提出改进的引力场算法，让无人机在复杂环境中自主避障飞行。
Robust UAV Path Planning with Obstacle Avoidance for Emergency Rescue
- 用模拟退火优化引力场，避免局部最优
- 仿真验证算法能高效规划避障路径
- 适合应急救援等复杂环境下的无人机控制
无人机在电子侦察、农业作业和灾后救援等任务中具有高效性。在复杂的三维环境中，实现带障碍物避让的路径规划是保障安全的关键问题。本文构建了一个包含障碍物和禁飞区的综合三维场景，用于动态无人机轨迹生成。同时提出一种结合模拟退火的新型人工势场算法（APF-SA），通过改进吸引与排斥势函数，并利用模拟退火机制跳出局部极小值，最终收敛到全局最优解。仿真结果表明，APF-SA算法在复杂3D环境下具备有效性，可实现无人机的高效自主路径规划与避障。
原文摘要 · Abstract (English)
The unmanned aerial vehicles (UAVs) are efficient tools for diverse tasks such as electronic reconnaissance, agricultural operations and disaster relief. In the complex three-dimensional (3D) environments, the path planning with obstacle avoidance for UAVs is a significant issue for security assurance. In this paper, we construct a comprehensive 3D scenario with obstacles and no-fly zones for dynamic UAV trajectory. Moreover, a novel artificial potential field algorithm coupled with simulated annealing (APF-SA) is proposed to tackle the robust path planning problem. APF-SA modifies the attractive and repulsive potential functions and leverages simulated annealing to escape local minimum and converge to globally optimal solutions. Simulation results demonstrate that the effectiveness of APF-SA, enabling efficient autonomous path planning for UAVs with obstacle avoidance.
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