Autonomous Mobile Robots and Multi-Robot Systems

Name: Autonomous Mobile Robots and Multi-Robot Systems | Motion-Planning, Communication, and Swarming
Brand: Jossey-Bass
Price: 113.99 EUR
Availability: OnlineOnly

Motion-Planning, Communication, and Swarming

Eugene Kagan Nir Shvalb Irad Ben-Gal(Herausgeber*in)

Jossey-Bass (Verlag)

1. Auflage

Erschienen am 2. September 2019

344 Seiten

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Beschreibung

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.

Weitere Details

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Inhalt

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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