Graph Neural Networks for Decentralized Multi-Robot Path Planning

TL;DR

This paper introduces a neural network-based approach combining CNNs and GNNs to enable decentralized multi-robot path planning, effectively learning communication and decision policies from expert demonstrations.

Contribution

It presents a novel neural architecture that automatically synthesizes local communication and decision-making policies for multi-robot navigation in constrained environments.

Findings

Achieves success rates close to expert algorithms in simulations

Generalizes well to larger environments and robot teams

Demonstrates effective decentralized planning with local communication

Abstract

Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it is far from obvious what information is crucial to the task at hand, and how and when it must be shared among robots. To side-step these issues and move beyond hand-crafted heuristics, we propose a combined model that automatically synthesizes local communication and decision-making policies for robots navigating in constrained workspaces. Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among robots. We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations. We evaluate our method in simulations {by navigating teams of robots to their destinations in 2D} cluttered workspaces. We measure the success rates and sum of costs over the planned paths. The results show a performance close to that of our expert algorithm, demonstrating the validity of our approach. In particular, we show our model's capability to generalize to previously unseen cases (involving larger environments and larger robot teams).