Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics
S. Bakker (TU Delft - Learning & Autonomous Control)
Luzia Knödler (TU Delft - Learning & Autonomous Control)
M. Spahn (TU Delft - Learning & Autonomous Control)
Wendelin Böhmer (TU Delft - Algorithmics)
Javier Alonso-Mora (TU Delft - Learning & Autonomous Control)
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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