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.
@article{arxiv.2310.12816,
title = {Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics},
author = {Saray Bakker and Luzia Knoedler and Max Spahn and Wendelin Böhmer and Javier Alonso-Mora},
journal= {arXiv preprint arXiv:2310.12816},
year = {2023}
}
Comments
6 pages + 1 page references, 2 tables, 4 figures, preprint version to accepted paper to IEEE International Symposium on Multi-Robot & Multi-Agent Systems, Boston, 2023