical domain. For the case of the bent ellipse, there are two coordinate transformations – one to bend the ellipse and the next to translate and rotate the ellipse into the desired position. Figure 10.5 shows a diagram of the coordinate systems involved. In this case, there are two intermediate coordinate systems. The reference coordinate systems, just as in the previous discussion in Chapter 4, contains an ellipsoid with center (0,0) oriented with 1 axis parallel to the x-axis. The “bent ellipse” coordinate system leaves the center a nd orientation unchanged from the reference, but bends the ellipse into the desired shape. Final ly, the physical coordinate system places the surface into the desired position and orientation. Figure 10.5. Diagram of transformations app lied to vector field for the bent ellipse.

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122Again starting with the reference domain, with variables X and Y the equation for the ellipsoid surface is in Equation (10.14). The variables X a nd Y are the result of the bending operation so they are functions of the bent domain variables x and y as in Equation (10.23). 22()(),()()z()()exyxyxyxyX,Y,X,Y, (10.23) Finally, the variables x and y are the result of a translation and a rotation in the physical domain with variables x and y 22()(),((x,y)(x,y))((x,y)(x,y))z((x,y)(x,y))((x,y)(x,y))eX,Y,X,Y,xyxyxyxy (10.24) To compute the vector field that controls the move ment of the swarm, the chain rule is applied. Ignoring the scalar term, (,) z XY becomes: (,) ((x,y)(x,y)) zxy ((x,y)(x,y)) xy xy X, Y, (10.25) Expanding the derivatives with the chain rule and writing the matrix elements as dot products produces: (,) x xxx yyyyzxy XXYYXXYY XXYYXXYYxxyy xxyyxxyy xxyy (10.26) Again, recognizing the multiplication of 3 matrices gives Equation (10.26) which can further be simplified:

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123(,)xx yyzxy XY X XY Yxx yyxy xy (10.27) Now the definitions of the coordinates are applie d to express Equation (10.27) in terms of the physical coordinate system. First, as in Equati on (4.3) the rotated x and y matrix is defined by: ()cos()sin ()sin()cos tt ttctct ctctxxyy xxyy x y (10.28) so cossin sincosxx tt tt yy xy xy (10.29) As stated previously, X and Y are: 222 LXrx yxX Y (10.30) so 210 21 rxy xyxXX YY (10.31) Substituting in the definition of x in Equation (10.28) gives: 210 2[()cos()sin]1 ttctctrxxyyxy xyXX YY (10.32)

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124Finally, expressing the vector from Equation (10.27) in terms of x and y yields: 2(,) )cos()sin 10 cossin 2[()cos()sin1 sincos (()sin()cos)tt tt ttctct tt ctct tt ctctzxy xxyy rxxyy xxyy (10.33) Multiplying Equation (10.29), (10.32 ) and (10.33) together gives the gradient vector, which can be substituted into Equation (10.22) to get the fi nal field vector. To understand the weighting function, it is easiest to consider this function in the reference domain, in that domain; the weight is a sigmoid that depends on the modified distance to the center of the ellipse. Figure 10.6 illustrates a rotated and bent ellipse. Figure 10.6. A rotated and bent ellipse.

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125 Chapter 11 Conclusions and Future Work 11.1 Conclusions In this work, a methodology for attracting memb ers of a swarm or other multi-agent system into a formation has been presented. Potential functions together with limiting functions can be successfully utilized to control robot swarm form ation, obstacle avoidance and the overall swarm movement. The presented method supports s calability, different swarm sizes, multiple formations, heterogeneous swarm member team s, centralized and decentralized formation control. These formations can move as a unit, adapt to non-uniform surfaces and change dynamically. A mathematical surface pulls th e swarm members around in formation much like a magnet. By adjusting the parameters on the surface, the shape an d extent of the formation can be controlled. Formations can change dynamically by making the surface parameters functions of time. Since our approach is based on potential field methodology, it is scalable to very large swarms and it is relatively tolerant to positional uncertainties for individual swam members. Finally, our approach requires relatively little informati on to be transmitted to the swarm The advantage of this approach in comparison to others is the simplicity of the functions and the ease of formation changes. There is no expe nsive tuning involved and little information is required for each robot to adhere to formation constraints. 11.2 Future Work In the future, the work will be expanded to in clude more formations. In addition, the fuzzy logic tuner for the control variables for the swarm functions will be expanded. The approach will also be expanded to unmanned aerial vehicles (UAVs) by making the formation function a trivariate normal.

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126The ongoing research objective is to deve lop a framework and methodology for swarm formation control by combining control and l earning techniques to model swarms and allow heterogeneous swarms of ground and/or aerial ve hicles to maintain formation while avoiding collisions and handling dynamic changes in the envi ronment. The goal of the design is to easily maintain formation of a group of UGVs/UAVs whic h is not dependent on the size of the swarm or the type of platform and is transferable to multiple formation types. The long term goal is to apply this technique to large numbers of autonomously functioning vehicles which can be used for critical missions such as convoy support. Learning can also be applied in the environment (e.g. terrain changes, potential threats) where the swarm formation can modify based on environmental parameters.

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137 Appendices

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138Appendix A Nomenclature fo r Mathematical Variables Table A.1. Swarm equation variables f (x, y) main swarm function controls distance vector field is aloud to exist control variable in y direction (YLength / XLength) xc, yc swarm surface center in world reference frame XLength (2A) major axis of the swarm surface YLength (2B) minor axis of the swarm surface dx, dy velocity vectors in the x and y directions xrot, yrot x & y coordinates in the rotated reference frame heading between the swarm formation x-axis and the center (xc,yc) R*,Rin ,Rout optimal, inner and outer elliptical rings Rin Rout Distance from the R* band to the inner and outer boundaries of the formation band r Euclidean distance from swarm member to center (xc,yc) xco, yco obstacle center location Sout, Sin,, N limiting functions away from the center, towards the center, and perpendicular to the center respectively SGN multiplier to change direction of perpendicular field about the x-axis out, in, control variables used in respective limiting functions Sout, Sin, and N Ravoid desired distance to maintain from obstacles and/or other swarm members ravoid Euclidean distance from swarm member to obstacle Savoid limiting function to control obstacle avoidance

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139Appendix A (Continued) Table A.1 (Continued) avoid control variables used in Savoid to control the range in which vector field around the obstacle extends dx_avoid,, dy_avoid velocity vectors around obstacles vx, vy combined velocity vectors Sspeed fuzzy output variable to modify magnitude of vx and vy in order to control speed dCenter fuzzy input variable (distance from center) dObst fuzzy input variable (distance to nearest obstacle / swarm member) Rshort minimum distance from obstacles / other swarm members Rlong maximum distance from obstacles / other swarm members dMembers fuzzy input variable (distance to nearest neighbor) Rbegin minimum acceptable distance from each swarm member Rend maximum acceptable distance from each swarm member

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About the Author Laura Barnes received a Bachelors Degree in Co mputer Science from Texas Tech University in 2003 and a M.S. in Computer Science from the University of South Florida in 2007. During her undergraduate program she worked as an inte rn IBM Global Services. During her time in graduate school she worked at the Army Researc h Lab at Aberdeen Proving Ground in Maryland. In addition, she worked as a graduate research assistant for the Unmanned Systems Lab and the Center for Robot-Assisted Search and Rescue. She also received a fellowship from the Oak Ridge Institute for Science and Education (ORISE). While in the Ph.D. program at the University of South Florida, Ms. Barnes has published numerous pieces of work on swarm formation control. Her research interests encompass distributed, intelligent robotic and agent systems.