摘要
针对传统蚁群算法在复杂仓储环境中路径规划时不具备自主避障并且陷入局部最优解从而得不到最优路径的问题进行研究。文章通过构造MAKLINK无向网络图和优化蚁群算法中的启发函数,引入动态光滑因子和全局信息因子,达到控制AGV路径的长度和光滑程度,并构造避障因子使AGV路径在达到全局最优路径的同时拥有避开障碍的能力。通过研究,提出的改进算法可以实现自主避障,并且在最大迭代次数、路径距离和光滑程度方面优于传统蚁群算法。
Aiming at the problem that traditional ant colony algorithm does not have obstacle avoidance in the path planning in complex storage environment and falls into the local optimal solution and can not get the optimal path. By constructing the MAKLINK undirected network graph and the heuristic function in the optimized ant colony algorithm, this paper introduces the dynamic smoothing factor and the global information factor to control the length and smoothness of the AGV path, and constructs the obstacle avoidance factor to make the AGV path reach the global maximum. The excellent path also has the ability to avoid obstacles. Through research, the improved algorithm proposed in this paper can achieve autonomous obstacle avoidance, and is superior to traditional ant colony algorithm in terms of maximum iteration number and optimal path planning.
作者
何成伟
茅健
HE Chengwei;MAO Jian(School of Mechanical and Automotive Engineering, Shanghai University of Engineering and Science, Shanghai 201620, China)
出处
《物流科技》
2019年第3期60-65,共6页
Logistics Sci-Tech
关键词
蚁群算法
启发函数
MAKLINK无向网络图
避障
全局最优光滑路径
Ant colony algorithm
heuristic function
MAKLINK undirected network graph
obstacle avoidance
global optimal smooth path
