基于ISPSO-ACO融合的无人机三维路径规划算法  

UAV 3D Path Planning Algorithm Based on ISPSO-ACO Fusion

作　　者：刘江庭 祝顺康 顾秋逸 李大鹏[2] LIU Jiangting;ZHU Shunkang;GU Qiuyi;LI Dapeng(The 20th Research Institute of CETC,Xi'an 710068,China;School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]中国电子科技集团公司第二十研究所,陕西西安710068 [2]南京邮电大学通信与信息工程学院,江苏南京210023

出　　处：《无线电工程》2025年第4期866-876,共11页Radio Engineering

基　　金：国家自然科学基金(62371245)。

摘　　要：针对复杂环境多约束的三维环境下无人机路径规划问题,首次将球面矢量粒子群(Spherical Vector-based Particle Swarm Optimization,SPSO)算法与蚁群优化(Ant Colony Optimization,ACO)算法相结合,并对前者进行改进,提出了一种融合的无人机三维路径规划算法——改进的SPSO及ACO(Improved SPSO and ACO,ISPSO-ACO)算法。利用Piece Wise混沌映射优化SPSO算法的种群初始化和速度更新,提升初始解的质量和搜索的多样性;设计自适应惯性权重系数与学习因子,平衡算法不同迭代时期全局与局部搜索能力;改进ACO算法信息素初始化策略,利用ISPSO算法预搜索路径作为ACO算法信息素初始值的增量;引入节点伪随机转移策略,保证在搜索不失随机性的同时提高目标的指向性。仿真结果表明,ISPSO-ACO算法在多个维度上超越了其他算法,减少了三维空间搜索的盲目性,并显著提升了搜索效率和路径质量,能够有效地为无人机在不同的三维任务环境中规划出最优路径。For the complex and constrained three-dimensional environment UAV path planning issue,the Spherical Vector-based Particle Swarm Optimization(SPSO) algorithm and Ant Colony Optimization(ACO) algorithm are combined for the first time,the SPSO algorithm is improved and a hybrid UAV three-dimensional path planning algorithm combining SPSO with Ant Colony Hybrid Algorithm—Improved SPSO and ACO(ISPSO-ACO) algorithm is proposed.The Piece Wise chaotic mapping is utilized to optimize the initialization and speed updates of the population in SPSO algorithm,enhancing the quality of initial solutions and the diversity of the search.Adaptive inertia weight coefficients and learning factors are designed to balance the algorithm's global and local search capabilities at different iterative stages.The ACO's pheromone initialization strategy is improved,using pre-searched path from ISPSO as an incremental value for the initial pheromone of ACO.A pseudo-random node transfer strategy is introduced to ensure the search's randomness while enhancing its target-oriented nature.Simulation results demonstrate that the ISPSO-ACO algorithm surpasses other algorithms in multiple dimensions,reducing the blindness of three-dimensional space search,and significantly enhancing search efficiency and path quality,effectively planning optimal paths for UAVs in diverse three-dimensional task environments.

关 键 词：无人机 路径规划 球面矢量粒子群算法 蚁群算法 混合算法 

分 类 号：TN919.23[电子电信—通信与信息系统]