煤矿井下移动机器人路径规划的算法优化

Algorithm Optimization of Coal Mine Underground Mobile Robot Path Planning

机器人研究领域中一大难题就是机器人路径规划问题,特别是对于条件极为恶劣、工况十分复杂的煤矿井下作业环境而言,获取满意的规划结果的难度更大。文章首先对Q-learning算法进行简单介绍,之后阐述了依托于Q-learning算法的路径规划,并进行了仿真分析。

In the robot research field, the robot path planning problem is a big problem especlauy for very bad, complex conditions of coal mine underground work environment, it＇s more difficult to obtain satisfactory results of the planning. In this paper, at first the Q-learning algorithm is simply described, then the path planning, based on Q-learning algorithm is expounded and the simulation analysis is carried out.