基于改进人工鱼群算法的复杂地貌无人机三维路径规划

针对无人机在复杂海域地貌中的三维路径规划，在人工鱼群算法的基础上提出了一种改进的适应性人工鱼群算法。首先，利用数学模型建立地貌的三维模型，选取路径最短为性能评价函数，保证路径规划的合理性；其次，考虑到传统的人工鱼群算法前期收敛速度慢，后期需要精确搜索提高算法精度，提出自适应步长和自适应视野范围来更新个体的位置。为了避免算法陷入局部最优，在追尾行为中引入鱼群中的社会经验位置进行更新；最后，利用MATLAB对在3个复杂程度不同的地图中与传统的人工鱼群算法与粒子群算法对比，仿真结果表明改进后的人工鱼群算法在三维路径规划问题求解中具有更好的收敛速度和精度。

3D Path Planning for UAV in Complex Landscape Based on Improved Artificial Fish Swarm Algorithm

An improved adaptive artificial fish swarm algorithm is proposed based on the artificial fish swarm algorithm for 3D path planning of UAV (Unmanned Aerial Vehicle) in complex sea landscapes. Firstly, a 3D model of the landscape is created using the mathematical formula, and the shortest path is selected as the performance evaluation function to ensure the rationality of the path planning. Secondly, considering the slow convergence speed of the traditional artificial fish swarm algorithm in the early stage and the need for precise search to improve the algorithm accuracy in the later stage, adaptive step size and adaptive field of view range are proposed to update the position of individuals. In order to avoid the algorithm falling into local optimum, the social experience position in the fish school is introduced to update the following behavior. Finally, MATLAB is used to compare with the particle swarm optimization algorithm in several maps, and the simulation results show that the improved artificial fish swarm algorithm has better convergence speed and accuracy in 3D path planning.