Autonomous Mobile Robot Navigation by Reinforcement Learning Considering Pedestrian Movement Tendencies

In this paper, we propose a path planning method using reinforcement learning for addressing the navigational issues of an autonomous mobile robot that shares space with a person and travels to its destination safely and efficiently. Here, we generate a path that does not interfere with the pedestrian’s path by incorporating the relative positions of the robot and the pedestrian, as well as the moving tendencies of the target pedestrian (velocity and direction) into the learning state. Simulation results showed that, compared to existing methods (e.g., artificial potential methods), the proposed method does not block the pedestrian’s path and takes a safe route to avoid collisions, although the duration to reach the goal was longer.