Formation path learning for cooperative transportation of multiple robots using MADDPG

Abstract

The cooperative transportation using multiple robots has been expected to be used in factories and construction sites. Cooperative transportation can support various situations compared to transportation using one robot. However, the industrial application of cooperative transportation has not been advanced due to the complexity of formation change. In this paper, the formation change using multi-agent deep deterministic policy gradient (MADDPG) is proposed, which is a deep reinforcement learning method specialized for multi -agent systems. The effectiveness of method is evaluated by simulations for multi -agent system.