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Multi-Agent Obstacle Avoidance Using Velocity Obstacles and Control Barrier Functions
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-4478-413X
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0009-0001-7232-486X
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-7714-928X
2025 (English)In: 2025 IEEE International Conference on Robotics and Automation, ICRA 2025, Institute of Electrical and Electronics Engineers (IEEE) , 2025, p. 6638-6644Conference paper, Published paper (Refereed)
Abstract [en]

Velocity Obstacles (VO) methods form a paradigm for collision avoidance strategies among moving obstacles and agents. While VO methods perform well in simple multi-agent environments, they do not guarantee safety and can show overly conservative behavior in common situations. In this paper, we propose to combine a VO strategy for guidance with a Control Barrier Function approach for safety, which overcomes the overly conservative behavior of VOs and formally guarantees safety. We validate our method in a baseline comparison study, using second-order integrator and car-like dynamics. Results support that our method outperforms the baselines with respect to path smoothness, collision avoidance, and success rates.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2025. p. 6638-6644
National Category
Robotics and automation
Identifiers
URN: urn:nbn:se:kth:diva-371362DOI: 10.1109/ICRA55743.2025.11127277Scopus ID: 2-s2.0-105016647369OAI: oai:DiVA.org:kth-371362DiVA, id: diva2:2005954
Conference
2025 IEEE International Conference on Robotics and Automation, ICRA 2025, Atlanta, United States of America, May 19 2025 - May 23 2025
Note

Part of ISBN 9798331541392

QC 20251013

Available from: 2025-10-13 Created: 2025-10-13 Last updated: 2025-10-13Bibliographically approved

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Sánchez Roncero, AlejandroCabral Muchacho, Rafael IgnacioÖgren, Petter

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