# Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation

arXiv: https://arxiv.org/abs/2203.11618 · HTML: https://mlsanity.com/paper/2203.11618/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference](https://mlsanity.com/paper/1707.07383/) (2017)
- [FutureMapping 2: Gaussian Belief Propagation for Spatial AI](https://mlsanity.com/paper/1910.14139/) (2019)
- [Bundle Adjustment on a Graph Processor](https://mlsanity.com/paper/2003.03134/) (2020)
- [Intractability of Optimal Multi-Robot Path Planning on Planar Graphs](https://mlsanity.com/paper/1504.02072/) (2015)
- [Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning](https://mlsanity.com/paper/1909.05150/) (2019)
- [A visual introduction to Gaussian Belief Propagation](https://mlsanity.com/paper/2107.02308/) (2021)
- [A Robot Web for Distributed Many-Device Localisation](https://mlsanity.com/paper/2202.03314/) (2022)
- [Incremental Abstraction in Distributed Probabilistic SLAM Graphs](https://mlsanity.com/paper/2109.06241/) (2021)

## Built on by (most cited first, 4 of 4)

- [Multi-Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2410.03072/) (2024)
- [A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing](https://mlsanity.com/paper/2607.16183/) (2026)
- [Learning in Deep Factor Graphs with Gaussian Belief Propagation](https://mlsanity.com/paper/2311.14649/) (2023)
- [Model Predictive Simulation Using Structured Graphical Models and Transformers](https://mlsanity.com/paper/2406.19635/) (2024)

## Abstract (arXiv)

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.