基站环境下改进A^(*)算法的无人机安全路径规划方法  

Safe Path Planning Method of UAV Based on Improved A^(*)Algorithm for the Base Station Environment

作　　者：屈宏伟 QU Hong-wei(China Tower Corporation Guangdong Branch,Guangzhou 510000,China)

机构地区：[1]中国铁塔股份有限公司广东省分公司,广东广州510000

出　　处：《电脑与电信》2024年第8期45-49,共5页Computer & Telecommunication

摘　　要：铁塔公司基站往往环境复杂、物理障碍繁多,导致传统A^(*)算法实时生成的无人机路径存在接触障碍物次数多、搜索范围过大、路径冗余点过多,从而在基站部署自动无人机库时,影响无人机自主降落安全性。针对此问题,提出一种基站环境下改进A^(*)算法的无人机安全路径规划方法。首先,在A^(*)算法评价函数中引入惩罚项,惩罚项由无人机当前位置至最近障碍物的距离所决定,以此提高全局路径安全性。同时,在A^(*)算法启发式函数中引入动态权重,权重大小可自适应变化,以此提高搜索效率。最后,采用连线检测策略消除路径冗余点得到安全且节能的全局路径。仿真实验表明,本文改进A^(*)算法规划的全局路径在接触障次数、搜索节点等方面相对于传统算法有优化。The base station of the tower company often has complex environment and numerous physical obstacles,resulting in the real-time Unmanned Aerial Vehicle(UAV)path generated by the traditional A^(*)algorithm having multiple times of contact with obstacles,too large search range and too many path redundancy points,thus affecting the autonomous landing safety of UAV when the hangar of UAV is deployed in the base station.Aiming at this problem,this paper proposes a safe path planning method of UAV based on improved A^(*)algorithm for the base station environment.Firstly,a penalty term is introduced into the evaluation function of the A^(*)algorithm to improve the global path safety.The penalty term is determined by the distance from the current position to the nearest obstacle.Meanwhile,the adaptive weight is introduced into the heuristic function of A^(*)algorithm to improve the search efficiency.Finally,the wire detection strategy is adopted to eliminate the path redundancy points to obtain a safe and energy-efficient global path.Simulation experiments show that the global path planned by the improved A^(*)algorithm is optimized in terms of times of contact with obstacles and search nodes.

关 键 词：A^(*)算法 无人机 全局路径 路径规划 启发式函数 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]