A recent paper by members of the DCIST alliance designs decentralized mechanisms for coverage control in heterogeneous multi-robot systems especially when considering limited sensing ranges of the robots and complex environments. These are part of the broader DCIST efforts for designing GNN-based control architectures which are, from the ground up, designed to operate in harsh operational conditions, leveraging multi-hop communication to overcome local informational limitations. Our efforts on creating a publication have identified the following salient features of our GNN-controller for multi-robot coverage: (1) We present a model-informed learning solution which leverages relevant (model-based) aspects of the coverage task and propagates it through the network via communication among neighbors in the graph; (2): We use ablation studies explicitly demonstrate that the resulting policies automatically leverage inter-robot communication for improved performance; (3) We show the GNN-based coverage controller outperforms Lloyd’s algorithm under a wide range of training and testing conditions, demonstrating scalability and transferability. 

 

Capability: T1C5 – Joint Resource Allocation in Perception-Action-Communication Loops

Points of Contact: Vijay Kumar (PI) and Walker Gosrich

Paper: https://arxiv.org/abs/2109.15278

Citation: Walker Gosrich, Siddharth Mayya, Rebecca Li, James Paulos, Mark Yim, Alejandro Ribeiro, and Vijay Kumar. “Coverage Control in Multi-Robot Systems via Graph Neural Networks.” arXiv preprint arXiv:2109.15278 (2021)