应用改进随机树算法的无人艇局部路径规划

Improved rapidly-exploring random tree algorithm application in unmanned surface vehicle local path planning

针对无人艇航速高及实时性要求高的特点,为满足路径规划需要,在经典快速扩展随机树(RRT)算法的基础上,设计一种基于改进RRT算法的局部路径规划方法,引入抑制因子、限定转角和距离启发信息,改进生长点和探索点的选择,提高了算法速度.为兼顾航行距离最短和无人艇操控性能的特殊要求,对规划路径采取多余航点处理以及考虑回转性能的平滑处理.以海上和湖上典型雷达图像的处理结果为环境模型,进行局部路径规划试验.试验结果表明:该方法可以快速完成路径搜索,在提高搜索效率的同时缩短了规划距离,优化处理后的航线更适用于无人艇的跟踪,满足无人艇规划系统的要求.

A path planning method based on improved RRT （Rapidly-Exploring Random Tree ） algorithm was proposed in application to local path planning of USV （Unmanned Surface Vehicle）. Aiming at the high speed and real-time control of USV, the inhibitory factor, limited angle and distance heuristic information were introduced into classical RRT algorithm, thus the selection of exploration points and growing points were modified, and the speed of the algorithm was improved. The excess navigation points in planning path were processed and smoothed considering the gyration performance to make the navigation distance shorter and meet the special demands of maneuver performance of USV. Tile experiment of local path planning was completed based on the environment model constructed by the process results of typical radar images in the sea and lake experiments. The experimental results showed that the suggested algorithm could rapidly complete the path search, the efficient of algorithm was improved and the distance of path was reduced, the planning path after optimization treatment could satisfy the planning system need. The suggested method can apply to USV local path planning.