Decentralized, Unlabeled Multi-Agent Navigation in Obstacle-Rich Environments using Graph Neural Networks

We propose a decentralized, learning-based solution to the challenging problem of unlabeled multi-agent navigation among obstacles, where robots need to simultaneously tackle the problems of goal assignment, local collision avoidance, and navigation. Our method has each robot infer their desired action by communicating with each other as well as a set of position-fixed routers. The inference is carried out on a graph neural network (GNN) with both robot and router nodes. We train our GNN using imitation learning on a small group of robots, where we modify the centralized version of the concurrent goal assignment and planning algorithm (CAPT) as our expert. By sharing weights among all robots and routers, our model can scale to unseen environments with any number of possibly kinodynamic agents during test time. We have achieved a success rate of 91.2% and 85.6% for point and car-like robots, respectively. Source code will be publicly available upon the publication of the work.