Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks
This addresses the problem of scalable multi-robot coordination for tasks like inspection or search and rescue, representing an incremental improvement over existing methods.
The paper tackled the multi-robot coverage problem by training a Graph Neural Network controller to imitate an expert solution, which generalizes to larger maps and teams, such as ten quadrotors and dozens of buildings, and surpasses planning-based approaches in exploration tasks.
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.