Real-time distributed non-myopic task selection for heterogeneous robotic teams

In this paper we introduce a novel algorithm for online distributed non-myopic task-selection in heterogeneous robotic teams. Our algorithm uses a temporal probabilistic representation that allows agents to evaluate their actions in the team's joint action space while robots individually search their own action space. We use Monte-Carlo tree search to asymmetrically search through the robot's individual action space while accounting for the probable future actions of their team members using the condensed temporal representation. This allows a distributed team of robots to non-myopically coordinate their actions in real-time. Our developed method can be applied across a wide range of tasks, robot team compositions, and reward functions. To evaluate our coordination method, we implemented it for a series of simulated and fielded hardware trials where we found that our coordination method is able to increase the cumulative team reward by a maximum of 47.2% in the simulated trials versus a distributed auction-based coordination. We also performed several outdoor hardware trials with a team of three quadcopters that increased the maximum cumulative reward by 24.5% versus a distributed auction-based coordination.