计算机科学 ›› 2026, Vol. 53 ›› Issue (9): 340-350.doi: 10.11896/jsjkx.250600054

• 人工智能 • 上一篇    下一篇

基于多策略改进白鲨优化的无人机路径规划

李洁1, 杜雪勇1, 张潇2   

  1. 1 湖北大学计算机学院 武汉 430062
    2 中南民族大学计算机科学学院 武汉 430074
  • 收稿日期:2025-06-09 修回日期:2025-10-17 出版日期:2026-09-15 发布日期:2026-09-10
  • 通讯作者: 李洁(lijneu@163.com)
  • 基金资助:
    湖北省自然科学基金青年项目(202311901201313)

UAV Path Planning Based on Multi-strategy Improved White Shark Optimizer

LI Jie1, DU Xueyong1, ZHANG Xiao2   

  1. 1 School of Computer,Hubei University,Wuhan 430062,China
    2 College of Computer Science,South-Central Minzu University,Wuhan 430074,China
  • Received:2025-06-09 Revised:2025-10-17 Published:2026-09-15 Online:2026-09-10
  • About author:LI Jie,born in 1988,Ph.D,lecturer,is a member of CCF(No.G3902M).Her main research interests include unmanned aerial vehicle path planning and self-organizing networks.
  • Supported by:
    Youth Project of Hubei Provincial Natural Science Foundation(202311901201313).

摘要: 为了提高无人机在复杂三维环境中的路径规划能力,生成安全、高效且可行的飞行路径,提出了一种多策略改进的白鲨优化算法。利用螺旋搜索策略处理探索和开发的平衡问题,提升搜索效率;结合纵横交叉策略优化解的分布,提高全局搜索能力的同时进一步提升解的精度;引入柯西突变策略来扩展搜索空间,增加种群多样性,避免陷入局部最优。为验证该算法的优化能力和在无人机路径规划问题上的可行性,首先基于CEC2022测试函数进行了基准测试,然后面向三维地形环境和无人机飞行约束进行建模,并通过仿真实验与其他算法进行对比。实验结果表明,该算法表现出较好的寻优能力,得到的最优解适应度均值比其他算法降低了40.35%~68.64%,能够为无人机规划一条更安全高效的路径。

关键词: 无人机, 路径规划, 白鲨优化算法, 螺旋搜索, 纵横交叉, 柯西突变

Abstract: To enhance the path planning capability of Unmanned Aerial Vehicle(UAV) in complex three-dimensional environments,generating safe,efficient,and feasible flight path,a multi-strategy improved white shark optimizer is proposed.A spiral search strategy is employed to address the balance between exploration and exploitation,improving search efficiency.The crisscross strategy is used to optimize solution distribution,improving global search ability while further enhancing solution accuracy.A Cauchy mutation strategy is introduced to expand the search space and enhance population diversity,preventing premature convergence to local optima.To validate the algorithm’s optimization capability and its feasibility in UAV path planning,benchmark tests are first conducted based on the CEC2022 test functions.Subsequently,a three-dimensional terrain environment and UAV flight constraints are modeled,and comparative simulations are performed against other algorithms.Experimental results demonstrate that the proposed algorithm achieves superior optimization performance,reducing the mean fitness value of optimal solutions by 40.35%~68.64% compared to other algorithms.This demonstrates its capability to plan safer and more efficient UAV path.

Key words: UAV, Path planning, White shark optimizer, Spiral search, Crisscross, Cauchy mutation

中图分类号: 

  • TP391.9
[1] LIU X,LI G,YANG H,et al.Agricultural UAV trajectoryplanning by incorporating multi-mechanism improved grey wolf optimization algorithm[J].Expert Systems with Applications,2023,233:120946.
[2] HU G,ZHONG J,WEI G.SaCHBA_PDN:Modified honeybadger algorithm with multi-strategy for UAV path planning[J].Expert Systems with Applications,2023,223:119941.
[3] ZHANG C,ZHOU W,QIN W,et al.A novel UAV path planning approach:Heuristic crossing search and rescue optimization algorithm[J].Expert Systems with Applications,2023,215:119243.
[4] LI J,ZHANG W,REN J,et al.A multi-area task path-planning algorithm for agricultural drones based on improved double deep Q-learning net[J].Agriculture,2024,14(8):1294.
[5] LIU N,HU Z H,WEI M,et al.Improved A* algorithm incorporating RRT* thought:A path planning algorithm for AGV in digitalised workshops[J].Computers & Operations Research,2025,177:106993.
[6] GUO J,XIA W,HU X,et al.Feedback RRT* algorithm for UAV path planning in a hostile environment[J].Computers & Industrial Engineering,2022,174:108771.
[7] HUSAIN Z,AL ZAABI A,HILDMANN H,et al.Search and rescue in a maze-like environment with ant and dijkstra algorithms[J].Drones,2022,6(10):273.
[8] HAO K,ZHAO J L,LI Z S,et al.Dynamic path planning of a three-dimensional underwater AUV based on an adaptive genetic algorithm[J].Ocean Engineering,2022,263:112421.
[9] YUAN M,CHEN M.Improved lazy theta* algorithm based on octree map for path planning of UAV[J].Defence Technology,2023,23:8-18.
[10] YE L,WU F,ZOU X,et al.Path planning for mobile robots in unstructured orchard environments:An improved kinematically constrained bi-directional RRT approach[J].Computers and Electronics in Agriculture,2023,215:108453.
[11] WU T,ZHANG Z,JING F,et al.A dynamic path planningmethod for UAVs based on improved informed-RRT* fused dynamic windows[J].Drones,2024,8(10):539.
[12] GU Y,ZHU Z,LV J,et al.DM-DQN:Dueling Munchausen deep Q network for robot path planning[J].Complex & intelligent systems,2023,9(4):4287-4300.
[13] WU J,SUN Y,LI D,et al.An adaptive conversion speed Q-learning algorithm for search and rescue UAV path planning in unknown environments[J].IEEE Transactions on Vehicular Technology,2023,72(12):15391-15404.
[14] WANG W,ZHANG G,DA Q,et al.Multiple unmanned aerial vehicle autonomous path planning algorithm based on whale-inspired deep Q-network[J].Drones,2023,7(9):572.
[15] HU M,CAO S H,WANG Y,et al.3D Obstacle Avoidance Path Planning Scheme for UAVs Based on Tree Sampling Dueling-DQN[J].Journal of Chinese Computer Systems.2025,46(7):1616-1624
[16] TANG J,LIANG Y,LI K.Dynamic scene path planning of UAVs based on deep reinforcement learning[J].Drones,2024,8(2):60.
[17] SONNY A,YEDURI S R,CENKERAMADDI L R.Q-learning-based unmanned aerial vehicle path planning with dynamic obstacle avoidance[J].Applied Soft Computing,2023,147:110773.
[18] ZHANG M,HAN Y,CHEN S,et al.A multi-strategy improved differential evolution algorithm for UAV 3D trajectory planning in complex mountainous environments[J].Engineering Applications of Artificial Intelligence,2023,125:106672.
[19] YU Z,SI Z,LI X,et al.A novel hybrid particle swarm optimization algorithm for path planning of UAVs[J].IEEE Internet of Things Journal,2022,9(22):22547-22558.
[20] YU X,JIANG N,WANG X,et al.A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning[J].Expert Systems with Applications,2023,215:119327.
[21] JIANG W,LYU Y,LI Y,et al.UAV path planning and collision avoidance in 3D environments based on POMPD and improved grey wolf optimizer[J].Aerospace Science and Technology,2022,121:107314.
[22] BRAIK M,HAMMOURI A,ATWAN J,et al.White Shark Optimizer:A novel bio-inspired meta-heuristic algorithm for global optimization problems[J].Knowledge-Based Systems,2022,243:108457.
[23] MCKINLEY S,LEVINE M.Cubic spline interpolation[J].College of the Redwoods,1998,45(1):1049-1060.
[24] DENG W,FENG J,ZHAO H.Autonomous path planning viasand cat swarm optimization with multi-strategy mechanism for unmanned aerial vehicles in dynamic environment[J].IEEE Internet of Things Journal,2025,12(14):26003-26013.
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