Coulomb force-guided deep reinforcement learning for effective and explainable robotic motion planning

Training mobile robots through digital twins with deep reinforcement learning (DRL) has gained increasing attention to ensure efficient and safe navigation in complex environments. In this paper, we propose a novel physics-inspired DRL framework that achieves both effective and explainable motion planning. We represent the robot, destination, and obstacles as electrical charges and model their interactions using Coulomb forces. These forces are incorporated into the reward function, providing both attractive and repulsive signals to guide robot behavior. In addition, obstacle boundaries extracted from LiDAR segmentation are integrated as anticipatory rewards, allowing the robot to avoid collisions from a distance. The proposed model is first trained in Gazebo simulation environments and subsequently deployed on a real TurtleBot v3 robot. Extensive experiments in both simulation and real-world scenarios demonstrate the effectiveness of the proposed framework. Results show that our method significantly reduces collisions, maintains safe distances from obstacles, and generates safer trajectories toward the destinations.