摘要
针对蚁群算法存在的收敛速度慢、易陷入局部最优和容易死锁等问题,提出了一种用于自动引导车(AGV,automated guided vehicle)路径规划的双种群蚁群算法;该算法引入差异化信息素初始值,修改启发函数并在信息素更新时对最优及最差路径进行奖惩;以改进策略为基础,引入自适应步长搜索策略,通过具有差异化步长的两个种群相互协作加强算法寻优能力和搜索效率;针对死锁问题,提出了将符合条件的单元格视为障碍物的“填充陷阱”策略;分别进行仿真实验和车间现场实验,结果表明,该算法可以为AGV规划出一条安全且综合性能较好的路径,为AGV路径规划提供了一种可行的方案。
Aiming at the problems of slow convergence,easily falling into local optimum and deadlock in ant colony algorithm,a dual-population ant colony algorithm for the path planning of automatic guided vehicle(AGV)is proposed.The algorithm introduces the initial values of different pheromones,modifies the heuristic function,rewards the best paths,and punishes the worst paths when the pheromone is updated;Based on the improved strategy,an adaptive step-size search strategy is introduced,and the optimization ability and search efficiency of the algorithm are strengthened by the cooperation of two populations with different step sizes;To solve the deadlock problem,a filling trap strategy of is proposed,in which the qualified cells are regarded as obstacles.The simulation and field experiments are carried out respectively.The results show that the algorithm can plan a safe and comprehensive path for the AGV,and it provides a feasible scheme for the AGV path planning.
作者
刘睿
杨程伟
高长水
李晓东
LIU Rui;YANG Chengwei;GAO Changshui;LI Xiaodong(College of Mechanical&Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Jiangyin Huilong Electric Heating Appliance Co.,Ltd.,Wuxi 214401,China)
出处
《计算机测量与控制》
2023年第5期193-199,206,共8页
Computer Measurement &Control
关键词
AGV
路径规划
蚁群算法
自适应步长
双种群
死锁避免
AGV
path planning
ant colony algorithm
adaptive step size
dual population
deadlock avoidance
