基于改进蚁群算法的无人机2维航路规划  

2D Path Planning for UAV Based on Improved Ant Colony Algorithm

作　　者：柳文林 潘子双 赵红超 Liu Wenlin;Pan Zishuang;Zhao Hongchao(College of Aviation Basic Sciences,Naval Aviation University,Yantai 264001,China;College of Artificial Intelligence,Yantai Institute of Technology,Yantai 264005,China)

机构地区：[1]海军航空大学航空基础学院,山东烟台264001 [2]烟台理工学院人工智能学院,山东烟台264005

出　　处：《兵工自动化》2022年第11期68-72,88,共6页Ordnance Industry Automation

基　　金：国家自然科学基金(61174031)。

摘　　要：针对传统蚁群算法在无人机3维航路规划中存在搜索时间长、容易陷入局部最优解的问题,提出一种蚁群算法的改进策略。将固定翼无人机的性能约束条件作为待扩展节点是否可行的判断条件,减小计算量和算法搜索时间;对航路点的高度规划采用直接设定策略,将3维航路规划问题简化为2维航路规划问题,减小算法的复杂性;改进全局信息素更新规则和安全启发因子,解决了局部最优解和威胁源规避问题。仿真结果表明:改进蚁群算法与传统蚁群算法相比,能够有效规划出一条从起点到终点的飞行航路,具有更高的有效性和实用性。Aiming at the problem that the traditional ant colony algorithm has long search time and is easy to fall into local optimal solution in UAV 3D route planning, an improved ant colony algorithm strategy is proposed. The performance constraints of the fixed-wing UAV are used to judge whether the node to be expanded is feasible or not, which reduces the amount of calculation and the search time of the algorithm. The height planning of the waypoint is directly set, which simplifies the 3D route planning problem into a 2D route planning problem and reduces the complexity of the algorithm. The global pheromone update rule and the safety heuristic factor are improved to solve the problems of local optimal solution and threat source evasion. The simulation results show that compared with the traditional ant colony algorithm, the improved ant colony algorithm can effectively plan a flight route from the starting point to the end point, and has higher effectiveness and practicability.

关 键 词：航路规划 改进蚁群算法 信息素 安全启发因子 

分 类 号：V279[航空宇航科学与技术—飞行器设计]