Multi-robot cooperative carrying in dynamic environment

Abstract

In the multi-robot cooperative carrying process, traditional reinforcement learning only uses numerical analysis and ignored reasoning approach. To solve this problem, independence reinforcement learning for multi-robot combines with Belief-Desire-Intention(BDI)model, which makes reinforcement learning link logical reasoning capabilities. And the distance nearest principle is employed which means that the nearest robot ranged from obstacles is the leader robot to control other robots move. Evaluation function which changes with the location of multi-robot and the barriers is proposed, and it combines with the behavior weight based on reinforcement learning which becomes more and more optimized through constantly interacting with the environment. Simulation results show that this method is feasible, and the cooperative carrying process can be successfully achieved.