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

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Edited by (Tel-Aviv University, Israel), Edited by , Edited by (Tel-Aviv University, Israel)
  • Formāts: Hardback, 344 pages, height x width x depth: 244x170x25 mm, weight: 794 g
  • Izdošanas datums: 04-Oct-2019
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119212863
  • ISBN-13: 9781119212867
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Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication, and SwarmingPalielināt
  • Formāts: Hardback, 344 pages, height x width x depth: 244x170x25 mm, weight: 794 g
  • Izdošanas datums: 04-Oct-2019
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119212863
  • ISBN-13: 9781119212867
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781119212867.html
Keywords:

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.

List of Contributors
xi
Preface xiii
Acknowledgments xv
About the Companion Website xvii
Introduction 1(20)
Eugene Kagan
Nir Shvalb
Irad Ben-Gal
1.1 Early History of Robots
1(1)
1.2 Autonomous Robots
2(4)
1.3 Robot Arm Manipulators
6(2)
1.4 Mobile Robots
8(4)
1.5 Multi-Robot Systems and Swarms
12(4)
1.6 Goal and Structure of the Book
16(1)
References
17(4)
1 Motion-Planning Schemes in Global Coordinates
21(14)
Oded Medina
Nir Shvalb
1.1 Motivation
21(1)
1.2 Notations
21(4)
1.2.1 The Configuration Space
22(1)
1.2.2 The Workspace
23(1)
1.2.3 The Weight Function
23(2)
1.3 Motion-Planning Schemes: Known Configuration Spaces
25(5)
1.3.1 Potential-Field Algorithms
25(2)
1.3.2 Grid-Based Algorithms
27(2)
1.3.3 Sampling-Based Algorithms
29(1)
1.4 Motion-Planning Schemes: Partially Known Configuration Spaces
30(3)
1.4.1 BUGO (Reads Bug-Zero)
31(1)
1.4.2 BUG1
32(1)
1.4.3 BUG2
32(1)
1.5 Summary
33(1)
References
33(2)
2 Basic Perception
35(30)
Simon Lineykin
2.1 Basic Scheme of Sensors
35(1)
2.2 Obstacle Sensor (Bumper)
36(12)
2.3 The Odometry Sensor
48(4)
2.4 Distance Sensors
52(11)
2.4.1 The ToF Range Finders
52(4)
2.4.2 The Phase Shift Range Finder
56(3)
2.4.3 Triangulation Range Finder
59(1)
2.4.4 Ultrasonic Rangefinder
60(3)
2.5 Summary
63(1)
References
63(2)
3 Motion in the Global Coordinates
65(22)
Nir Shvalb
Shlomi Hacohen
3.1 Models of Mobile Robots
65(4)
3.1.1 Wheeled Mobile Robots
65(2)
3.1.2 Aerial Mobile Robots
67(2)
3.2 Kinematic and Control of Hilare-Type Mobile Robots
69(5)
3.2.1 Forward Kinematics of Hilare-Type Mobile Robots
69(2)
3.2.2 Velocity Control of Hilare-Type Mobile Robots
71(1)
3.2.3 Trajectory Tracking
72(2)
3.3 Kinematic and Control of Quadrotor Mobile Robots
74(11)
3.3.1 Dynamics of Quadrotor-Type Mobile Robots
74(1)
3.3.2 Forces and Torques Generated by the Propellers
75(1)
3.3.3 Relative End Global Coordinates
76(2)
3.3.4 The Quadrotor Dynamic Model
78(1)
3.3.5 A Simplified Dynamic Model
79(1)
3.3.6 Trajectory Tracking Control of Quadrotors
80(4)
3.3.7 Simulations
84(1)
References
85(2)
4 Motion in Potential Field and Navigation Function
87(22)
Nir Shvalb
Shlomi Hacohen
4.1 Problem Statement
87(2)
4.2 Gradient Descent Method of Optimization
89(5)
4.2.1 Gradient Descent Without Constraints
89(3)
4.2.2 Gradient Descent with Constraints
92(2)
4.3 Minkowski Sum
94(1)
4.4 Potential Field
95(4)
4.5 Navigation Function
99(7)
4.5.1 Navigation Function in Static Deterministic Environment
99(3)
4.5.2 Navigation Function in Static Uncertain Environment
102(2)
4.5.3 Navigation Function and Potential Fields in Dynamic Environment
104(1)
4.5.3.1 Estimation
105(1)
4.5.3.2 Prediction
105(1)
4.5.3.3 Optimization
106(1)
4.6 Summary
106(1)
References
107(2)
5 GNSS and Robot Localization
109(16)
Roi Yozevitch
Boaz Ben-Moshe
5.1 Introduction to Satellite Navigation
109(2)
5.1.1 Trilateration
109(2)
5.2 Position Calculation
111(2)
5.2.1 Multipath Signals
111(1)
5.2.2 GNSS Accuracy Analysis
112(1)
5.2.3 DoP
112(1)
5.3 Coordinate Systems
113(2)
5.3.1 Latitude, Longitude, and Altitude
113(1)
5.3.2 UTM Projection
113(1)
5.3.3 Local Cartesian Coordinates
114(1)
5.4 Velocity Calculation
115(1)
5.4.1 Calculation Outlines
115(1)
5.4.2 Implantation Remarks
116(1)
5.5 Urban Navigation
116(2)
5.5.1 Urban Canyon Navigation
117(1)
5.5.2 Map Matching
117(1)
5.5.3 Dead Reckoning -- Inertial Sensors
118(1)
5.6 Incorporating GNSS Data with INS
118(2)
5.6.1 Modified Particle Filter
118(1)
5.6.2 Estimating Velocity by Combining GNSS and INS
119(1)
5.7 GNSS Protocols
120(1)
5.8 Other Types of GPS
121(2)
5.8.1 A-GPS
121(1)
5.8.2 DGPS Systems
122(1)
5.8.3 RTK Navigation
122(1)
5.9 GNSS Threats
123(1)
5.9.1 GNSS Jamming
123(1)
5.9.2 GNSS Spoofing
123(1)
References
123(2)
6 Motion in Local Coordinates
125(18)
Shraga Shoval
6.1 Global Motion Planning and Navigation
125(3)
6.2 Motion Planning with Uncertainties
128(3)
6.2.1 Uncertainties in Vehicle Performance
128(1)
6.2.1.1 Internal Dynamic Uncertainties
128(1)
6.2.1.2 External Dynamic Uncertainties
129(1)
6.2.2 Sensors Uncertainties
129(1)
6.2.3 Motion-Planning Adaptation to Uncertainties
130(1)
6.3 Online Motion Planning
131(4)
6.3.1 Motion Planning with Differential Constraints
132(2)
6.3.2 Reactive Motion Planning
134(1)
6.4 Global Positioning with Local Maps
135(2)
6.5 UAV Motion Planning in 3D Space
137(2)
6.6 Summary
139(1)
References
140(3)
7 Motion in an Unknown Environment
143(40)
Eugene Kagan
7.1 Probabilistic Map-Based Localization
143(11)
7.1.1 Beliefs Distribution and Markov Localization
145(5)
7.1.2 Motion Prediction and Kalman Localization
150(4)
7.2 Mapping the Unknown Environment and Decision-Making
154(1)
7.2.1 Mapping and Localization
155(6)
7.2.2 Decision-Making under Uncertainties
161(8)
7.3 Examples of Probabilistic Motion Planning
169(9)
7.3.1 Motion Planning in Belief Space
169(7)
7.3.2 Mapping of the Environment
176(2)
7.4 Summary
178(1)
References
179(4)
8 Energy Limitations and Energetic Efficiency of Mobile Robots
183(16)
Michael Ben Chaim
8.1 Introduction
183(1)
8.2 The Problem of Energy Limitations in Mobile Robots
183(2)
8.3 Review of Selected Literature on Power Management and Energy Control in Mobile Robots
185(1)
8.4 Energetic Model of Mobile Robot
186(2)
8.5 Mobile Robots Propulsion
188(4)
8.5.1 Wheeled Mobile Robots Propulsion
189(1)
8.5.2 Propulsion of Mobile Robots with Caterpillar Drive
190(2)
8.6 Energetic Model of Mechanical Energies Sources
192(3)
8.6.1 Internal Combustion Engines
193(1)
8.6.2 Lithium Electric Batteries
194(1)
8.7 Summary
195(1)
References
195(4)
9 Multi-Robot Systems and Swarming
199(1)
Eugene Kagan
Nir Shvalb
Shlomi Hacohen
Alexander Novoselsky
9.1 Multi-Agent Systems and Swarm Robotics
199(1)
9.1.1 Principles of Multi-Agent Systems
200(8)
9.1.2 Basic Flocking and Methods of Aggregation and Collision Avoidance
208(10)
9.2 Control of the Agents and Positioning of Swarms
218(18)
9.2.1 Agent-Based Models
219(15)
9.2.2 Probabilistic Models of Swarm Dynamics
234(2)
9.3 Summary
236(2)
References
238(5)
10 Collective Motion with Shared Environment Map
243(30)
Eugene Kagan
Irad Ben-Gal
10.1 Collective Motion with Shared Information
243(10)
10.1.1 Motion in Common Potential Field
244(6)
10.1.2 Motion in the Terrain with Sharing Information About Local Environment
250(3)
10.2 Swarm Dynamics in a Heterogeneous Environment
253(8)
10.2.1 Basic Flocking in Heterogeneous Environment and External Potential Field
253(6)
10.2.2 Swarm Search with Common Probability Map
259(2)
10.3 Examples of Swarm Dynamics with Shared Environment Map
261(9)
10.3.1 Probabilistic Search with Multiple Searchers
261(3)
10.3.2 Obstacle and Collision Avoidance Using Attraction/Repulsion Potentials
264(6)
10.4 Summary
270(1)
References
270(3)
11 Collective Motion with Direct and Indirect Communication
273(32)
Eugene Kagan
Irad Ben-Gal
11.1 Communication Between Mobile Robots in Groups
273(4)
11.2 Simple Communication Protocols and Examples of Collective Behavior
277(16)
11.2.1 Examples of Communication Protocols for the Group of Mobile Robots
278(1)
11.2.1.1 Simple Protocol for Emulating One-to-One Communication in the Lego NXT Robots
278(6)
11.2.1.2 Flocking and Preserving Collective Motion of the Robot's Group
284(3)
11.2.2 Implementation of the Protocols and Examples of Collective Behavior of Mobile Robots
287(1)
11.2.2.1 One-to-One Communication and Centralized Control in the Lego NXT Robots
287(4)
11.2.2.2 Collective Motion of Lego NXT Robots Preserving the Group Activity
291(2)
11.3 Examples of Indirect and Combined Communication
293(7)
11.3.1 Models of Ant Motion and Simulations of Pheromone Robotic System
293(4)
11.3.2 Biosignaling and Destructive Search by the Group of Mobile Agents
297(3)
11.4 Summary
300(1)
References
301(4)
12 Brownian Motion and Swarm Dynamics
305(1)
Eugene Khmelnitsky
12.1 Langevin and Fokker-Plank Formalism
305(2)
12.2 Examples
307(9)
12.3 Summary
316(1)
References
316(1)
13 Conclusions
317(2)
Nir Shvalb
Eugene Kagan
Irad Ben-Gal
Index 319
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.