Скачать или смотреть [IROS 2020] Graph Neural Networks for Decentralized Multi-Robot Path Planning

Paper published at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Abstract:
Effective communication is key to successful, decentralized, multirobot 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 handcrafted 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).

Paper link: https://arxiv.org/pdf/1912.06095.pdf