Autonomous Mobile Robot Navigation for Complicated Environments by Switching Multiple Control Policies

Abstract

In recent years, many navigation methods using deep reinforcement learning for autonomous mobile robots have been proposed to apply to various dynamic environments. However, since the learning results depend on the simulation environment, it may be inappropriate to apply them directly to the real environments from the standpoint of safety and efficiency. To solve the problem, in this paper, we propose a multi-policy switching method that enables safe and efficient navigation for autonomous mobile robots that share space with humans in various real-world environments, such as dense and crowded situations. Specifically, the method switches the navigation rule among four policies including deep reinforcement learning-based policy according to the size of the target robot’s movable space (i.e., unoccupied area around the robot). We verify the effectiveness of the proposed method by conducting navigation experiments with computer simulation. The results show that the proposed method improves collision avoidance rate in a narrow space where the robot tends to halt or oscillate by existing methods.