针对在林场巡检中，无人机航迹规划算法面临难以收敛和容易陷入局部最优解等问题，提出一种引力重构开普勒优化算法的航迹规划方法。通过建立三维环境和航迹代价模型，将航迹规划的复杂问题转化为多个多维函数问题。在开普勒优化算法的行星引力定义时，综合考虑其他行星影响，增加算法空间搜索的多样性；在探索区域，结合Levy飞行策略，提出新的探索区域位置更新方法，以增强局部逃逸能力；在开发利用区域中，结合粒子群优化算法的位置更新策略，将太阳位置作为全局最优值，提出新的开发利用区域位置更新方法，从而提升全局收敛能力。在简单和复杂林场环境模型下进行验证，实验表明，与粒子群算法、鲸鱼优化算法以及开普勒优化算法相比，引力重构开普勒优化算法在复杂的环境具有良好的航迹规划能力和较强的鲁棒性，能够满足大多数林场巡检任务的需求。

To address the challenges in UAV(unmanned aerial vehicle) trajectory planning for forest inspection, such as slow convergence and susceptibility to local optima, a trajectory planning method based on the gravitationally reconfigured Keplerian optimization algorithm was proposed. The complex trajectory planning problem was transformed into multiple multidimensional optimization problems by constructing a three-dimensional environment and a trajectory cost model. In the planetary gravity definition of the Keplerian optimization algorithm, the influence of other planets was comprehensively considered to enhance the spatial search diversity. In the exploration phase, a novel position updating strategy incorporating the Levy flight mechanism was introduced to improve the algorithm's ability to escape local optima. In the exploitation phase, a position updating strategy inspired by the particle swarm optimization algorithm was adopted, where the sun's position was regarded as the global optimum, thereby improving convergence. The proposed method was validated in both simple and complex forest environments. Experimental results show that, compared to the particle swarm optimization, whale optimization algorithm, and standard Keplerian optimization algorithm, the GRKOA exhibits superior trajectory planning performance and robustness in complex environments, effectively satisfying the demands of most forest inspection tasks.