基于人工势场法的无人机路径规划避障算法

随着无人机广泛应用于生产生活的各个方面,无人机的避障研究成为热点问题。为了提高无人机的避障性能,文中提出一种基于人工势场法的无人机路径规划避障算法。该算法通过生成预规划路径弱化了目标点对无人机的吸引作用,增加了路径的连贯性;在势场函数中加入了动态调节因子,可减少无人机轨迹不必要的转弯机动,减少机动能耗;该算法综合考虑无人机飞行中的安全性、平滑性和机动能耗,提出了一种新的代价函数,并通过使得代价函数最小化来选出最优路径。实验结果表明,该算法克服了传统人工势场的不足,在不同的飞行环境下均能够规划出安全、平滑、机动能耗小的路径,有效避开障碍物,且具有较好的适应性。

UAV Path Planning Obstacle Avoidance Algorithm Based on Artificial Potential Field Method

With the widespread use of drones in all aspects of production and life, the study of the algorithms of obstacle avoidance of drones has become a hot issue.In order to improved the obstacle avoidance performance of UAV, this paper proposed an obstacle avoidance algorithm for UAV path-planning based on the artificial potential field method.The algorithm decreased the attraction of the target point to the drone by generating the pre-planning path, and extends the continuity of the path. The dynamic adjustment factor was added to the potential field function to reduce the unnecessary turning maneuvers of the drone trajectory thus reducing the consumption of Mobile energy;The algorithm considered the safety, smoothness and maneuvering energy consumption of UAVs in flight. A new cost function is proposed and the optimal path was selected by minimizing the cost function.The experimental results showed that the algorithm overcomes the shortcomings of the traditional artificial potential field, and can plan safe, smooth, and small mobile energy consumption paths in different flight environments, effectively avoiding the obstacles with better adaptability.