基于多目标快速探索随机树的移动机器人巡检路径优化方法

Multi-objective Rapidly-Exploring Random Tree Robot Patrol Path Optimization Method

针对移动机器人需要访问多目标的巡检路径规划问题,该文提出一种多目标快速探索随机树路径优化方法。首先,根据提供的环境地图与巡检目标点,该文采用一种RRT-Connect-ACO算法得到目标点的巡检顺序和可行路径;然后,通过引入信息子集,对路径进行优化,得到最终的最优路径。实验结果表明,与现有的多目标路径规划算法相比,该方法考虑了地形的影响,得到的最优路径更符合实际情况。

A multi-objective rapidly-exploring random tree path optimization method is proposed for the multiobjective patrol path planning of mobile robots.According to the provided environment map and patrol target points,a new method RRT-Connect-ACO is used to obtain the patrol sequence and feasible path of the target points.Then the optimal path is obtained by introducing informed subset to optimize the path.The experiment results show that the method considers the influence of terrain and obtains an optimal path that is more consistent with the actual situation,which is different from the existing multi-objective path planning algorithms.