Graph Neural Networks for Decentralized Multi-Robot Path Planning

By Qingbiao Li, Fernando Gama, Alejandro Ribeiro, Amanda Prorok. IEEE/RJS International Conference on Intelligent RObots and Systems, 2019

315 citations recorded by the source corpus.

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).

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8284195cf32a24beeff5b1aa262093435dddbdad

Citations in the corpus

• Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning (2020, unknown): “Most recent approaches still show difficulties in scaling to arbitrarily large multi-robot systems [7], as the sizes of the robot state, joint action, and joint observation”