Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics

Conference paper (2024)

Authors

S. Bakker Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
L. Knödler Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
M. Spahn Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
J.W. Böhmer Algorithmics - EEMCS
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
More Info
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Published Date

2024

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

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