Multi-AGV Path Planning Algorithm Based on Improved DDPG

In the field of intelligent logistics,the challenge of path planning and obstacle avoidance for automated guided vehicles(AGVs) is significant.Traditional deep reinforcement learning(DRL) methods exhibit limitations in efficiency,dynamic adaptability,and handling competitive-cooperative interactions among multiple AGVs.This paper presents the improved adaptive co-operative deep deterministic policy gradient(Improved-AC-DDPG) algorithm,an advancement over the standard DDPG.It leverages environmental data to construct state vectors and employs a real-time path planning strategy that dynamically creates task sequences to prevent AGV conflicts.This algorithm also includes continuous policy parameter optimization for obstacle avoidance.Experiments show that the Improved-AC-DDPG surpasses both the standard DDPG and the artificial potential field optimization DDPG(APF-DDPG) in convergence speed,obstacle avoidance,path planning,and energy efficiency,thus enhancing multi-AGV system performance.This study provides innovative insights and solutions for multi-agent system modeling and collaboration in dynamic environments,with substantial theoretical and practical implications.