Improving Multi-robot Coordination by Game-Theoretic Learning Algorithms

Abstract

Cooperative games-based robot cooperation is analysed for reoccurring scenarios. It is shown that potential games can be used for robot coordination when the robots have a shared objective. By observing each others' behaviour in similar scenarios, they estimate each other's expected actions, which they use for their own choice of action. The resulting learning scheme can enable “tuning” of smooth cooperation by task allocation in teams of robots for various goals and in reoccurring scenarios of their environment. The theoretical results and methods are illustrated in simulation.