Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation

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.

References in corpus (1)

• A visual introduction to Gaussian Belief Propagation

Cited by in corpus (5)

• A Robot Web for Distributed Many-Device Localisation
• Distributed Simultaneous Localisation and Auto-Calibration using Gaussian Belief Propagation
• MR.CAP: Multi-Robot Joint Control and Planning for Object Transport
• AVOCADO: Adaptive Optimal Collision Avoidance driven by Opinion
• Efficient Collaborative Navigation through Perception Fusion for Multi-Robots in Unknown Environments