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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 universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik.ORCID-id: 0000-0001-9316-233X
Vise andre og tillknytning
2023 (engelsk)Inngår i: 2023 American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE), 2023, s. 1325-1330Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023. s. 1325-1330
Serie
Proceedings of the American Control Conference, ISSN 0743-1619, E-ISSN 2378-5861
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-512446DOI: 10.23919/ACC55779.2023.10156635ISI: 001027160301035ISBN: 979-8-3503-2806-6 (digital)ISBN: 979-8-3503-2807-3 (digital)ISBN: 978-1-6654-6952-4 (tryckt)OAI: oai:DiVA.org:uu-512446DiVA, id: diva2:1800312
Konferanse
American Control Conference (ACC), MAY 31-JUN 2, 2023, San Diego, CA, USA
Tilgjengelig fra: 2023-09-26 Laget: 2023-09-26 Sist oppdatert: 2023-09-26bibliografisk kontrollert

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