Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics

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)

2024

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

http://resolver.tudelft.nl/uuid:f9966be1-6a47-48e5-9f4e-14f3160496a0

https://doi.org/10.1109/MRS60187.2023.10416784

IEEE

2024-08-05

979-8-3503-7076-8

Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems (MRS)

International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2023-12-04 → 2023-12-05, Boston, United States

2023 International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2023

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.

Institutional Repository

conference paper

© 2024 S. Bakker, L. Knödler, M. Spahn, J.W. Böhmer, J. Alonso-Mora