基于改进蚁群算法的越野车辆路径规划研究  

Research on Off-Road Vehicle Path Planning Based on Improved Ant Colony Algorithm

作　　者：宋晓博 高经纬 张朝衍 SONG Xiao-bo;GAO Jing-wei;ZHANG Chao-yan(National University of Defense Technology,Changsha Hunan 410015,China)

机构地区：[1]国防科技大学,湖南长沙410015

出　　处：《计算机仿真》2023年第10期200-204,325,共6页Computer Simulation

摘　　要：目前广泛采用的路径规划算法通常把环境划分为可以通过与不可通过两类。对于越野车辆,其所通行环境的通过性能往往介于两者之间,在进行路径规划时需要将通过性考虑在内。针对传统蚁群算法易陷入停滞、找到的路径并非最优路径的弊端,提出一种考虑通过性的自适应蚁群算法,对信息素分配规则进行了改进,同时通过自适应来调整信息素挥发系数。通过实验和对比证明,改进后的算法能够有效适应复杂环境下的路径规划问题,同时算法的性能和运算效率也得到了明显提升。At present,the widely used path planning algorithms usually divide the environment into two categories:passable and non-passable.For off-road vehicles,the trafficability of their passing environment is often between the two,so trafficability needs to be taken into account in path planning.Aiming at the disadvantages that the traditional ant colony algorithm is easy to fall into stagnation and the path found is not the optimal path,an adaptive ant colony algorithm considering trafficability is proposed in this paper.The pheromone allocation rule is improved,and the pheromone volatilization coefficient is adjusted by adaptation.Experiments and comparisons show that the improved algorithm can effectively adapt to the path planning problem in complex environments,and the performance and computational efficiency of the algorithm have been significantly improved.

关 键 词：蚁群算法 路径规划 通过性 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]