基于运动预测与改进APF的无人机路径规划方法

动态路径规划是保障无人机在复杂干扰环境下飞行安全的关键要素。针对动态路径规划中存在的迭代次数高、收敛速度慢以及动态障碍物避障问题，提出了一种基于障碍物运动预测与改进APF的无人机动态路径规划方法。首先，针对动态障碍物，设计了基于激光雷达的目标检测算法和基于卡尔曼滤波的运动预测算法估计动态障碍物信息，并提出速度方向相似性检测方法进行局部位置脱离决策。其次，针对静态障碍物，引入模拟退火法扰动当前状态，并结合基于目标点的邻域优化函数进行动态路径规划。仿真结果表明，本文算法在应对静态障碍物时可缩短69%动态避障时间，应对动态障碍物时可缩短了19.7%的避障距离与23.6%的任务耗时，提高了无人机任务执行的安全性和效率。

Unmanned aerial vehicle path planning method based on motion prediction and enhanced APF

Dynamic path planning is a critical factor in ensuring the safe flight of unmanned aerial vehicles (UAVs) in complex interference environments. To address the issues of high iteration counts, slow convergence, and dynamic obstacle avoidance in dynamic path planning, this paper proposes a UAV dynamic path planning method based on obstacle motion prediction and improved artificial potential fields (APF). First, for dynamic obstacles, a target detection algorithm based on laser radar and a motion prediction algorithm based on Kalman filtering are designed to estimate dynamic obstacle information. A velocity direction similarity detection method is introduced for local position evasion decisions. Secondly, for static obstacles, a simulated annealing algorithm is introduced to perturb the current state, coupled with a neighborhood optimization function based on target points for dynamic path planning. Simulation results show that the proposed algorithm reduces dynamic obstacle avoidance time by 69% when dealing with static obstacles and reduces obstacle avoidance distance by 19.7% and task duration by 23.6% when dealing with dynamic obstacles, thereby enhancing the safety and efficiency of UAV mission execution.