Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation

• URL: http://arxiv.org/abs/2203.11618v1
• Date: Tue, 22 Mar 2022 11:13:36 GMT
• Title: Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation
• Authors: Aalok Patwardhan, Riku Murai and Andrew J. Davison
• Abstract summary: We demonstrate a new purely distributed technique based on a generic factor graph defining dynamics and collision constraints. We show that our method allows extremely high performance collaborative planning in a simulated road traffic scenario.
• Score: 13.65857209749568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise coordinated planning enables safe and highly efficient motion when many robots must work together in tight spaces, but this would normally require centralised control of all devices which is difficult to scale. We demonstrate a new purely distributed technique based on Gaussian Belief Propagation on multi-robot planning problems formulated by a generic factor graph defining dynamics and collision constraints. We show that our method allows extremely high performance collaborative planning in a simulated road traffic scenario, where vehicles are able to cross each other at a busy multi-lane junction while maintaining much higher average speeds than alternative distributed planning techniques. We encourage the reader to view the accompanying video demonstration to this work at https://youtu.be/5d4LXbxgxaY.

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