A Hybrid Path Planning Framework Integrating Deep Reinforcement Learning and Variable-Direction Potential Fields

To address the local optimality in path planning for logistics robots using APF (artificial potential field) and the stagnation problem when encountering trap obstacles, this paper proposes VDPF (variable-direction potential field) combined with RL (reinforcement learning) to effectively solve these problems. First, based on obstacle distribution, an obstacle classification algorithm is designed, enabling the robot to select appropriate obstacle avoidance strategies according to obstacle types. Second, the attractive force and repulsive force in APF are separated, and the direction of the repulsive force is modified to break the local optimum, allowing the robot to focus on handling current obstacle avoidance tasks. Finally, the improved APF is integrated with the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm, and a weight factor is introduced to adjust the robot’s acting forces. By sacrificing a certain level of safety for a larger exploration space, the robot is guided to escape from local optima and trap regions. Experimental results show that the improved algorithm effectively mitigates the trajectory oscillation of the robot and can efficiently solve the problems of local optimum and trap obstacles in the APF method. Compared with the algorithm APF-TD3 in scenarios with five obstacles, the proposed algorithm reduces the GS (Global Safety) by 8.6% and shortens the length by 8.3%. In 10 obstacle scenarios, the proposed algorithm reduces the GS by 29.8% and shortens the length by 9.7%.