针对在复杂障碍物环境下的低空无人机路径规划中的局部最优或目标不可达等问题，提出一种基于改进人工势场法的低空无人机动态路径规划算法。首先，优化人工势场法斥力势场函数，利用障碍物距离动态调整斥力强度，解决路径规划目标点不可达问题。然后，改进引力势场函数，对引力权重进行动态分配，规避陷入局部最优问题。最后，提出合力向量平滑优化策略，增强路径规划的稳定性与动态适应性。实验结果表明，相比标准人工势场算法，提出的改进人工势场法在复杂障碍物环境下解决了路径规划局部最优和目标不可达问题，研究结果对低空飞行器动态路径规划具有较好的借鉴意义。

Aiming at the problems of local optimum and target unreachability existing in low-altitude UAV path planning under complex obstacle environments，an improved artificial potential field （APF） algorithm was proposed for dynamic path planning of low-altitude UAVs. The proposed algorithm featured three key enhancements： first， the repulsive potential field function was optimized through dynamic adjustment of repulsive intensity based on obstacle distances， effectively resolving target inaccessibility. Second， the gravitational potential field function was refined with dynamic weight allocation to prevent local optima entrapment. Finally， a smooth optimization strategy for resultant vectors was introduced to enhance planning stability and dynamic adaptability. Experimental results demonstrate that compared with the standard APF algorithm， the improved method successfully overcomes both local optima and target inaccessibility issues in complex obstacle environments. This research provides valuable insights for dynamic path planning of low-altitude aerial vehicles.