改进蚁群算法的无人机路径规划

Unmanned Aerial Vehicle Path Planning with Improved Ant Colony Algorithm

摘要: 路径规划是无人机研究领域的重要课题之一。针对蚁群算法在优化路径规划问题出现算法运行效率较低，易陷入局部最优的问题。提出了基于对数正太分布函数改进蚁群算法的信息素蒸发因子，然后改进信息素强度和引入轮盘赌算法。并对其进行计算机仿真和结果分析。结果表明：该算法能够保证无人机在最短的距离到达终点，且减少了算法的迭代次数，验证了算法的可行性和高效性。

Abstract: Path planning is one of important topics in the field unmanned aerial vehicle (UAV) of study. In view of the ant colony algorithm (ACO) in the optimization of path planning problem that ACO has low algorithm efficiency and is easily trapped into local optimal optimization in the optimization of path planning, path planning puts forward the pheromone evaporation factor that improves Ant Colony Algorithm based on the normal distribution function, and then improves the pheromone intensity and introduces roulette algorithm. And it focuses on the computer simulation and the result analysis of the ACO. The results show that the algorithm can guarantee the UAV to reach the destination in the shortest distance, and reduce the iteration times of the algorithm which verifies the feasibility and efficiency of the algorithm.