Collaborative Fleet Maneuvering for Multiple Autonomous Vehicle Systems
1st Edition
Published on 23. September 2023
Book
Paperback/Softback
XIII, 153 pages
978-981-19-5800-7 (ISBN)
Description
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.
More details
Product info
Paperback
Series
Edition
1st ed. 2023
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Illustrations
7
38 farbige Abbildungen, 7 s/w Abbildungen
XIII, 153 p. 45 illus., 38 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 9 mm
Weight
296 gr
ISBN-13
978-981-19-5800-7 (9789811958007)
DOI
10.1007/978-981-19-5798-7
Schweitzer Classification
Other editions
Additional editions
Yuanzhe Wang | Danwei Wang
Collaborative Fleet Maneuvering for Multiple Autonomous Vehicle Systems
Book
09/2022
1st Edition
Springer
€106.99
Shipment within 3-4 weeks
Persons
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.
Content
1 Introduction1.1 Background1.1.1 Motivations1.1.2 Challenges1.2 Objectives of This Book1.3 Preview of ChaptersReferences2 Technical Background2.1 Vehicle Model2.1.1 Kinematic Model2.1.2 Dynamic Model2.2 Fleet Configuration2.2.1 Description2.2.2 Several Common Configurations2.2.3 Optimal Configuration2.3 Collaborative Localization2.3.1 Infrastructure-Based Localization2.3.2 Infrastructure-Free Localization2.4 Fleet Keeping and Reconstruction2.4.1 Fleet Keeping2.4.2 Fleet Reconstruction2.5 Collision Avoidance2.5.1 Map-Based Collision Avoidance2.5.2 Reactive Collision AvoidanceReferences3 GPS/INS Based Virtual-Structure Maneuvering in Outdoor Open Environments3.1 Introduction3.2 Problem Formulation3.2.1 Vehicle Model3.2.2 Fleet Configuration3.2.3 Problem Statement3.3 Approach3.3.1 Collaborative Localization Based on GPS/INS3.3.2 Motion Planning and Control for Fleet Keeping3.3.3 Intra-Fleet Information Sharing3.4 Validation3.4.1 Experimental Setup3.4.2 Experimental Results3.5 ConclusionsReferences4 Point Cloud Matching Based Virtual-Structure Maneuvering in ClutteredEnvironments4.1 Introduction4.2 Problem Formulation4.2.1 Vehicle Model4.2.2 Fleet Configuration4.2.3 Problem Statement4.3 Approach4.3.1 Collaborative Localization Based on Point Cloud Matching4.3.2 Motion Planning and Control with Multiple Objectives4.3.3 Intra-Fleet Information Sharing4.4 Validation4.4.1 Experimental Setup4.4.2 Experimental Results4.5 ConclusionsReferences5 UWB Based Flexible Fleet Maneuvering in Featureless Environments5.1 Introduction5.2 Problem Formulation5.2.1 Vehicle Model5.2.2 Fleet Configuration5.2.3 Problem Statement5.3 Approach5.3.1 Collaborative Localization Based on UWB5.3.2 Motion Planning and Control for Flexile Fleet Keeping5.3.3 Intra-Fleet Information Sharing5.4 Validation5.4.1 Experimental Setup5.4.2 Experimental Results5.5 ConclusionsReferences6 Vision Based Leader-Follower Queue Maneuvering in Cluttered Environments6.1 Introduction6.2 Problem Formulation6.2.1 Vehicle Model6.2.2 Fleet Configuration6.2.3 Leader-Loss Situation6.2.4 Problem Statement6.3 Approach6.3.1 Collaborative Localization Based on Vision Detection6.3.2 Motion Planning and Control for Leader-Follower Queue Keeping6.3.3 Solution to Leader-Loss Situation6.3.4 Intra-Fleet Information Sharing6.4 Validation6.4.1 Experimental Setup6.4.2 Experimental Results6.5 ConclusionsReferences7 Vision Based Flexible Fleet Maneuvering in Cluttered Environments7.1 Introduction7.2 Problem Formulation7.2.1 Vehicle Model7.2.2 Fleet Configuration7.2.3 Problem Statement7.3 Approach7.3.1 Collaborative Localization Based on Vision Detection7.3.2 Motion Planning and Control for Flexible Fleet Keeping7.3.3 Intra-Fleet Information Sharing7.4 Validation7.4.1 Experimental Setup7.4.2 Experimental Results7.5 ConclusionsReferences8 Local Map Matching Based Leader-Follower Path Retracing Maneuvering in GPS-Denied Environments8.1 Introduction8.2 Problem Formulation8.2.1 Vehicle Model8.2.2 Fleet Configuration8.2.3 Problem Statement8.3 Approach8.3.1 Collaborative Localization Based on Local Map Matching8.3.2 Motion Planning and Control for Leader-Follower Path Retracing8.3.3 Intra-Fleet Information Sharing8.4 Validation8.4.1 Experimental Setup8.4.2 Experimental Results8.5 ConclusionsReferences9 Multi-UAV Optimal Fleet Flying for Area Patrol in Constrained Environments9.1 Introduction9.2 Problem Formulation9.2.1 Vehicle Model9.2.2 Fleet Configuration9.2.3 Problem Statement9.3 Approach9.3.1 Optimal Configuration for Area Patrol9.3.2 Motion Planning and Control for Fleet Keeping9.3.3 Information Sharing Strategy9.4 Validation9.4.1 Simulation Setup9.4.2 Simulation Results9.5 ConclusionsReferences10 Conclusion10.1 Summary10.2 Open Challenges