基于改进甲虫搜索算法的城市无人机路径规划  

Urban UAV Path Planning Based on Improved Beetle Search Algorithm

作　　者：杨青青[1,2] 邓敏仪 彭艺[1,2] Yang Qingqing;Deng Minyi;Peng Yi(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Provincial Key Laboratory of Computer Science,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500 [2]昆明理工大学云南省计算机重点实验室,云南昆明650500

出　　处：《系统仿真学报》2023年第12期2527-2536,共10页Journal of System Simulation

基　　金：国家自然科学基金(61761025)。

摘　　要：为提高无人机在城市多障碍物环境下执行任务时的安全性和路径平滑度,并获得最短路径,提出一种改进退火甲虫搜索算法。该算法在探索路径进行位置更新时不再完全依赖于甲虫左右触须的气味浓度差,而是在充分利用甲虫搜索算法较强的搜索能力的基础上,通过引入退火算法增加下一位置的邻域位置解,最终在邻域位置解中筛选得到下一步最佳位置。由退火算法的Metropolis准则对以上得到的最佳位置进行是否可以移动的判断,克服了经典甲虫搜索算法易陷入局部最优解的缺点。仿真结果表明:在城市多障碍物环境下,该算法在收敛速度和生成路径的安全性、平滑度和路径长度方面都优于甲虫搜索算法和蚁群算法。在当前多障碍物城市场景下,当初始步长和步长因子分别为16 m和0.99时,规划的路径最优。An improved SABAS is proposed to improve the safety and path smoothing of UAV missions in urban multi-obstacle environments and to obtain the shortest path.The algorithm no longer completely depends on the difference of odor concentration between the left and the right tentacles of beetle when exploring the path for position update.Instead,it makes full use of the strong searching ability of BAS algorithm,and introduces the annealing algorithm to add the neighborhood position solution of the next position,and finally selects the next best position from the neighborhood position solution.Metropolis criterion of annealing algorithm is used to judge whether the obtained best position is mobile or not,which overcomes the shortcoming of classical BAS being easy to fall into local optimal solution.Simulation results show that SABAS is superior to BAS and ACO convergence speed,safety,smoothness and the length of the path in urban multi-obstacle environment.It can be concluded that the planned path is optimal when the initial step size and step size factor are 16 m and 0.99 respectively in the current multi-obstacle city scenario.

关 键 词：无人机 路径规划 退火甲虫搜索算法 城市环境 最优路径 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]