基于随机树势场函数的复杂障碍物逃逸航路规划方法

A Complex Obstacle Escape Path Planning Method Based on Random Tree Potential Field Function

无人机在执行任务过程中需根据外界环境规划出满足约束条件的飞行路径。针对传统人工势场法(APF)规划的航路存在局部极小值、无法脱困和狭窄航路震荡等缺陷导致航路规划失败的问题,提出了一种基于随机树势场的复杂障碍物逃逸航路规划方法。首先,建立了无人机离散化环境地图模型,采用快速随机搜索树(RRT)的方式分别在各个环境地图中生成随机树路径,并设计了一种随机树势场函数,对路径节点构建势场;然后,通过合理的设计随机树势场的参数与引入时机,引导无人机逃逸出缺陷情况下的势场环境,解决了传统人工势场法的缺陷问题;最后,将文中算法与对比算法进行仿真和分析。结果显示,相比快速搜索随机树算法与传统人工势场法存在航路规划失败的情况,文中算法在各个障碍物情况下都完成了航路规划,并缩短了规划的航路长度,进一步提升了航路规划的安全性和有效性。

A drone needs to plan flight paths to the satisfaction of constraints according to the external environment in the process of mission execution.Aimed at the problems that local minimum is in existence in the traditional artificial potential field(APF)planning paths,and the failure of path planning is unable to get out of the trap,narrow path oscillation and other defects,a complex obstacle escape path planning method is proposed based on random tree potential field(RTPF).First,a discrete drone environment map model is established to generate a random tree path in each environment map by using rapidly exploring random trees(RRT),and a random tree potential field function is designed to construct the potential field for the path nodes.And then,by reasonably designing the parameters and introduction timing of random tree potential field,the drone is guided to escape from the potential field environment in the defective situation,solving the defective problem of the traditional artificial potential field method.Finally,the algorithm in the paper is simulated and analyzed with the comparison algorithms.The results show that compared with the fast search random tree algorithm and the traditional artificial potential field method,the algorithm in the paper completes the path planning in all obstacle cases and shortens the length of planned path,further improving the safety and effectiveness of path planning.