Coordination of control in robot teams using game-theoretic learning

Abstract

This paper presents a distributed decision making approach to the problem of control effort allocation to robotic team members. The objective is for a team of autonomous robots to coordinate their actions in order to efficiently complete a task. A novel controller design methodology is proposed which allows the robot team to work together based on a gametheoretic learning algorithms using fictitious play and extended Kalman filters. In particular each robot of the team predicts the other robots' planned actions while making decision to maximise its own expected reward that is dependent on the reward for joint successful completion of the task. After theoretical analysis the performance of the proposed algorithm is tested on a scenario of collaboration between material handling and patrolling robots in a warehouse.