摘要
在自动化和智能物流领域,多自动引导车(Automated Guided Vehicle,AGV)系统的路径规划是关键技术难题。针对传统深度强化学习方法在多AGV系统应用中的效率、协作竞争和动态环境适应性问题,提出了一种改进的自适应协同深度确定性策略梯度算法Improved-AC-DDPG(Improved-Adaptive Cooperative-Deep Deterministic Policy Gradient)。该算法通过环境数据采集构建状态向量,并实时规划路径,动态生成任务序列以减少AGV间的冲突,同时监测并预测调整避障策略,持续优化策略参数。实验结果表明,与常规DDPG和人工势场优化DDPG(Artificial Potential Field-Deep Deterministic Policy Gradient,APF-DDPG)算法相比,Improved-AC-DDPG在收敛速度、避障能力、路径规划效果和能耗方面均表现更佳,显著提升了多AGV系统的效率与安全性。本研究为多智能体系统在动态环境中的建模与协作提供了新思路,具有重要的理论价值和应用潜力。
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 cooperative 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.
作者
赵学健
叶昊
李豪
孙知信
ZHAO Xuejian;YE Hao;LI Hao;SUN Zhixin(Modern Postal College,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Jiangsu Postal Big Data Technology and Application Engineering Research Center,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;State Post Bureau Postal Industry Technology Research and Development Center(Internet of Things Technology),Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《计算机科学》
北大核心
2025年第6期306-315,共10页
Computer Science
基金
国家自然科学基金(61972208)
中国博士后科学基金(2018M640509)
江苏省研究生科研与实践创新计划项目(SICX23_0303,SJCX24_0339)。
