%{search_type} search results

• chapter 1. Introduction
• chapter 2. Methods of optimal search and screening
• chapter 3. Methods of optimal foraging
• chapter 4. Models of individual search and foraging
• chapter 5. Coalitional search and swarm dynamics
• chapter 6. Remarks on swarm robotic systems for search and foraging
• chapter 7. Conclusion

• Introduction. Methods of Optimal Search and Screening. Methods of Optimal Foraging. Models of Individual Search and Foraging. Coalitional Search and Swarm Dynamics. Remarks on Swarm Robotic Systems for Search and Foraging. Conclusion. Bibliography. Index.
• (source: Nielsen Book Data)

• Introduction Group Testing Search and Screening Games of Search Foraging Goal and Structure of This Book
• Methods of Optimal Search and Screening Location Probabilities and Search Density Search for a Static Target Search for a Moving Target
• Methods of Optimal Foraging Preying and Foraging by Patches Spatial Dynamics of Populations Methods of Optimal Foraging Inferences and Restrictions
• Models of Individual Search and Foraging Movements of the Agents and Their Trajectories Brownian Search and Foraging Foraging by Levy Flights Algorithms of Probabilistic Search and Foraging
• Coalitional Search and Swarm Dynamics Swarming and Collective Foraging Foraging by Multiple Foragers in Random Environment Modeling by Active Brownian Motion Turing System for the Swarm Foraging
• Remarks on Swarm Robotic Systems for Search and Foraging
• Conclusion
• Bibliography
• Index
• A Summary appears at the end of each chapter.
• (source: Nielsen Book Data)

Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that ar.
(source: Nielsen Book Data)
Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers. Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks. The book discusses foraging theory as well as search and screening theory in the same mathematical and algorithmic framework. It presents an overview of the main ideas and methods of foraging and search theories, making the concepts of one theory accessible to specialists of the other. The book covers Brownian walks and Levy flight models of individual foraging and corresponding diffusion models and algorithms of search and foraging in random environments both by single and multiple agents. It also describes the active Brownian motion models for swarm dynamics with corresponding Fokker-Planck equations. Numerical examples and laboratory verifications illustrate the application of both theories.
(source: Nielsen Book Data)

• List of figures xi Preface xv Notation and terms xvii
• 1 Introduction 1 1.1 Motivation and applications 4 1.2 General description of the search problem 5 1.3 Solution approaches in the literature 7 1.4 Methods of local search 11 1.5 Objectives and structure of the book 14 References 15
• 2 Problem of search for static and moving targets 19 2.1 Methods of search and screening 20 2.1.1 General definitions and notation 20 2.1.2 Target location density for a Markovian search 24 2.1.3 The search-planning problem 30 2.2 Group-testing search 55 2.2.1 General definitions and notation 56 2.2.2 Combinatorial group-testing search for static targets 63 2.2.3 Search with unknown number of targets and erroneous observations 71 2.2.4 Basic information theory search with known location probabilities 84 2.3 Path planning and search over graphs 108 2.3.1 General BF* and A* algorithms 109 2.3.2 Real-time search and learning real-time A* algorithm 122 2.3.3 Moving target search and the fringe-retrieving A* algorithm 131 2.4 Summary 140 References 140
• 3 Models of search and decision making 145 3.1 Model of search based on MDP 146 3.1.1 General definitions 146 3.1.2 Search with probabilistic and informational decision rules 152 3.2 Partially observable MDP model and dynamic programming approach 161 3.2.1 MDP with uncertain observations 162 3.2.2 Simple Pollock model of search 166 3.2.3 Ross model with single-point observations 174 3.3 Models of moving target search with constrained paths 179 3.3.1 Eagle model with finite and infinite horizons 180 3.3.2 Branch-and-bound procedure of constrained search with single searcher 184 3.3.3 Constrained path search with multiple searchers 189 3.4 Game theory models of search 192 3.4.1 Game theory model of search and screening 192 3.4.2 Probabilistic pursuit-evasion games 201 3.4.3 Pursuit-evasion games on graphs 206 3.5 Summary 214 References 215
• 4 Methods of information theory search 218 4.1 Entropy and informational distances between partitions 219 4.2 Static target search: Informational LRTA* algorithm 227 4.2.1 Informational LRTA* algorithm and its properties 228 4.2.2 Group-testing search using the ILRTA* algorithm 234 4.2.3 Search by the ILRTA* algorithm with multiple searchers 244 4.3 Moving target search: Informational moving target search algorithm 254 4.3.1 The informational MTS algorithm and its properties 254 4.3.2 Simple search using the IMTS algorithm 260 4.3.3 Dependence of the IMTS algorithm's actions on the target's movement 269 4.4 Remarks on programming of the ILRTA* and IMTS algorithms 270 4.4.1 Data structures 270 4.4.2 Operations and algorithms 282 4.5 Summary 290 References 290
• 5 Applications and perspectives 293 5.1 Creating classification trees by using the recursive ILRTA* algorithm 293 5.1.1 Recursive ILRTA* algorithm 294 5.1.2 Recursive ILRTA* with weighted distances and simulation results 297 5.2 Informational search and screening algorithm with single and multiple searchers 299 5.2.1 Definitions and assumptions 299 5.2.2 Outline of the algorithm and related functions 300 5.2.3 Numerical simulations of search with single and multiple searchers 304 5.3 Application of the ILRTA* algorithm for navigation of mobile robots 305 5.4 Application of the IMTS algorithm for paging in cellular networks 310 5.5 Remark on application of search algorithms for group testing 312 References 313
• 6 Final remarks 316 References 317 Index 319.
• (source: Nielsen Book Data)

This text looks at search algorithms that are applicable in various practical settings. With the implementation of suitable data structures and metrics within the suggested search algorithms, the book presents a unified framework to demonstrate immediate merits and applications. It also looks at applying information theory to search models and algorithms on graphs that can be represented by Markov decision processes and demonstrates that such an approach can lead to the construction of optimal search plans and policies.
(source: Nielsen Book Data)

• List of Contributors xi Preface xiii Acknowledgments xv About the Companion Website xvii Introduction 1 Eugene Kagan, Nir Shvalb, and Irad Ben-Gal I.1 Early History of Robots 1 I.2 Autonomous Robots 2 I.3 Robot Arm Manipulators 6 I.4 Mobile Robots 8 I.5 Multi-Robot Systems and Swarms 12 I.6 Goal and Structure of the Book 16 References 17
• 1 Motion-Planning Schemes in Global Coordinates 21 Oded Medina and Nir Shvalb 1.1 Motivation 21 1.2 Notations 21 1.2.1 The Configuration Space 22 1.2.2 The Workspace 23 1.2.3 The Weight Function 23 1.3 Motion-Planning Schemes: Known Configuration Spaces 25 1.3.1 Potential-Field Algorithms 25 1.3.2 Grid-Based Algorithms 27 1.3.3 Sampling-Based Algorithms 29 1.4 Motion-Planning Schemes: Partially Known Configuration Spaces 30 1.4.1 BUG0 (Reads Bug-Zero) 31 1.4.2 BUG1 32 1.4.3 BUG2 32 1.5 Summary 33 References 33
• 2 Basic Perception 35 Simon Lineykin 2.1 Basic Scheme of Sensors 35 2.2 Obstacle Sensor (Bumper) 36 2.3 The Odometry Sensor 48 2.4 Distance Sensors 52 2.4.1 The ToF Range Finders 52 2.4.2 The Phase Shift Range Finder 56 2.4.3 Triangulation Range Finder 59 2.4.4 Ultrasonic Rangefinder 60 2.5 Summary 63 References 63
• 3 Motion in the Global Coordinates 65 Nir Shvalb and Shlomi Hacohen 3.1 Models of Mobile Robots 65 3.1.1 Wheeled Mobile Robots 65 3.1.2 Aerial Mobile Robots 67 3.2 Kinematic and Control of Hilare-Type Mobile Robots 69 3.2.1 Forward Kinematics of Hilare-Type Mobile Robots 69 3.2.2 Velocity Control of Hilare-Type Mobile Robots 71 3.2.3 Trajectory Tracking 72 3.3 Kinematic and Control of Quadrotor Mobile Robots 74 3.3.1 Dynamics of Quadrotor-Type Mobile Robots 74 3.3.2 Forces and Torques Generated by the Propellers 75 3.3.3 Relative End Global Coordinates 76 3.3.4 The Quadrotor Dynamic Model 78 3.3.5 A Simplified Dynamic Model 79 3.3.6 Trajectory Tracking Control of Quadrotors 80 3.3.7 Simulations 84 References 85
• 4 Motion in Potential Field and Navigation Function 87 Nir Shvalb and Shlomi Hacohen 4.1 Problem Statement 87 4.2 Gradient Descent Method of Optimization 89 4.2.1 Gradient Descent Without Constraints 89 4.2.2 Gradient Descent with Constraints 92 4.3 Minkowski Sum 94 4.4 Potential Field 95 4.5 Navigation Function 99 4.5.1 Navigation Function in Static Deterministic Environment 99 4.5.2 Navigation Function in Static Uncertain Environment 102 4.5.3 Navigation Function and Potential Fields in Dynamic Environment 104 4.5.3.1 Estimation 105 4.5.3.2 Prediction 105 4.5.3.3 Optimization 106 4.6 Summary 106 References 107
• 5 GNSS and Robot Localization 109 Roi Yozevitch and Boaz Ben-Moshe 5.1 Introduction to Satellite Navigation 109 5.1.1 Trilateration 109 5.2 Position Calculation 111 5.2.1 Multipath Signals 111 5.2.2 GNSS Accuracy Analysis 112 5.2.3 DoP 112 5.3 Coordinate Systems 113 5.3.1 Latitude, Longitude, and Altitude 113 5.3.2 UTM Projection 113 5.3.3 Local Cartesian Coordinates 114 5.4 Velocity Calculation 115 5.4.1 Calculation Outlines 115 5.4.2 Implantation Remarks 116 5.5 Urban Navigation 116 5.5.1 Urban Canyon Navigation 117 5.5.2 Map Matching 117 5.5.3 Dead Reckoning - Inertial Sensors 118 5.6 Incorporating GNSS Data with INS 118 5.6.1 Modified Particle Filter 118 5.6.2 Estimating Velocity by Combining GNSS and INS 119 5.7 GNSS Protocols 120 5.8 Other Types of GPS 121 5.8.1 A-GPS 121 5.8.2 DGPS Systems 122 5.8.3 RTK Navigation 122 5.9 GNSS Threats 123 5.9.1 GNSS Jamming 123 5.9.2 GNSS Spoofing 123 References 123
• 6 Motion in Local Coordinates 125 Shraga Shoval 6.1 Global Motion Planning and Navigation 125 6.2 Motion Planning with Uncertainties 128 6.2.1 Uncertainties in Vehicle Performance 128 6.2.1.1 Internal Dynamic Uncertainties 128 6.2.1.2 External Dynamic Uncertainties 129 6.2.2 Sensors Uncertainties 129 6.2.3 Motion-Planning Adaptation to Uncertainties 130 6.3 Online Motion Planning 131 6.3.1 Motion Planning with Differential Constraints 132 6.3.2 Reactive Motion Planning 134 6.4 Global Positioning with Local Maps 135 6.5 UAV Motion Planning in 3D Space 137 6.6 Summary 139 References 140
• 7 Motion in an Unknown Environment 143 Eugene Kagan 7.1 Probabilistic Map-Based Localization 143 7.1.1 Beliefs Distribution and Markov Localization 145 7.1.2 Motion Prediction and Kalman Localization 150 7.2 Mapping the Unknown Environment and Decision-Making 154 7.2.1 Mapping and Localization 155 7.2.2 Decision-Making under Uncertainties 161 7.3 Examples of Probabilistic Motion Planning 169 7.3.1 Motion Planning in Belief Space 169 7.3.2 Mapping of the Environment 176 7.4 Summary 178 References 179
• 8 Energy Limitations and Energetic Efficiency of Mobile Robots 183 Michael Ben Chaim 8.1 Introduction 183 8.2 The Problem of Energy Limitations in Mobile Robots 183 8.3 Review of Selected Literature on Power Management and Energy Control in Mobile Robots 185 8.4 Energetic Model of Mobile Robot 186 8.5 Mobile Robots Propulsion 188 8.5.1 Wheeled Mobile Robots Propulsion 189 8.5.2 Propulsion of Mobile Robots with Caterpillar Drive 190 8.6 Energetic Model of Mechanical Energies Sources 192 8.6.1 Internal Combustion Engines 193 8.6.2 Lithium Electric Batteries 194 8.7 Summary 195 References 195
• 9 Multi-Robot Systems and Swarming 199 Eugene Kagan, Nir Shvalb, Shlomi Hacohen, and Alexander Novoselsky 9.1 Multi-Agent Systems and Swarm Robotics 199 9.1.1 Principles of Multi-Agent Systems 200 9.1.2 Basic Flocking and Methods of Aggregation and Collision Avoidance 208 9.2 Control of the Agents and Positioning of Swarms 218 9.2.1 Agent-Based Models 219 9.2.2 Probabilistic Models of Swarm Dynamics 234 9.3 Summary 236 References 238
• 10 Collective Motion with Shared Environment Map 243 Eugene Kagan and Irad Ben-Gal 10.1 Collective Motion with Shared Information 243 10.1.1 Motion in Common Potential Field 244 10.1.2 Motion in the Terrain with Sharing Information About Local Environment 250 10.2 Swarm Dynamics in a Heterogeneous Environment 253 10.2.1 Basic Flocking in Heterogeneous Environment and External Potential Field 253 10.2.2 Swarm Search with Common Probability Map 259 10.3 Examples of Swarm Dynamics with Shared Environment Map 261 10.3.1 Probabilistic Search with Multiple Searchers 261 10.3.2 Obstacle and Collision Avoidance Using Attraction/Repulsion Potentials 264 10.4 Summary 270 References 270
• 11 Collective Motion with Direct and Indirect Communication 273 Eugene Kagan and Irad Ben-Gal 11.1 Communication Between Mobile Robots in Groups 273 11.2 Simple Communication Protocols and Examples of Collective Behavior 277 11.2.1 Examples of Communication Protocols for the Group of Mobile Robots 278 11.2.1.1 Simple Protocol for Emulating One-to-One Communication in the Lego NXT Robots 278 11.2.1.2 Flocking and Preserving Collective Motion of the Robot's Group 284 11.2.2 Implementation of the Protocols and Examples of Collective Behavior of Mobile Robots 287 11.2.2.1 One-to-One Communication and Centralized Control in the Lego NXT Robots 287 11.2.2.2 Collective Motion of Lego NXT Robots Preserving the Group Activity 291 11.3 Examples of Indirect and Combined Communication 293 11.3.1 Models of Ant Motion and Simulations of Pheromone Robotic System 293 11.3.2 Biosignaling and Destructive Search by the Group of Mobile Agents 297 11.4 Summary 300 References 301
• 12 Brownian Motion and Swarm Dynamics 305 Eugene Khmelnitsky 12.1 Langevin and Fokker-Plank Formalism 305 12.2 Examples 307 12.3 Summary 316 References 316
• 13 Conclusions 317 Nir Shvalb, Eugene Kagan, and Irad Ben-Gal Index 319.
• (source: Nielsen Book Data)

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot's location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge. The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.
(source: Nielsen Book Data)

• List of Contributors xi Preface xiii Acknowledgments xv About the Companion Website xvii Introduction 1 Eugene Kagan, Nir Shvalb, and Irad Ben-Gal I.1 Early History of Robots 1 I.2 Autonomous Robots 2 I.3 Robot Arm Manipulators 6 I.4 Mobile Robots 8 I.5 Multi-Robot Systems and Swarms 12 I.6 Goal and Structure of the Book 16 References 17
• 1 Motion-Planning Schemes in Global Coordinates 21 Oded Medina and Nir Shvalb 1.1 Motivation 21 1.2 Notations 21 1.2.1 The Configuration Space 22 1.2.2 The Workspace 23 1.2.3 The Weight Function 23 1.3 Motion-Planning Schemes: Known Configuration Spaces 25 1.3.1 Potential-Field Algorithms 25 1.3.2 Grid-Based Algorithms 27 1.3.3 Sampling-Based Algorithms 29 1.4 Motion-Planning Schemes: Partially Known Configuration Spaces 30 1.4.1 BUG0 (Reads Bug-Zero) 31 1.4.2 BUG1 32 1.4.3 BUG2 32 1.5 Summary 33 References 33
• 2 Basic Perception 35 Simon Lineykin 2.1 Basic Scheme of Sensors 35 2.2 Obstacle Sensor (Bumper) 36 2.3 The Odometry Sensor 48 2.4 Distance Sensors 52 2.4.1 The ToF Range Finders 52 2.4.2 The Phase Shift Range Finder 56 2.4.3 Triangulation Range Finder 59 2.4.4 Ultrasonic Rangefinder 60 2.5 Summary 63 References 63
• 3 Motion in the Global Coordinates 65 Nir Shvalb and Shlomi Hacohen 3.1 Models of Mobile Robots 65 3.1.1 Wheeled Mobile Robots 65 3.1.2 Aerial Mobile Robots 67 3.2 Kinematic and Control of Hilare-Type Mobile Robots 69 3.2.1 Forward Kinematics of Hilare-Type Mobile Robots 69 3.2.2 Velocity Control of Hilare-Type Mobile Robots 71 3.2.3 Trajectory Tracking 72 3.3 Kinematic and Control of Quadrotor Mobile Robots 74 3.3.1 Dynamics of Quadrotor-Type Mobile Robots 74 3.3.2 Forces and Torques Generated by the Propellers 75 3.3.3 Relative End Global Coordinates 76 3.3.4 The Quadrotor Dynamic Model 78 3.3.5 A Simplified Dynamic Model 79 3.3.6 Trajectory Tracking Control of Quadrotors 80 3.3.7 Simulations 84 References 85
• 4 Motion in Potential Field and Navigation Function 87 Nir Shvalb and Shlomi Hacohen 4.1 Problem Statement 87 4.2 Gradient Descent Method of Optimization 89 4.2.1 Gradient Descent Without Constraints 89 4.2.2 Gradient Descent with Constraints 92 4.3 Minkowski Sum 94 4.4 Potential Field 95 4.5 Navigation Function 99 4.5.1 Navigation Function in Static Deterministic Environment 99 4.5.2 Navigation Function in Static Uncertain Environment 102 4.5.3 Navigation Function and Potential Fields in Dynamic Environment 104 4.5.3.1 Estimation 105 4.5.3.2 Prediction 105 4.5.3.3 Optimization 106 4.6 Summary 106 References 107
• 5 GNSS and Robot Localization 109 Roi Yozevitch and Boaz Ben-Moshe 5.1 Introduction to Satellite Navigation 109 5.1.1 Trilateration 109 5.2 Position Calculation 111 5.2.1 Multipath Signals 111 5.2.2 GNSS Accuracy Analysis 112 5.2.3 DoP 112 5.3 Coordinate Systems 113 5.3.1 Latitude, Longitude, and Altitude 113 5.3.2 UTM Projection 113 5.3.3 Local Cartesian Coordinates 114 5.4 Velocity Calculation 115 5.4.1 Calculation Outlines 115 5.4.2 Implantation Remarks 116 5.5 Urban Navigation 116 5.5.1 Urban Canyon Navigation 117 5.5.2 Map Matching 117 5.5.3 Dead Reckoning - Inertial Sensors 118 5.6 Incorporating GNSS Data with INS 118 5.6.1 Modified Particle Filter 118 5.6.2 Estimating Velocity by Combining GNSS and INS 119 5.7 GNSS Protocols 120 5.8 Other Types of GPS 121 5.8.1 A-GPS 121 5.8.2 DGPS Systems 122 5.8.3 RTK Navigation 122 5.9 GNSS Threats 123 5.9.1 GNSS Jamming 123 5.9.2 GNSS Spoofing 123 References 123
• 6 Motion in Local Coordinates 125 Shraga Shoval 6.1 Global Motion Planning and Navigation 125 6.2 Motion Planning with Uncertainties 128 6.2.1 Uncertainties in Vehicle Performance 128 6.2.1.1 Internal Dynamic Uncertainties 128 6.2.1.2 External Dynamic Uncertainties 129 6.2.2 Sensors Uncertainties 129 6.2.3 Motion-Planning Adaptation to Uncertainties 130 6.3 Online Motion Planning 131 6.3.1 Motion Planning with Differential Constraints 132 6.3.2 Reactive Motion Planning 134 6.4 Global Positioning with Local Maps 135 6.5 UAV Motion Planning in 3D Space 137 6.6 Summary 139 References 140
• 7 Motion in an Unknown Environment 143 Eugene Kagan 7.1 Probabilistic Map-Based Localization 143 7.1.1 Beliefs Distribution and Markov Localization 145 7.1.2 Motion Prediction and Kalman Localization 150 7.2 Mapping the Unknown Environment and Decision-Making 154 7.2.1 Mapping and Localization 155 7.2.2 Decision-Making under Uncertainties 161 7.3 Examples of Probabilistic Motion Planning 169 7.3.1 Motion Planning in Belief Space 169 7.3.2 Mapping of the Environment 176 7.4 Summary 178 References 179
• 8 Energy Limitations and Energetic Efficiency of Mobile Robots 183 Michael Ben Chaim 8.1 Introduction 183 8.2 The Problem of Energy Limitations in Mobile Robots 183 8.3 Review of Selected Literature on Power Management and Energy Control in Mobile Robots 185 8.4 Energetic Model of Mobile Robot 186 8.5 Mobile Robots Propulsion 188 8.5.1 Wheeled Mobile Robots Propulsion 189 8.5.2 Propulsion of Mobile Robots with Caterpillar Drive 190 8.6 Energetic Model of Mechanical Energies Sources 192 8.6.1 Internal Combustion Engines 193 8.6.2 Lithium Electric Batteries 194 8.7 Summary 195 References 195
• 9 Multi-Robot Systems and Swarming 199 Eugene Kagan, Nir Shvalb, Shlomi Hacohen, and Alexander Novoselsky 9.1 Multi-Agent Systems and Swarm Robotics 199 9.1.1 Principles of Multi-Agent Systems 200 9.1.2 Basic Flocking and Methods of Aggregation and Collision Avoidance 208 9.2 Control of the Agents and Positioning of Swarms 218 9.2.1 Agent-Based Models 219 9.2.2 Probabilistic Models of Swarm Dynamics 234 9.3 Summary 236 References 238
• 10 Collective Motion with Shared Environment Map 243 Eugene Kagan and Irad Ben-Gal 10.1 Collective Motion with Shared Information 243 10.1.1 Motion in Common Potential Field 244 10.1.2 Motion in the Terrain with Sharing Information About Local Environment 250 10.2 Swarm Dynamics in a Heterogeneous Environment 253 10.2.1 Basic Flocking in Heterogeneous Environment and External Potential Field 253 10.2.2 Swarm Search with Common Probability Map 259 10.3 Examples of Swarm Dynamics with Shared Environment Map 261 10.3.1 Probabilistic Search with Multiple Searchers 261 10.3.2 Obstacle and Collision Avoidance Using Attraction/Repulsion Potentials 264 10.4 Summary 270 References 270
• 11 Collective Motion with Direct and Indirect Communication 273 Eugene Kagan and Irad Ben-Gal 11.1 Communication Between Mobile Robots in Groups 273 11.2 Simple Communication Protocols and Examples of Collective Behavior 277 11.2.1 Examples of Communication Protocols for the Group of Mobile Robots 278 11.2.1.1 Simple Protocol for Emulating One-to-One Communication in the Lego NXT Robots 278 11.2.1.2 Flocking and Preserving Collective Motion of the Robot's Group 284 11.2.2 Implementation of the Protocols and Examples of Collective Behavior of Mobile Robots 287 11.2.2.1 One-to-One Communication and Centralized Control in the Lego NXT Robots 287 11.2.2.2 Collective Motion of Lego NXT Robots Preserving the Group Activity 291 11.3 Examples of Indirect and Combined Communication 293 11.3.1 Models of Ant Motion and Simulations of Pheromone Robotic System 293 11.3.2 Biosignaling and Destructive Search by the Group of Mobile Agents 297 11.4 Summary 300 References 301
• 12 Brownian Motion and Swarm Dynamics 305 Eugene Khmelnitsky 12.1 Langevin and Fokker-Plank Formalism 305 12.2 Examples 307 12.3 Summary 316 References 316
• 13 Conclusions 317 Nir Shvalb, Eugene Kagan, and Irad Ben-Gal Index 319.
• (source: Nielsen Book Data)

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot's location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge. The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.
(source: Nielsen Book Data)

• List of Contributors xi Preface xiii Acknowledgments xv About the Companion Website xvii Introduction 1 Eugene Kagan, Nir Shvalb, and Irad Ben-Gal I.1 Early History of Robots 1 I.2 Autonomous Robots 2 I.3 Robot Arm Manipulators 6 I.4 Mobile Robots 8 I.5 Multi-Robot Systems and Swarms 12 I.6 Goal and Structure of the Book 16 References 17
• 1 Motion-Planning Schemes in Global Coordinates 21 Oded Medina and Nir Shvalb 1.1 Motivation 21 1.2 Notations 21 1.2.1 The Configuration Space 22 1.2.2 The Workspace 23 1.2.3 The Weight Function 23 1.3 Motion-Planning Schemes: Known Configuration Spaces 25 1.3.1 Potential-Field Algorithms 25 1.3.2 Grid-Based Algorithms 27 1.3.3 Sampling-Based Algorithms 29 1.4 Motion-Planning Schemes: Partially Known Configuration Spaces 30 1.4.1 BUG0 (Reads Bug-Zero) 31 1.4.2 BUG1 32 1.4.3 BUG2 32 1.5 Summary 33 References 33
• 2 Basic Perception 35 Simon Lineykin 2.1 Basic Scheme of Sensors 35 2.2 Obstacle Sensor (Bumper) 36 2.3 The Odometry Sensor 48 2.4 Distance Sensors 52 2.4.1 The ToF Range Finders 52 2.4.2 The Phase Shift Range Finder 56 2.4.3 Triangulation Range Finder 59 2.4.4 Ultrasonic Rangefinder 60 2.5 Summary 63 References 63
• 3 Motion in the Global Coordinates 65 Nir Shvalb and Shlomi Hacohen 3.1 Models of Mobile Robots 65 3.1.1 Wheeled Mobile Robots 65 3.1.2 Aerial Mobile Robots 67 3.2 Kinematic and Control of Hilare-Type Mobile Robots 69 3.2.1 Forward Kinematics of Hilare-Type Mobile Robots 69 3.2.2 Velocity Control of Hilare-Type Mobile Robots 71 3.2.3 Trajectory Tracking 72 3.3 Kinematic and Control of Quadrotor Mobile Robots 74 3.3.1 Dynamics of Quadrotor-Type Mobile Robots 74 3.3.2 Forces and Torques Generated by the Propellers 75 3.3.3 Relative End Global Coordinates 76 3.3.4 The Quadrotor Dynamic Model 78 3.3.5 A Simplified Dynamic Model 79 3.3.6 Trajectory Tracking Control of Quadrotors 80 3.3.7 Simulations 84 References 85
• 4 Motion in Potential Field and Navigation Function 87 Nir Shvalb and Shlomi Hacohen 4.1 Problem Statement 87 4.2 Gradient Descent Method of Optimization 89 4.2.1 Gradient Descent Without Constraints 89 4.2.2 Gradient Descent with Constraints 92 4.3 Minkowski Sum 94 4.4 Potential Field 95 4.5 Navigation Function 99 4.5.1 Navigation Function in Static Deterministic Environment 99 4.5.2 Navigation Function in Static Uncertain Environment 102 4.5.3 Navigation Function and Potential Fields in Dynamic Environment 104 4.5.3.1 Estimation 105 4.5.3.2 Prediction 105 4.5.3.3 Optimization 106 4.6 Summary 106 References 107
• 5 GNSS and Robot Localization 109 Roi Yozevitch and Boaz Ben-Moshe 5.1 Introduction to Satellite Navigation 109 5.1.1 Trilateration 109 5.2 Position Calculation 111 5.2.1 Multipath Signals 111 5.2.2 GNSS Accuracy Analysis 112 5.2.3 DoP 112 5.3 Coordinate Systems 113 5.3.1 Latitude, Longitude, and Altitude 113 5.3.2 UTM Projection 113 5.3.3 Local Cartesian Coordinates 114 5.4 Velocity Calculation 115 5.4.1 Calculation Outlines 115 5.4.2 Implantation Remarks 116 5.5 Urban Navigation 116 5.5.1 Urban Canyon Navigation 117 5.5.2 Map Matching 117 5.5.3 Dead Reckoning - Inertial Sensors 118 5.6 Incorporating GNSS Data with INS 118 5.6.1 Modified Particle Filter 118 5.6.2 Estimating Velocity by Combining GNSS and INS 119 5.7 GNSS Protocols 120 5.8 Other Types of GPS 121 5.8.1 A-GPS 121 5.8.2 DGPS Systems 122 5.8.3 RTK Navigation 122 5.9 GNSS Threats 123 5.9.1 GNSS Jamming 123 5.9.2 GNSS Spoofing 123 References 123
• 6 Motion in Local Coordinates 125 Shraga Shoval 6.1 Global Motion Planning and Navigation 125 6.2 Motion Planning with Uncertainties 128 6.2.1 Uncertainties in Vehicle Performance 128 6.2.1.1 Internal Dynamic Uncertainties 128 6.2.1.2 External Dynamic Uncertainties 129 6.2.2 Sensors Uncertainties 129 6.2.3 Motion-Planning Adaptation to Uncertainties 130 6.3 Online Motion Planning 131 6.3.1 Motion Planning with Differential Constraints 132 6.3.2 Reactive Motion Planning 134 6.4 Global Positioning with Local Maps 135 6.5 UAV Motion Planning in 3D Space 137 6.6 Summary 139 References 140
• 7 Motion in an Unknown Environment 143 Eugene Kagan 7.1 Probabilistic Map-Based Localization 143 7.1.1 Beliefs Distribution and Markov Localization 145 7.1.2 Motion Prediction and Kalman Localization 150 7.2 Mapping the Unknown Environment and Decision-Making 154 7.2.1 Mapping and Localization 155 7.2.2 Decision-Making under Uncertainties 161 7.3 Examples of Probabilistic Motion Planning 169 7.3.1 Motion Planning in Belief Space 169 7.3.2 Mapping of the Environment 176 7.4 Summary 178 References 179
• 8 Energy Limitations and Energetic Efficiency of Mobile Robots 183 Michael Ben Chaim 8.1 Introduction 183 8.2 The Problem of Energy Limitations in Mobile Robots 183 8.3 Review of Selected Literature on Power Management and Energy Control in Mobile Robots 185 8.4 Energetic Model of Mobile Robot 186 8.5 Mobile Robots Propulsion 188 8.5.1 Wheeled Mobile Robots Propulsion 189 8.5.2 Propulsion of Mobile Robots with Caterpillar Drive 190 8.6 Energetic Model of Mechanical Energies Sources 192 8.6.1 Internal Combustion Engines 193 8.6.2 Lithium Electric Batteries 194 8.7 Summary 195 References 195
• 9 Multi-Robot Systems and Swarming 199 Eugene Kagan, Nir Shvalb, Shlomi Hacohen, and Alexander Novoselsky 9.1 Multi-Agent Systems and Swarm Robotics 199 9.1.1 Principles of Multi-Agent Systems 200 9.1.2 Basic Flocking and Methods of Aggregation and Collision Avoidance 208 9.2 Control of the Agents and Positioning of Swarms 218 9.2.1 Agent-Based Models 219 9.2.2 Probabilistic Models of Swarm Dynamics 234 9.3 Summary 236 References 238
• 10 Collective Motion with Shared Environment Map 243 Eugene Kagan and Irad Ben-Gal 10.1 Collective Motion with Shared Information 243 10.1.1 Motion in Common Potential Field 244 10.1.2 Motion in the Terrain with Sharing Information About Local Environment 250 10.2 Swarm Dynamics in a Heterogeneous Environment 253 10.2.1 Basic Flocking in Heterogeneous Environment and External Potential Field 253 10.2.2 Swarm Search with Common Probability Map 259 10.3 Examples of Swarm Dynamics with Shared Environment Map 261 10.3.1 Probabilistic Search with Multiple Searchers 261 10.3.2 Obstacle and Collision Avoidance Using Attraction/Repulsion Potentials 264 10.4 Summary 270 References 270
• 11 Collective Motion with Direct and Indirect Communication 273 Eugene Kagan and Irad Ben-Gal 11.1 Communication Between Mobile Robots in Groups 273 11.2 Simple Communication Protocols and Examples of Collective Behavior 277 11.2.1 Examples of Communication Protocols for the Group of Mobile Robots 278 11.2.1.1 Simple Protocol for Emulating One-to-One Communication in the Lego NXT Robots 278 11.2.1.2 Flocking and Preserving Collective Motion of the Robot's Group 284 11.2.2 Implementation of the Protocols and Examples of Collective Behavior of Mobile Robots 287 11.2.2.1 One-to-One Communication and Centralized Control in the Lego NXT Robots 287 11.2.2.2 Collective Motion of Lego NXT Robots Preserving the Group Activity 291 11.3 Examples of Indirect and Combined Communication 293 11.3.1 Models of Ant Motion and Simulations of Pheromone Robotic System 293 11.3.2 Biosignaling and Destructive Search by the Group of Mobile Agents 297 11.4 Summary 300 References 301
• 12 Brownian Motion and Swarm Dynamics 305 Eugene Khmelnitsky 12.1 Langevin and Fokker-Plank Formalism 305 12.2 Examples 307 12.3 Summary 316 References 316
• 13 Conclusions 317 Nir Shvalb, Eugene Kagan, and Irad Ben-Gal Index 319.
• (source: Nielsen Book Data)

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot's location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge. The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.
(source: Nielsen Book Data)

• Introduction. Methods of Optimal Search and Screening. Methods of Optimal Foraging. Models of Individual Search and Foraging. Coalitional Search and Swarm Dynamics. Remarks on Swarm Robotic Systems for Search and Foraging. Conclusion. Bibliography. Index.
• (source: Nielsen Book Data)

Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that ar.
(source: Nielsen Book Data)