Learning based multi-robot coverage algorithm

Abstract

Multi-robot coverage algorithm is essential in exploration, search and rescue, tracking and other tasks. Nowadays, global planning-based approaches are difficult to solve the actual deployments of very large robot team coverage problems. In this article we use the heuristic algorithm based on graph neural networks to solve the multi robot coverage algorithm. Firstly, we discretize the coverage task and encode it into a graph. The location of graph and the robots are nodes. Then we design a graph neural network controller and use imitation methods to train the controller. The controller will generate the solution that is not inferior to the expert through imitating an open-loop expert solution based on VPR. Finally, we designed a graph neural network architecture to perform zero shot generalization on large maps and teams, enabling the system to be extended to larger map teams. It is difficult for the expert. And we successfully use this model to simulate 10 quadcopter and a number of buildings in a city. We also prove the GNN controller is better than the method based on the planning in the exploration task.