Research on UAV Path Planning Based on Improved A* Algorithm

针对于标准的A*算法在无人机路径中存在搜索效率低，对动态环境的适应性较差及路径不平滑等问题，本文提出两种改进的A*算法。首先提出二维栅格地图三维化建模方法，通过叠加高度信息并引入颜色表征地形高低，简化三维路径规划问题，使环境模型更直观，便于快速建立仿真模型；其次针对标准A*算法启发式函数权重固定的缺陷，引入动态自适应权重系数ω，根据预估代价与阈值的关系动态调整权重比例进行优化，提出自适应权重A*算法以提升路径求解效率；最后引入B样条曲线平滑的方法，对规划路径进行几何优化以改善平滑度。仿真结果表明，改进后的A*算法能有效解决复杂环境下的路径规划问题，在缩短路径长度、改善路径平滑度、提升搜索效率方面表现显著。Aiming at the problems of low search efficiency, poor adaptability to dynamic environments, and uneven paths of the standard A* algorithm in UAV paths, this paper proposes two improved A* algorithm. Firstly, a three-dimensional modeling method for two-dimensional grid maps is proposed. By overlaying height information and introducing color representation of terrain height, the problem of three-dimensional path planning is simplified, making the environment model more intuitive and facilitating the rapid establishment of simulation models; Secondly, in response to the fixed weight of the heuristic function in the standard A* algorithm, a dynamic adaptive weight coefficient ω is introduced to dynamically adjust the weight ratio based on the relationship between the estimated cost and the threshold for optimization. The adaptive weight A* algorithm is proposed to improve the efficiency of path solving; Finally, the B-spline curve smoothing method is introduced to perform geometric optimization on the planned path to improve smoothness. The simulation results show that the improved A* algorithm can effectively solve path planning problems in complex environments, with significant performance in shortening path length, improving path smoothness, and enhancing search efficiency.