Title
Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics
Author
Bakker, S. (TU Delft Learning & Autonomous Control)
Knödler, L. (TU Delft Learning & Autonomous Control)
Spahn, M. (TU Delft Learning & Autonomous Control)
Böhmer, J.W. (TU Delft Algorithmics)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control)
Date
2024
Abstract
In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high-success rates and real-time performance. Code, video: https://github.com/tud-amr/multi-robot-fabrics
To reference this document use:
http://resolver.tudelft.nl/uuid:f9966be1-6a47-48e5-9f4e-14f3160496a0
DOI
https://doi.org/10.1109/MRS60187.2023.10416784
Publisher
IEEE
Embargo date
2024-08-05
ISBN
979-8-3503-7076-8
Source
Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems (MRS)
Event
International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2023-12-04 → 2023-12-05, Boston, United States
Series
2023 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2023
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2024 S. Bakker, L. Knödler, M. Spahn, J.W. Böhmer, J. Alonso-Mora