The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Network controller that leverages the spatial equivariance of the task to imitate an expert open-loop routing solution. This approach generalizes well to much larger maps and larger teams that are intractable for the expert. In particular, the model generalizes effectively to a simulation of ten quadrotors and dozens of buildings. We also demonstrate the GNN controller can surpass planning-based approaches in an exploration task.
@article{arxiv.2011.01119,
title = {Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks},
author = {Ekaterina Tolstaya and James Paulos and Vijay Kumar and Alejandro Ribeiro},
journal= {arXiv preprint arXiv:2011.01119},
year = {2021}
}