[1] 余娜娜, 李铁克, 王柏琳, 等. 自动化分拣仓库中多AGV调度与路径规划算法[J]. 计算机集成制造系统, 2020, 26(1): 171-180 YU Nana, LI Tieke, WANG Bailin, et al. Multi-AGVs scheduling and path planning algorithm in automated sorting warehouse[J]. Computer Integrated Manufacturing Systems, 2020, 26(1): 171-180 [2] 王秀红, 刘雪豪, 王永成. 基于改进A*算法的仓储物流移动机器人任务调度和路径优化研究[J]. 工业工程, 2019, 22(6): 34-39 WANG Xiuhong, LIU Xuehao, WANG Yongcheng. A research on task scheduling and path planning of mobile robot in warehouse logistics based on improved A* algorithm[J]. Industrial Engineering Journal, 2019, 22(6): 34-39 [3] YANG L, FU L, LI P, et al. An effective dynamic path planning approach for mobile robots based on ant colony fusion dynamic windows[J]. Machines, 2022, 10(1): 50 [4] ZHONG X, TIAN J, HU H, et al. Hybrid path planning based on safe A* algorithm and adaptive window approach for mobile robot in large-scale dynamic environment[J]. Journal of Intelligent & Robotic Systems, 2020, 99(1): 65-77 [5] YANG Y, LI Juntao, PENG Lingling. Multi-robot path planning based on a deep reinforcement learning DQN algorithm[J]. CAAI Transactions on Intelligence Technology, 2020, 5(3): 177-183 [6] GUO S, ZHANG X, ZHENg Y, et al. An autonomous path planning model for unmanned ships based on deep reinforcement learning[J]. Sensors, 2020, 20(2): 426 [7] GAO P, LIU Z, WU Z, et al. A global path planning algorithm for robots using reinforcement learning[C/OL]//2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). Dali: IEEE, 2019: 1693-1698 (2019-12-01). https:// doi.org/10.1109/ROBIO49542.2019.8961753 [8] WATKINS C J C H, DAYAN P. Q-learning[J]. Machine Learning, 1992, 8(3): 279-292 [9] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251 ZHOU Feiyan, JIN Linpeng, DONG Jun. Review of convolutional neural network[J]. Chinses Journal of Computers, 2017, 40(6): 1229-1251 [10] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518: 529-533 [11] JIANG L, HUANG H, DING Z. Path planning for intelligent robots based on deep Q-learning with experience replay and heuristic knowledge[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 7(4): 1179-1189 [12] 朱霸坤, 朱卫纲, 李伟, 等. 基于先验知识的多功能雷达智能干扰决策方法[J]. 系统工程与电子技术, 2022, 44(12): 3685-3695 ZHU Bakun, ZHU Weigang, LI Wei, et al. Multi-function radar intelligent jamming decision method based on prior knowledge[J]. Systems Engineering and Electronics, 2022, 44(12): 3685-3695 [13] 邹裕吉, 宋豫川, 王馨坤, 等. 自动导向小车与加工设备多目标集成调度的聚类遗传算法[J]. 中国机械工程, 2022, 33(1): 97-108 ZOU Yuji, SONG Yuchuan, WANG Xinkun, et al. Clustering genetic algorithm for multi-objective integrated scheduling of AGVs and machine[J]. China Mechanical Engineering, 2022, 33(1): 97-108 [14] MATIGNON L, LAURENT G J, LE FORT-PIAT N L. Hysteretic Q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams[C]// 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego: IEEE, 2007: 64-69.
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