面向复杂城市环境的无人机动态航迹规划算法
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关键词：蚁群算法；快速探索随机树算法；初始化；信息素挥发因子；B样条曲线；无人机动态航迹规划中图分类号：TP391.9文献标志码：A 文章编号：1001-3695（2025）12-018-3668-07doi：10.19734/j. issn. 1001-3695.2025.04.0112
Dynamic path planning algorithm for UAV in complex urban environment
Xiao Hailinlat，MaRenyulb，Zhang Zhongshan²，Wu Zihaolb，KongXiangting1a （1.a.SchoolpeSeolrifaltelice，ubeiUyWnOna;2hol Electronics，BeijingInstituteofTechnology，Beijing1Ooo81，China）
Abstract：Tosolvetheproblemthattraditionalantcolonyalgorithmexistslowsearcheficiencyandeasytofallintothelocal optimumforcomplex urbanenvironment，this paper proposedadynamic three-dimensional UAVpath planning algorithm.By defining thecostfunctionsforflightangleandflightheight，thealgorithmcouldfurtherestablishtheobjectivefunctionof UAV three-dimensionaltrajectory.Bynon-uniformlyinitializingthepheromoneconcentrationandpresentingapathoptimization mechanism thatadoptedtherapidly-exploring randomtreealgorithm toimprovetheindividual state transitionrules，theproposed algorithmcan avoid being trapped in the local optimum.Then，thealgorithm designedanonlinear dynamicadjusting pheromone volatile factortoenhance thesearch eficiency.Finall，itusedaquasi-uniform B-splinetomake the path more consistentwiththeUAV’sflighttrajectory.Inordertoverifytheefectivenessandsuperiorityoftheproposedalgorithm，this papercariedoutthestrategyefectivenesstestandalgorithmperformance test，andpresentedacomparisonoftheexperimental resultswiththoseofotherintellgentalgorithmsunderdiferentenvironmentalmodels.Numericalresultsshowthat，theaverage convergenceacuracyoftheproposedalgorithmisthesmallestamongallalgorithms，whichindicatesthatthealgorithmhas strongeroptimizationabilityandhighersearchefficiency.Itcanefectivelyavoidstaticanddynamicobstaclesincomplexenvironments，achieving the desired trajectory.
Key words：antcolonyalgorithm（ACO）；rapidly-exploring random treealgorithm（RRT）；initialize；pheromonevolatile factors；B-spline curve；dynamic UAV path planning
0 引言
无人机因其高机动性、易操作性和不受地形环境约束等特点，被广泛用于军事战争、物资运输和信息收集等领域[1，2]。（剩余15107字）
