带有禁飞区约束的物流无人机群路径规划  

Path planning of unmanned logistic aerial vehicle group under the constraint of a no-fly zone

作　　者：苗苗 牛樱清 李晓辉[1] 赵毅 董媛 王萍[1] MIAO Miao;NIU Ying-qing;LI Xiao-hui;ZHAO Yi;DONG Yuan;WANG Ping(School of Electronics and Control Engineering,Chang'an University,Xi'an 710064,China)

机构地区：[1]长安大学电子与控制工程学院,西安710064

出　　处：《兰州大学学报（自然科学版）》2023年第1期98-105,111,共9页Journal of Lanzhou University(Natural Sciences)

基　　金：国家重点研发计划项目(2020YEB1600400);工信部国家物联网重点研发项目(2019ZDLGY03-01)。

摘　　要：探讨带有禁飞区约束条件的物流无人机群送货路径的规划问题,综合考虑禁飞区、无人机载重量、无人机电池容量等约束条件,目标为降低无人机数量及耗电量.基于粒子群算法框架设计了一种将避障功能与路径规划相结合的混合粒子群算法,利用变邻域下降搜索对当前迭代中的全局最优解进行改进,再加入改进的A*算法解决避障问题.通过实际案例分析,验证了该算法的可行性;对16组数据集进行测试,将所得结果与粒子群算法、文化基因算法、迭代局部搜索算法进行对比分析.结果表明,本算法性能优于对比算法,在算法复杂度上的平均目标值结果比粒子群算法提高了3.97%,比文化基因算法提高了2.95%,比迭代局部搜索算法提高了5.13%.The path planning problem of express unmanned aerial vehicle(UAV)swarm was studied in consideration of both restricted conditions and no-fly zones.The constraints of no-fly zones,weight of the UAV and battery capacity of the UAV were all taken in account.The goal was to reduce the number of drones and the power consumption of drones.A hybrid particle swarm algorithm was designed that combined obstacle avoidance and path planning.The algorithm was based on a particle swarm optimization framework,the global optimal solution in the current iteration improved by variable neighborhood descent search,an improved A*algorithm added to solve the obstacle avoidance problem.The comparative study with particle swarm optimization algorithm,cultural gene algorithm and iterative local search algorithm suggested that our algorithm significantly outperformed the others:the average target value results of the algorithm in terms of algorithm complexity were improved by 3.97%against the particle swarm optimization algorithm,2.95%against the cultural gene algorithm and 5.13%against iterative local search algorithm.

关 键 词：物流工程 路径规划 物流无人机群 禁飞区 粒子群算法 变邻域搜索算法 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]