Collaborative Fleet Maneuvering for Multiple Autonomous Vehicle Systems

ISBN-13: 9789811957970 / Angielski / Twarda / 2022

Yuanzhe Wang;Danwei Wang

Opis

This book presents theoretical foundations and technical implementation guidelines for multi-vehicle fleet maneuvering, which can be implemented by readers and can also be a basis for future research. As a research monograph, this book presents fundamental concepts, theories, and technologies for localization, motion planning, and control of multi-vehicle systems, which can be a reference book for researchers and graduate students from different levels. As a technical guide, this book provides implementation guidelines, pseudocode, and flow diagrams for practitioners to develop their own systems. Readers should have a preliminary knowledge of mobile robotics, state estimation and automatic control to fully understand the contents in this book. To make this book more readable and understandable, extensive experimental results are presented to support each chapter.

Spis treści

1 Introduction

1.1 Background

1.1.1 Motivations

1.1.2 Challenges

1.2 Objectives of This Book

1.3 Preview of Chapters

References

2 Technical Background

2.1 Vehicle Model

2.1.1 Kinematic Model

2.1.2 Dynamic Model

2.2 Fleet Configuration

2.2.1 Description

2.2.2 Several Common Configurations

2.2.3 Optimal Configuration

2.3 Collaborative Localization

2.3.1 Infrastructure-Based Localization

2.3.2 Infrastructure-Free Localization

2.4 Fleet Keeping and Reconstruction

2.4.1 Fleet Keeping

2.4.2 Fleet Reconstruction

2.5 Collision Avoidance

2.5.1 Map-Based Collision Avoidance

2.5.2 Reactive Collision Avoidance

References

3 GPS/INS Based Virtual-Structure Maneuvering in Outdoor Open Environments

3.1 Introduction

3.2 Problem Formulation

3.2.1 Vehicle Model

3.2.2 Fleet Configuration

3.2.3 Problem Statement

3.3 Approach

3.3.1 Collaborative Localization Based on GPS/INS

3.3.2 Motion Planning and Control for Fleet Keeping

3.3.3 Intra-Fleet Information Sharing

3.4 Validation

3.4.1 Experimental Setup

3.4.2 Experimental Results

3.5 Conclusions

References

4 Point Cloud Matching Based Virtual-Structure Maneuvering in Cluttered

Environments

4.1 Introduction

4.2 Problem Formulation

4.2.1 Vehicle Model

4.2.2 Fleet Configuration

4.2.3 Problem Statement

4.3 Approach

4.3.1 Collaborative Localization Based on Point Cloud Matching

4.3.2 Motion Planning and Control with Multiple Objectives

4.3.3 Intra-Fleet Information Sharing

4.4 Validation

4.4.1 Experimental Setup

4.4.2 Experimental Results

4.5 Conclusions

References

5 UWB Based Flexible Fleet Maneuvering in Featureless Environments

5.1 Introduction

5.2 Problem Formulation

5.2.1 Vehicle Model

5.2.2 Fleet Configuration

5.2.3 Problem Statement

5.3 Approach

5.3.1 Collaborative Localization Based on UWB

5.3.2 Motion Planning and Control for Flexile Fleet Keeping

5.3.3 Intra-Fleet Information Sharing

5.4 Validation

5.4.1 Experimental Setup

5.4.2 Experimental Results

5.5 Conclusions

References

6 Vision Based Leader-Follower Queue Maneuvering in Cluttered Environments

6.1 Introduction

6.2 Problem Formulation

6.2.1 Vehicle Model

6.2.2 Fleet Configuration

6.2.3 Leader-Loss Situation

6.2.4 Problem Statement

6.3 Approach

6.3.1 Collaborative Localization Based on Vision Detection

6.3.2 Motion Planning and Control for Leader-Follower Queue Keeping

6.3.3 Solution to Leader-Loss Situation

6.3.4 Intra-Fleet Information Sharing

6.4 Validation

6.4.1 Experimental Setup

6.4.2 Experimental Results

6.5 Conclusions

References

7 Vision Based Flexible Fleet Maneuvering in Cluttered Environments

7.1 Introduction

7.2 Problem Formulation

7.2.1 Vehicle Model

7.2.2 Fleet Configuration

7.2.3 Problem Statement

7.3 Approach

7.3.1 Collaborative Localization Based on Vision Detection

7.3.2 Motion Planning and Control for Flexible Fleet Keeping

7.3.3 Intra-Fleet Information Sharing

7.4 Validation

7.4.1 Experimental Setup

7.4.2 Experimental Results

7.5 Conclusions

References

8 Local Map Matching Based Leader-Follower Path Retracing Maneuvering in GPS-Denied Environments

8.1 Introduction

8.2 Problem Formulation

8.2.1 Vehicle Model

8.2.2 Fleet Configuration

8.2.3 Problem Statement

8.3 Approach

8.3.1 Collaborative Localization Based on Local Map Matching

8.3.2 Motion Planning and Control for Leader-Follower Path Retracing

8.3.3 Intra-Fleet Information Sharing

8.4 Validation

8.4.1 Experimental Setup

8.4.2 Experimental Results

8.5 Conclusions

References

9 Multi-UAV Optimal Fleet Flying for Area Patrol in Constrained Environments

9.1 Introduction

9.2 Problem Formulation

9.2.1 Vehicle Model

9.2.2 Fleet Configuration

9.2.3 Problem Statement

9.3 Approach

9.3.1 Optimal Configuration for Area Patrol

9.3.2 Motion Planning and Control for Fleet Keeping

9.3.3 Information Sharing Strategy

9.4 Validation

9.4.1 Simulation Setup

9.4.2 Simulation Results

9.5 Conclusions

References

10 Conclusion

10.1 Summary

10.2 Open Challenges

O autorze

Yuanzhe Wang received the B.Eng. degree from the Southeast University, China, in 2010, the M.Eng. degree from the Beihang University, China, in 2013, and the Ph.D. degree from the Nanyang Technological University (NTU), Singapore, in 2019. He is a Research Fellow in the School of Electrical and Electronic Engineering, NTU. He has served as an Associate Editor for The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) from 2020 to 2022. His current research interests include mobile robotics, control application, and cybersecurity in robotics.

 

Danwei Wang received his Ph.D. and M.S.E. degrees from the University of Michigan, Ann Arbor in 1989 and 1984, respectively. He received his B.E. degree from the South China University of Technology, China, in 1982. He is a Professor in the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore. He has served as the Head of the Division of Control and Instrumentation, NTU from 2005 to 2011, the Director of the Center for System Intelligence and Efficiency, NTU from 2014 to 2016, and the Director of the ST Engineering-NTU Corporate Laboratory, NTU from 2015 to 2021. He also served as general chairman, technical chairman and various positions in several international conferences. He was a recipient of Alexander von Humboldt fellowship, Germany. He is a Fellow of Academy of Engineering, Singapore, and a Fellow of IEEE. His research interests include robotics, control engineering, and fault diagnosis.