Douthwaite, J.A. orcid.org/0000-0002-7149-0372, Zhao, S. and Mihaylova, L.S. orcid.org/0000-0001-5856-2223 (2018) A comparative study of velocity obstacle approaches for multi-agent systems. In: Proceedings of 2018 UKACC 12th International Conference on Control (CONTROL). Control 2018: The 12th International UKACC Conference on Control, 05-07 Sep 2018, Sheffield, UK. IEEE , pp. 289-294. ISBN 978-1-5386-2864-5
Abstract
This paper presents a critical analysis of some of the most promising approaches aimed at geometrically generating reactive avoidance trajectories for multi-agent systems. Several evaluation scenarios are proposed that include both sensor uncertainty and increasing difficulty. An intensive 1000 cycle Monte Carlo analysis is used to assess the performance of the selected algorithms under the presented conditions. The Optimal Reciprocal Collision Avoidance (ORCA) method was shown to demonstrate the most scalable computation times and collision likelihood in the presented scenarios. The respective features and limitations of the algorithms are discussed and presented through examples.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Collision avoidance; multi-agent systems; velocity obstacles; VO; RVO; HRVO; OCRA |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Sep 2018 09:57 |
Last Modified: | 27 Jul 2020 08:30 |
Published Version: | https://doi.org/10.1109/CONTROL.2018.8516848 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/CONTROL.2018.8516848 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135915 |