Resilient Fusion Navigation Techniques: Collaboration in Swarm

226 Pages·2022·16.119 MB·English

by  Rong Wang, Zhi Xiong, Jianye Liu

Unmanned System Technologies
Rong Wang
Zhi Xiong
Jianye Liu
Resilient Fusion
Navigation
Techniques:
Collaboration in
Swarm
Unmanned System Technologies
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Rong Wang Zhi Xiong Jianye Liu
Resilient Fusion Navigation
Techniques: Collaboration
in Swarm
Rong Wang Zhi Xiong
Navigation Research Center Navigation Research Center
College of Automation Engineering College of Automation Engineering
Nanjing University of Aeronautics Nanjing University of Aeronautics
and Astronautics and Astronautics
Nanjing, Jiangsu, China Nanjing, Jiangsu, China
Jianye Liu
Navigation Research Center
College of Automation Engineering
Nanjing University of Aeronautics
and Astronautics
Nanjing, Jiangsu, China
ISSN 2523-3734 ISSN 2523-3742 (electronic)
Unmanned System Technologies
ISBN 978-981-19-8370-2 ISBN 978-981-19-8371-9 (eBook)
https://doi.org/10.1007/978-981-19-8371-9
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To our families for their love and support
Preface
Aerial swarms are one of the future trends in the development of aeronautics tech-
nology, with the advantages of a wide operating range and the ability to perform
multiple missions. Ensuring the mission completion rate and the safety and stability
of swarm vehicles in complex and dynamic environments requires the support of
navigation technology with high accuracy and reliability.
Although unmanned swarms are receiving increasing attention, both through theo-
retical research and through increasing participation in industrial developments,
the enhancement of navigation by means of effective and efficient collaboration
remains largely unexplored. While my scholarly work has explored various aspects
related to adaptive navigation systems (such as the navigation systems of robots
and ground vehicles, aircraft, aerospace vehicles, and unmanned aerial vehicles),
such as modelling, error characteristics, fusion algorithms, and fault detection and
isolation, the present book presents a specialized investigation of navigation with
resilient characteristics, which allows performance to be maintained through essen-
tial collaboration among members of a swarm in a global navigation satellite system
(GNSS)-challenged environment.
The lack of theories and algorithms related to resilient navigation fusion under
multiplatform collaboration conditions is a bottleneck limiting the development of
self-healing capabilities for unmanned swarm navigation. The traditional methods
of fault-tolerant navigation theory have been proposed mainly to address the perfor-
mance requirements of single-vehicle airborne navigation systems. Usually, the
sources of redundant information for airborne augmentation systems are concentrated
on the carrier platforms, while the sources of redundant information for ground-based
augmentation systems generally consist of stations with fixed known locations. The
sources of redundant information for unmanned swarm navigation consist of the
collection of sensors on each platform scattered among the swarm members, and
the redundancy of the information used is derived from a “crowd-sourced” body
of navigation information obtained through independent and collaborative measure-
ment. The mapping relationship between the navigation performance and observation
error under cooperative conditions differs significantly from that for single-platform
navigation: the observations are spatially distributed in an uncentralized manner,
vii
viii Preface
and the solutions of each platform’s navigation system are coupled and correlated
rather than completely independent. The distributed nature of the redundant infor-
mation sources for aerial swarms represents a significant difference compared to
existing airborne and ground-based navigation augmentation approaches, and there
is a lack of systematic research on quality control models for global swarm navigation
information and optimal reconfiguration algorithms.
This book addresses these issues by proposing that, unlike in traditional airborne
and ground-based navigation augmentation, unmanned swarms can achieve resilient
navigation fusion capabilities through intermember collaboration. In this book,
we focus on resilient navigation fusion technology based on cooperation in the
swarm and present research on six topics, namely collaborative fusion frame-
works, modelling methods for collaboration, collaborative positioning fusion algo-
rithms, collaborative geometry optimization algorithms, collaborative integrity
augmentation algorithms, and collaborative fault-tolerant algorithms, to improve the
comprehensive fault tolerance of swarm vehicle navigation.
The frameworks of and modelling for collaborative resilient navigation fusion are
discussed in this book. The algebraic and geometric fundamentals of collaborative
resilient navigation fusion are introduced; various collaborative navigation structures
of an aerial swarm and information fusion frameworks are introduced in Chap. 2, and
3 addresses the necessary modelling for resilient fusion in collaborative navigation.
Positioning and geometry optimization algorithms for collaborative resilient navi-
gation fusion are also proposed in this book. This part of the book focuses on
improving the navigation accuracy of an aerial swarm in an environment with insuf-
ficient GNSS observations. Positioning algorithms for collaborative resilient naviga-
tion fusion are introduced, including the collaborative localization-based approach in
Chap. 4 and the collaborative observation-based approach in Chap. 5. Furthermore,
geometry optimization algorithms to improve navigation accuracy in resilient fusion
for collaborative navigation are introduced in Chap. 6.
Furthermore, integrity augmentation and fault-tolerant algorithms for collabo-
rative resilient navigation fusion are proposed in this book. This part of the book
focuses on improving the navigation robustness of an aerial swarm in an environment
with insufficient GNSS observations. Optimal collaborative integrity augmentation
methods to improve navigation integrity protection level in collaborative resilient
navigation fusion are introduced in Chap. 7. On this basis, the collaborative fault
detection methods to realize fault identification and exclusion in resilient fusion for
collaborative navigation are introduced in Chap. 8.
This book combines research results on resilient navigation fusion with hot topics
in unmanned swarm development and will serve as a reference for those who are
engaged in research on unmanned swarm collaboration theory, robust positioning,
and navigation augmentation technology. The target audience includes postgraduate
students, scholars, and general readers interested in navigation technologies and
their application in autonomous collaborative swarms. This book is also relevant to
Preface ix
people studying the operation of autonomous collaborative swarms with a focus on
guidance, navigation, and control techniques.
Nanjing, China Rong Wang
October 2022 Zhi Xiong
Jianye Liu
Acknowledgments
This work was partially supported by the National Natural Science Foundation of
China (62073163, 61703208, 61873125), the Natural Science Foundation of Jiangsu
Province (BK20170815), and the Qing Lan Project (2022).
We would like to express our sincere thanks to the National Science Foundation
of China. It has been a great pleasure to work with the colleagues of Springer. The
support and help from Mr. Wayne Hu (the Project Editor) are greatly appreciated.
We would like to acknowledge the contribution of Mr. Junnan Du, Miss Xin Chen,
Miss Huiyuan Zhang, Mr. Li Liu, Mr. Hui He, and Mr. Weicheng Zhao, six students
at the Nanjing University of Aeronautics and Astronautics, who helped conduct the
tasks and preparing parts of the content. Discussion with members of the Naviga-
tion Research Center, College of Automation Engineering, Nanjing University of
Aeronautics and Astronautics, is appreciated.
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