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

Intro -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Acknowledgments -- About the Companion Website -- Introduction -- I.1 Early History of Robots -- I.2 Autonomous Robots -- I.3 Robot Arm Manipulators -- I.4 Mobile Robots -- I.5 Multi-Robot Systems and Swarms -- I.6 Goal and Structure of the Book -- References -- Chapter 1 Motion-Planning Schemes in Global Coordinates -- 1.1 Motivation -- 1.2 Notations -- 1.2.1 The Configuration Space -- 1.2.2 The Workspace -- 1.2.3 The Weight Function -- 1.3 Motion-Planning Schemes: Known Configuration Spaces -- 1.3.1 Potential-Field Algorithms -- 1.3.2 Grid-Based Algorithms -- 1.3.3 Sampling-Based Algorithms -- 1.4 Motion-Planning Schemes: Partially Known Configuration Spaces -- 1.4.1 BUG0 (Reads Bug-Zero) -- 1.4.2 BUG1 -- 1.4.3 BUG2 -- 1.5 Summary -- References -- Chapter 2 Basic Perception -- 2.1 Basic Scheme of Sensors -- 2.2 Obstacle Sensor (Bumper) -- 2.3 The Odometry Sensor -- 2.4 Distance Sensors -- 2.4.1 The ToF Range Finders -- 2.4.2 The Phase Shift Range Finder -- 2.4.3 Triangulation Range Finder -- 2.4.4 Ultrasonic Rangefinder -- 2.5 Summary -- References -- Chapter 3 Motion in the Global Coordinates -- 3.1 Models of Mobile Robots -- 3.1.1 Wheeled Mobile Robots -- 3.1.2 Aerial Mobile Robots -- 3.2 Kinematic and Control of Hilare-Type Mobile Robots -- 3.2.1 Forward Kinematics of Hilare-Type Mobile Robots -- 3.2.2 Velocity Control of Hilare-Type Mobile Robots -- 3.2.3 Trajectory Tracking -- 3.3 Kinematic and Control of Quadrotor Mobile Robots -- 3.3.1 Dynamics of Quadrotor-Type Mobile Robots -- 3.3.2 Forces and Torques Generated by the Propellers -- 3.3.3 Relative End Global Coordinates -- 3.3.4 The Quadrotor Dynamic Model -- 3.3.5 A Simplified Dynamic Model -- 3.3.6 Trajectory Tracking Control of Quadrotors -- 3.3.7 Simulations -- References.

Chapter 4 Motion in Potential Field and Navigation Function -- 4.1 Problem Statement -- 4.2 Gradient Descent Method of Optimization -- 4.2.1 Gradient Descent Without Constraints -- 4.2.2 Gradient Descent with Constraints -- 4.3 Minkowski Sum -- 4.4 Potential Field -- 4.5 Navigation Function -- 4.5.1 Navigation Function in Static Deterministic Environment -- 4.5.2 Navigation Function in Static Uncertain Environment -- 4.5.3 Navigation Function and Potential Fields in Dynamic Environment -- 4.5.3.1 Estimation -- 4.5.3.2 Prediction -- 4.5.3.3 Optimization -- 4.6 Summary -- References -- Chapter 5 GNSS and Robot Localization -- 5.1 Introduction to Satellite Navigation -- 5.1.1 Trilateration -- 5.2 Position Calculation -- 5.2.1 Multipath Signals -- 5.2.2 GNSS Accuracy Analysis -- 5.2.3 DoP -- 5.3 Coordinate Systems -- 5.3.1 Latitude, Longitude, and Altitude -- 5.3.2 UTM Projection -- 5.3.3 Local Cartesian Coordinates -- 5.4 Velocity Calculation -- 5.4.1 Calculation Outlines -- 5.4.2 Implantation Remarks -- 5.5 Urban Navigation -- 5.5.1 Urban Canyon Navigation -- 5.5.2 Map Matching -- 5.5.3 Dead Reckoning-Inertial Sensors -- 5.6 Incorporating GNSS Data with INS -- 5.6.1 Modified Particle Filter -- 5.6.2 Estimating Velocity by Combining GNSS and INS -- 5.7 GNSS Protocols -- 5.8 Other Types of GPS -- 5.8.1 A-GPS -- 5.8.2 DGPS Systems -- 5.8.3 RTK Navigation -- 5.9 GNSS Threats -- 5.9.1 GNSS Jamming -- 5.9.2 GNSS Spoofing -- References -- Chapter 6 Motion in Local Coordinates -- 6.1 Global Motion Planning and Navigation -- 6.2 Motion Planning with Uncertainties -- 6.2.1 Uncertainties in Vehicle Performance -- 6.2.1.1 Internal Dynamic Uncertainties -- 6.2.1.2 External Dynamic Uncertainties -- 6.2.2 Sensors Uncertainties -- 6.2.3 Motion-Planning Adaptation to Uncertainties -- 6.3 Online Motion Planning -- 6.3.1 Motion Planning with Differential Constraints.

6.3.2 Reactive Motion Planning -- 6.4 Global Positioning with Local Maps -- 6.5 UAV Motion Planning in 3D Space -- 6.6 Summary -- References -- Chapter 7 Motion in an Unknown Environment -- 7.1 Probabilistic Map-Based Localization -- 7.1.1 Beliefs Distribution and Markov Localization -- 7.1.2 Motion Prediction and Kalman Localization -- 7.2 Mapping the Unknown Environment and Decision-Making -- 7.2.1 Mapping and Localization -- 7.2.2 Decision-Making under Uncertainties -- 7.3 Examples of Probabilistic Motion Planning -- 7.3.1 Motion Planning in Belief Space -- 7.3.2 Mapping of the Environment -- 7.4 Summary -- References -- Chapter 8 Energy Limitations and Energetic Efficiency of Mobile Robots -- 8.1 Introduction -- 8.2 The Problem of Energy Limitations in Mobile Robots -- 8.3 Review of Selected Literature on Power Management and Energy Control in Mobile Robots -- 8.4 Energetic Model of Mobile Robot -- 8.5 Mobile Robots Propulsion -- 8.5.1 Wheeled Mobile Robots Propulsion -- 8.5.2 Propulsion of Mobile Robots with Caterpillar Drive -- 8.6 Energetic Model of Mechanical Energies Sources -- 8.6.1 Internal Combustion Engines -- 8.6.2 Lithium Electric Batteries -- 8.7 Summary -- References -- Chapter 9 Multi-Robot Systems and Swarming -- 9.1 Multi-Agent Systems and Swarm Robotics -- 9.1.1 Principles of Multi-Agent Systems -- 9.1.2 Basic Flocking and Methods of Aggregation and Collision Avoidance -- 9.2 Control of the Agents and Positioning of Swarms -- 9.2.1 Agent-Based Models -- 9.2.2 Probabilistic Models of Swarm Dynamics -- 9.3 Summary -- References -- Chapter 10 Collective Motion with Shared Environment Map -- 10.1 Collective Motion with Shared Information -- 10.1.1 Motion in Common Potential Field -- 10.1.2 Motion in the Terrain with Sharing Information About Local Environment -- 10.2 Swarm Dynamics in a Heterogeneous Environment.

10.2.1 Basic Flocking in Heterogeneous Environment and External Potential Field -- 10.2.2 Swarm Search with Common Probability Map -- 10.3 Examples of Swarm Dynamics with Shared Environment Map -- 10.3.1 Probabilistic Search with Multiple Searchers -- 10.3.2 Obstacle and Collision Avoidance Using Attraction/Repulsion Potentials -- 10.4 Summary -- References -- Chapter 11 Collective Motion with Direct and Indirect Communication -- 11.1 Communication Between Mobile Robots in Groups -- 11.2 Simple Communication Protocols and Examples of Collective Behavior -- 11.2.1 Examples of Communication Protocols for the Group of Mobile Robots -- 11.2.1.1 Simple Protocol for Emulating One-to-One Communication in the Lego NXT Robots -- 11.2.1.2 Flocking and Preserving Collective Motion of the Robot's Group -- 11.2.2 Implementation of the Protocols and Examples of Collective Behavior of Mobile Robots -- 11.2.2.1 One-to-One Communication and Centralized Control in the Lego NXT Robots -- 11.2.2.2 Collective Motion of Lego NXT Robots Preserving the Group Activity -- 11.3 Examples of Indirect and Combined Communication -- 11.3.1 Models of Ant Motion and Simulations of Pheromone Robotic System -- 11.3.2 Biosignaling and Destructive Search by the Group of Mobile Agents -- 11.4 Summary -- References -- Chapter 12 Brownian Motion and Swarm Dynamics -- 12.1 Langevin and Fokker-Plank Formalism -- 12.2 Examples -- 12.3 Summary -- References -- Chapter 13 Conclusions -- Index -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.