摘要
仓储物流机器人路径规划问题是智能仓储运作的关键问题,解决该问题的经典算法之一是蚁群算法.针对基本蚁群算法收敛速度慢、算法不稳定等问题,提出一种改进蚁群算法.算法增加启发函数的重要性,对转移规律采用确定性选择和随机选择相结合的方式,信息素更新规则根据迭代次数进行动态适应性改变,得到路径规划结果后,再进行平滑处理.仿真实验结果表明:改进蚁群算法有效缩短仓储物流机器人路径长度,且更为稳定,有较强适应性.
Path planning of warehouse logistics robot is the key problem of intelligent warehouse operation.One of the classic algorithms to solve this problem is ant colony algorithm.Aiming at the problems of slow convergence and unstable algorithm of basic ant colony algorithm,an improved ant colony algorithm was proposed.The algorithm increases the importance of heuristic function,and combines deterministic selection with random selection for the transfer rule.The pheromone update rule changes dynamically and adaptively according to the iteration times,and then smoothes the path planning result.Simulation results show that the improved ant colony algorithm can effectively shorten the path length of warehouse logistics robot,and it is more stable and adaptable.
作者
辜勇
段晶晶
苏宇霞
袁源乙
GU Yong;DUAN Jingjing;SU Yuxia;YUAN Yuanyi(School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China)
出处
《武汉理工大学学报（交通科学与工程版）》
2020年第4期688-693,697,共7页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
海洋环境安全保障重点专项资助(2018YFC1407405)。
关键词
蚁群算法
栅格化
仓储物流机器人
路径规划
平滑处理
ant colony algorithm
gridding
warehouse logistics robot
path planning
smoothing
