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.
Author(s): Yuanzhe Wang, Danwei Wang
Series: Springer Tracts in Advanced Robotics, 150
Publisher: Springer
Year: 2022
Language: English
Pages: 159
City: Singapore
Preface
Contents
Acronyms
1 Introduction
1.1 Background
1.1.1 Motivation
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 UGV Model
2.1.2 UAV 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
2.4.3 Cohesion Maintenance
2.4.4 Visibility Maintenance
2.5 Collision Avoidance
2.5.1 Map-Based Collision Avoidance
2.5.2 Reactive Collision Avoidance
References
3 Anchor-Based Flexible Fleet Maneuvering in 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 Anchor-Based Localization
3.3.2 Flexible Fleet Planning and Control
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 Map-Based Virtual-Structure Fleet Maneuvering in Cluttered Environments
4.1 Introduction
4.2 Problem Formulation
4.2.1 Vehicle Model
4.2.2 Fleet Configuration
4.2.3 System Constraints
4.2.4 Problem Statement
4.3 Approach
4.3.1 Map-Based Localization
4.3.2 Multi-objective Fleet Planning and Control
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 Vision-Based Leader-Follower Queue Maneuvering in Unknown Cluttered Environments
5.1 Introduction
5.2 Problem Formulation
5.2.1 Vehicle Model
5.2.2 Fleet Configuration
5.2.3 System Constraints
5.2.4 Leader-Loss Situation
5.2.5 Problem Statement
5.3 Approach
5.3.1 Vision Detection and Fleet Keeping
5.3.2 Unknown Obstacle Avoidance
5.3.3 Leader-Loss Reaction
5.4 Validation
5.4.1 Experimental Setup
5.4.2 Experimental Results
5.5 Conclusions
References
6 Vision-Based Flexible Fleet Maneuvering in Unknown Cluttered Environments
6.1 Introduction
6.2 Problem Formulation
6.2.1 Vehicle Model
6.2.2 Fleet Configuration
6.2.3 System Constraints
6.2.4 Problem Statement
6.3 Approach
6.3.1 Vision-Based Pose Estimation
6.3.2 Flexible Fleet Planning
6.3.3 Multi-objective Resolution
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 Map Matching Based Leader-Follower Path Retracing in Infrastructure-Free 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 Map Matching Based Relative Localization
7.3.2 Fleet Motion Planning and Control
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 Multi-UAV Optimal Fleet Flying for Air Patrol in Constrained Environments
8.1 Introduction
8.2 Problem Formulation
8.2.1 Vehicle Model
8.2.2 Fleet Configuration
8.2.3 Communication Network
8.2.4 Problem Statement
8.3 Approach
8.3.1 Configuration Design
8.3.2 Reference Generation
8.3.3 Trajectory Replanning
8.3.4 A2G and A2A Information Sharing
8.4 Validation
8.4.1 Simulation Setup
8.4.2 Simulation Results
8.5 Conclusions
References
9 Conclusion
9.1 Summary
9.2 Open Challenges
