Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation

TL;DR

This paper introduces GBP Planning, a distributed multi-robot planning method using Gaussian Belief Propagation, enabling scalable, safe, and efficient coordination in complex environments without central control.

Contribution

The paper presents a novel distributed planning algorithm based on Gaussian Belief Propagation, formulated with a factor graph for multi-robot coordination over a forward time window.

Findings

Enables high-performance collaborative planning in complex scenarios

Robots maintain smooth, quick trajectories even with communication failures

Outperforms alternative distributed planning techniques

Abstract

Precise coordinated planning over a forward time window 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 GBP Planning, a new purely distributed technique based on Gaussian Belief Propagation for multi-robot planning problems, formulated by a generic factor graph defining dynamics and collision constraints over a forward time window. In simulations, we show that our method allows high performance collaborative planning where robots are able to cross each other in busy, intricate scenarios. They maintain shorter, quicker and smoother trajectories than alternative distributed planning techniques even in cases of communication failure. We encourage the reader to view the accompanying video demonstration at https://youtu.be/8VSrEUjH610.