Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection, exploration, 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 in an urban setting. We also demonstrate the GNN controller can surpass planning-based approaches in an exploration task.

Authors

• Ekaterina I. Tolstaya
• James Paulos
• Vijay Kumar 0001
• Alejandro Ribeiro

Keywords

• Buildings
• Inspection
• Search problems
• Routing
• Graph neural networks
• Task analysis
• Intelligent robots
• Multi-robot Coverage
• Spatial Graph Neural Networks
• Discretion
• Nodes In The Graph
• Equivalency
• Larger Team
• Large Mapping
• Coverage Problem
• Exploration Task
• Value Function
• System State
• Free Space
• Receptive Field
• Path Planning
• Size Of Map
• Learning Control
• Open Loop
• Edge Features
• Trajectory Length
• Team Size
• Swarm Robotics
• Use Of Heuristics
• Graph Signal
• Large Receptive Field
• Latent Vector
• Imitation Learning
• Expert Demonstrations
• Types Of Edges
• Aggregation Operators