A*蚁群融合的复合启发式路径规划算法
A*and Ant Colony Fusional Composite Heuristic Path Planning Algorithm
陈焱 1陆杰 2李大鹏1
作者信息
- 1. 南京邮电大学通信与信息工程学院,江苏南京 210003
- 2. 中国电科新一代移动通信创新中心,江苏南京 210019
- 折叠
摘要
为提高无人机执行巡飞探查任务的效率,规划出一条最优的飞行路径,针对传统蚁群算法应用于路径规划时存在搜索效率低、迭代次数多、路径拐点多的问题,提出一种A*蚁群融合的复合启发式路径规划算法.利用A*算法预搜索差异化初始信息素,减少蚁群算法前期搜索盲目性,提高搜索效率.构造一种复合启发函数,引入A*算法启发思想改进蚁群原本的启发式信息,设计路径平滑启发函数减少路径拐点.改进信息素更新规则,引入路径综合评分指标,使得每次迭代保留综合性能评分最优的路径.仿真实验表明,改进算法提高了搜索效率,一定程度上解决了拐点过多的问题,综合性能指标优于基本算法与对比文献算法,具有一定工程实践与理论研究意义.
Abstract
In order to improve the efficiency of UAV patrol exploration task and plan an optimal flight path,a compound heuristic A*ant colony path planning algorithm is proposed to solve the problems of low search efficiency,too much iterations and many path in-flection points when ant colony algorithm is applied to UAV path planning.Firstly,A*algorithm is used to pre-search the differentiated initial pheromone to reduce the blindness of the ant colony algorithm.Then a composite heuristic function is constructed,an A*heuristic idea is introduced to construct the ant colony path length heuristic function,and the path smoothness evaluation function is designed.Fi-nally,the pheromone update rule is improved,and the path comprehensive score index is introduced,so that the path with the best com-prehensive performance score is retained as much as possible in each iteration.Simulation experiments show that the improved algorithm solves the problem of too many inflection points to a certain extent,improves search efficiency,and the comprehensive performance index is better than the basic algorithm compared with the literature algorithm,which has certain engineering significance.
关键词
A*算法/蚁群算法/路径规划/启发式Key words
A*algorithm/ant colony algorithm/path planning/heuristic引用本文复制引用
基金项目
国家重点研发计划(2021ZD0140405)
出版年
2024
国家科技期刊平台
NSTL
万方数据