Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication, and Swarming

ISBN-13: 9781119212867 / Angielski / Twarda / 2019 / 344 str.

Eugene Kagan;Nir Shvalb;Irad Ben-Gal

Opis

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming covers the methods and algorithms for navigation, motion planning and control of mobile robots acting individually and in groups. Following recent challenges in locomotion, communication and control, the book 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. Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming provides a theoretical and practical guide to the navigation of mobile robots. It is self-contained and includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots. It is also accompanied by a website hosting codes, videos and powerpoint slides

Spis treści

List of Contributors xiPreface xiiiAcknowledgments xvAbout the Companion Website xviiIntroduction 1Eugene Kagan, Nir Shvalb, and Irad Ben-GalI.1 Early History of Robots 1I.2 Autonomous Robots 2I.3 Robot Arm Manipulators 6I.4 Mobile Robots 8I.5 Multi-Robot Systems and Swarms 12I.6 Goal and Structure of the Book 16References 171 Motion-Planning Schemes in Global Coordinates 21Oded Medina and Nir Shvalb1.1 Motivation 211.2 Notations 211.2.1 The Configuration Space 221.2.2 The Workspace 231.2.3 The Weight Function 231.3 Motion-Planning Schemes: Known Configuration Spaces 251.3.1 Potential-Field Algorithms 251.3.2 Grid-Based Algorithms 271.3.3 Sampling-Based Algorithms 291.4 Motion-Planning Schemes: Partially Known Configuration Spaces 301.4.1 BUG0 (Reads Bug-Zero) 311.4.2 BUG1 321.4.3 BUG2 321.5 Summary 33References 332 Basic Perception 35Simon Lineykin2.1 Basic Scheme of Sensors 352.2 Obstacle Sensor (Bumper) 362.3 The Odometry Sensor 482.4 Distance Sensors 522.4.1 The ToF Range Finders 522.4.2 The Phase Shift Range Finder 562.4.3 Triangulation Range Finder 592.4.4 Ultrasonic Rangefinder 602.5 Summary 63References 633 Motion in the Global Coordinates 65Nir Shvalb and Shlomi Hacohen3.1 Models of Mobile Robots 653.1.1 Wheeled Mobile Robots 653.1.2 Aerial Mobile Robots 673.2 Kinematic and Control of Hilare-Type Mobile Robots 693.2.1 Forward Kinematics of Hilare-Type Mobile Robots 693.2.2 Velocity Control of Hilare-Type Mobile Robots 713.2.3 Trajectory Tracking 723.3 Kinematic and Control of Quadrotor Mobile Robots 743.3.1 Dynamics of Quadrotor-Type Mobile Robots 743.3.2 Forces and Torques Generated by the Propellers 753.3.3 Relative End Global Coordinates 763.3.4 The Quadrotor Dynamic Model 783.3.5 A Simplified Dynamic Model 793.3.6 Trajectory Tracking Control of Quadrotors 803.3.7 Simulations 84References 854 Motion in Potential Field and Navigation Function 87Nir Shvalb and Shlomi Hacohen4.1 Problem Statement 874.2 Gradient Descent Method of Optimization 894.2.1 Gradient Descent Without Constraints 894.2.2 Gradient Descent with Constraints 924.3 Minkowski Sum 944.4 Potential Field 954.5 Navigation Function 994.5.1 Navigation Function in Static Deterministic Environment 994.5.2 Navigation Function in Static Uncertain Environment 1024.5.3 Navigation Function and Potential Fields in Dynamic Environment 1044.5.3.1 Estimation 1054.5.3.2 Prediction 1054.5.3.3 Optimization 1064.6 Summary 106References 1075 GNSS and Robot Localization 109Roi Yozevitch and Boaz Ben-Moshe5.1 Introduction to Satellite Navigation 1095.1.1 Trilateration 1095.2 Position Calculation 1115.2.1 Multipath Signals 1115.2.2 GNSS Accuracy Analysis 1125.2.3 DoP 1125.3 Coordinate Systems 1135.3.1 Latitude, Longitude, and Altitude 1135.3.2 UTM Projection 1135.3.3 Local Cartesian Coordinates 1145.4 Velocity Calculation 1155.4.1 Calculation Outlines 1155.4.2 Implantation Remarks 1165.5 Urban Navigation 1165.5.1 Urban Canyon Navigation 1175.5.2 Map Matching 1175.5.3 Dead Reckoning - Inertial Sensors 1185.6 Incorporating GNSS Data with INS 1185.6.1 Modified Particle Filter 1185.6.2 Estimating Velocity by Combining GNSS and INS 1195.7 GNSS Protocols 1205.8 Other Types of GPS 1215.8.1 A-GPS 1215.8.2 DGPS Systems 1225.8.3 RTK Navigation 1225.9 GNSS Threats 1235.9.1 GNSS Jamming 1235.9.2 GNSS Spoofing 123References 1236 Motion in Local Coordinates 125Shraga Shoval6.1 Global Motion Planning and Navigation 1256.2 Motion Planning with Uncertainties 1286.2.1 Uncertainties in Vehicle Performance 1286.2.1.1 Internal Dynamic Uncertainties 1286.2.1.2 External Dynamic Uncertainties 1296.2.2 Sensors Uncertainties 1296.2.3 Motion-Planning Adaptation to Uncertainties 1306.3 Online Motion Planning 1316.3.1 Motion Planning with Differential Constraints 1326.3.2 Reactive Motion Planning 1346.4 Global Positioning with Local Maps 1356.5 UAV Motion Planning in 3D Space 1376.6 Summary 139References 1407 Motion in an Unknown Environment 143Eugene Kagan7.1 Probabilistic Map-Based Localization 1437.1.1 Beliefs Distribution and Markov Localization 1457.1.2 Motion Prediction and Kalman Localization 1507.2 Mapping the Unknown Environment and Decision-Making 1547.2.1 Mapping and Localization 1557.2.2 Decision-Making under Uncertainties 1617.3 Examples of Probabilistic Motion Planning 1697.3.1 Motion Planning in Belief Space 1697.3.2 Mapping of the Environment 1767.4 Summary 178References 1798 Energy Limitations and Energetic Efficiency of Mobile Robots 183Michael Ben Chaim8.1 Introduction 1838.2 The Problem of Energy Limitations in Mobile Robots 1838.3 Review of Selected Literature on Power Management and Energy Control in Mobile Robots 1858.4 Energetic Model of Mobile Robot 1868.5 Mobile Robots Propulsion 1888.5.1 Wheeled Mobile Robots Propulsion 1898.5.2 Propulsion of Mobile Robots with Caterpillar Drive 1908.6 Energetic Model of Mechanical Energies Sources 1928.6.1 Internal Combustion Engines 1938.6.2 Lithium Electric Batteries 1948.7 Summary 195References 1959 Multi-Robot Systems and Swarming 199Eugene Kagan, Nir Shvalb, Shlomi Hacohen, and Alexander Novoselsky9.1 Multi-Agent Systems and Swarm Robotics 1999.1.1 Principles of Multi-Agent Systems 2009.1.2 Basic Flocking and Methods of Aggregation and Collision Avoidance 2089.2 Control of the Agents and Positioning of Swarms 2189.2.1 Agent-Based Models 2199.2.2 Probabilistic Models of Swarm Dynamics 2349.3 Summary 236References 23810 Collective Motion with Shared Environment Map 243Eugene Kagan and Irad Ben-Gal10.1 Collective Motion with Shared Information 24310.1.1 Motion in Common Potential Field 24410.1.2 Motion in the Terrain with Sharing Information About Local Environment 25010.2 Swarm Dynamics in a Heterogeneous Environment 25310.2.1 Basic Flocking in Heterogeneous Environment and External Potential Field 25310.2.2 Swarm Search with Common Probability Map 25910.3 Examples of Swarm Dynamics with Shared Environment Map 26110.3.1 Probabilistic Search with Multiple Searchers 26110.3.2 Obstacle and Collision Avoidance Using Attraction/Repulsion Potentials 26410.4 Summary 270References 27011 Collective Motion with Direct and Indirect Communication 273Eugene Kagan and Irad Ben-Gal11.1 Communication Between Mobile Robots in Groups 27311.2 Simple Communication Protocols and Examples of Collective Behavior 27711.2.1 Examples of Communication Protocols for the Group of Mobile Robots 27811.2.1.1 Simple Protocol for Emulating One-to-One Communication in the Lego NXT Robots 27811.2.1.2 Flocking and Preserving Collective Motion of the Robot's Group 28411.2.2 Implementation of the Protocols and Examples of Collective Behavior of Mobile Robots 28711.2.2.1 One-to-One Communication and Centralized Control in the Lego NXT Robots 28711.2.2.2 Collective Motion of Lego NXT Robots Preserving the Group Activity 29111.3 Examples of Indirect and Combined Communication 29311.3.1 Models of Ant Motion and Simulations of Pheromone Robotic System 29311.3.2 Biosignaling and Destructive Search by the Group of Mobile Agents 29711.4 Summary 300References 30112 Brownian Motion and Swarm Dynamics 305Eugene Khmelnitsky12.1 Langevin and Fokker-Plank Formalism 30512.2 Examples 30712.3 Summary 316References 31613 Conclusions 317Nir Shvalb, Eugene Kagan, and Irad Ben-GalIndex 319

O autorze

EUGENE KAGAN, PHD, is a senior lecturer in the Department of Industrial Engineering at Ariel University, Israel, and is also an advisor in the Department of Mathematics at the Weizmann Institute of Science and an affiliated researcher at the Laboratory for AI, Machine Learning, & Business Data Analytics at Tel-Aviv University.NIR SHVALB, PHD, is a Professor at the Faculty of Engineering at Ariel University, Israel, and the joint head of Kinematics and Computational Geometry laboratory.IRAD BEN-GAL, PHD, is a Professor at the Department of Industrial Engineering at Tel-Aviv University, Israel. He is the head of the Laboratory for AI, Machine Learning, Business & Data Analytics (LAMBDA) at Tel-Aviv University.