摘要
针对复杂仓储环境中自动引导车AGV的路径规划问题,提出一种改进型蚁群路径规划算法。首先,通过栅格法建立AGV运行环境,在传统蚁群算法基础上引入方向系数,改进蚁群算法的启发函数,使算法初期在路径选择上具有指向性;其次,加入全局信息素更新机制,以提高算法搜索效率;最后在路径选择过程中引入安全距离判断策略,使AGV在安全距离范围内通过障碍物。仿真结果表明,改进蚁群算法能够快速搜索出最优路径,同时能实现自主避障和避免陷入死锁。
Aiming at the path planning problem of automatic guided vehicle AGV in complex storage environment,an improved ant colony path planning algorithm is proposed.Firstly,the AGV operating environment is established by the grid method.The directional coefficient is introduced on the basis of the traditional ant colony algorithm.The heuristic function of the ant colony algorithm is improved,so that the algorithm has directivity in path selection at the beginning.Secondly,the global pheromone update mechanism is added to Improve the efficiency of algorithm search;in the final path selection process,a safe distance judgment strategy is introduced to enable the AGV to pass obstacles within a safe distance.The simulation results show that the improved ant colony algorithm can quickly search for the optimal path,and can achieve autonomous obstacle avoidance and avoid falling into deadlock.
作者
李任江
滕智鹏
LI Ren-jiang;TENG Zhi-peng(School of Mechatronic Engineering,Changchun University of Technology,Changchun 130012,China)
出处
《机械工程与自动化》
2020年第1期21-23,共3页
Mechanical Engineering & Automation
关键词
蚁群算法
路径规划
栅格法
AGV
ant colony algorithm
path planning
grid method
AGV
