基于ICOA算法的三维无人机路径规划

UAV 3D Path Planning Based on ICOA Algorithm

针对浣熊算法(COA)全局寻优能力不足的问题,提出改进的浣熊算法(ICOA)。通过SPM混沌映射初始化种群,引入莱维飞行和透镜成像反向学习综合的位置更新策略,提高了算法跳出局部最优解和全局搜索能力。采用基准函数测试,结果表明,ICOA算法与COA算法、麻雀搜索算法(SSA)和算术搜索算法(AOA)相比拥有更优的收敛速度和收敛精度。针对各种地形、威胁和约束三维无人机的路径规划问题,构建丘陵地貌的仿真环境模拟无人机执行古建筑群勘探任务的场景,并使用ICOA算法、COA算法、SSA算法和AOA算法进行路径规划。仿真结果表明,ICOA算法规划路径的适应度值更优,路径距离更短且俯仰角度更小,进一步验证了ICOA算法的有效性及其应用于丘陵地区无人机勘探任务的可行性。

To solve the problem of insufficient global searching ability of coati optimization algorithm(COA),this paper proposes an improved coati optimization algorithm(ICOA),which initializes the population through SPM chaotic mapping,introduces a comprehensive position update strategy of Levy flight and lens imaging reverse learning,and improves the ability of jumping out of local optimal solution and global searching.Benchmark function test results show that ICOA has better convergence speed and convergence accuracy than COA,sparrow search algorithm(SSA)and arithmetic search algorithm(AOA).Aiming at the path planning problems of various terrain,threats and constraints of 3D UAVs,the simulation environment of hilly landform is constructed to simulate the scene of UAVs performing exploration tasks of ancient buildings.Meanwhile,ICOA,COA,SSA and AOA are used for path planning.The simulation results show that the fitness value of the path planned by ICOA algorithm is the best,the path distance is shorter and the pitch Angle is smaller,which further verifies the effectiveness of ICOA and the feasibility of its application in UAV exploration missions in hilly areas.