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Distributed formation trajectory planning for multi-vehicle systems
Texas A&M Univ Corpus Christi, Dept Engn, Corpus Christi, TX 78412 USA..
No Arizona Univ, Sch Informat Comp & Cyber Syst, Flagstaff, AZ 86011 USA..
Federat Univ Australia, Sch Engn Informat Technol & Phys Sci, Churchill, Vic 3842, Australia..ORCID iD: 0000-0001-5360-886X
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-9316-233X
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2023 (English)In: 2023 American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 1325-1330Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 1325-1330
Series
Proceedings of the American Control Conference, ISSN 0743-1619, E-ISSN 2378-5861
National Category
Control Engineering Vehicle and Aerospace Engineering
Identifiers
URN: urn:nbn:se:uu:diva-512446DOI: 10.23919/ACC55779.2023.10156635ISI: 001027160301035ISBN: 979-8-3503-2806-6 (electronic)ISBN: 979-8-3503-2807-3 (electronic)ISBN: 978-1-6654-6952-4 (print)OAI: oai:DiVA.org:uu-512446DiVA, id: diva2:1800312
Conference
American Control Conference (ACC), MAY 31-JUN 2, 2023, San Diego, CA, USA
Available from: 2023-09-26 Created: 2023-09-26 Last updated: 2025-02-14Bibliographically approved

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Nguyen, Anh Tung

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