一种水面无人艇多任务点路径规划方法

A multi-task point path planning method for unmanned surface vehicles

为解决多障碍物环境下水面无人艇(unmanned surface vehicle,USV)多任务点路径规划问题,提出一种基于改进的快速探索随机树(rapidly-exploring random tree,RRT)的路径规划算法。在分析USV运动数学模型和经典RRT算法的基础上,将USV的运动数学模型融合到RRT算法中,预报两个任务点之间的路径曲线和距离;针对RRT算法随机性的特点,设计RRT路径优化算法,删除冗余路径点,得到优化路径;最后利用改进遗传算法,确定多任务点的访问顺序,生成多任务点路径,节省USV巡航路径距离。仿真结果证明,在多任务点及多障碍物存在的条件下,该方法能够确定一条合理的路径,具有一定的实际意义。

In order to solve the problem of multi-task point path planning for unmanned surface vehicles(USVs)in the multi-obstacle environment,a path planning algorithm based on the improved rapidly-exploring random tree(RRT)is proposed.Based on the analysis of USV’s motion mathematical model and the classical RRT algorithm,the USV’s motion mathematical model is integrated into the RRT algorithm,and the path curve and distance between two task points are forecasted.According to the randomness of RRT algorithm,an RRT path optimization algorithm is designed to delete the redundant path points so as to get an optimized path.Finally,the improved genetic algorithm is used to determine the access sequence of multi-task points and generate the multi-task point path,which can save the USV cruise path distance.The simulation result shows that the proposed method can determine a reasonable path under the condition of multi-task points and multiple obstacles,and has a certain practical significance.