Graph Neural Networks for Decentralized Multi-Robot Path Planning
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Publication Date
2020-10-24Journal Title
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Conference Name
IEEE/RSJ International Conference on Intelligent Robots and Systems
ISSN
2153-0858
ISBN
978-1-7281-6212-6
Publisher
IEEE
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Li, Q., Gama, F., Ribeiro, A., & Prorok, A. (2020). Graph Neural Networks for Decentralized Multi-Robot Path Planning. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) https://doi.org/10.1109/IROS45743.2020.9341668
Abstract
Effective communication is key to successful, de- centralized, 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).
Sponsorship
We gratefully acknowledge the support of ARL grant DCIST CRA W911NF-17-2-0181. A. Prorok was supported by the Engineering and Physical Sciences Research Council (grant EP/S015493/1). We gratefully acknowledge their support.
Funder references
Engineering and Physical Sciences Research Council (EP/S015493/1)
Identifiers
External DOI: https://doi.org/10.1109/IROS45743.2020.9341668
This record's URL: https://www.repository.cam.ac.uk/handle/1810/308037
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