未知环境下机器人Q学习覆盖算法

Robot Q-learning coverage algorithm in unknown environments

为提高未知环境下机器人区域覆盖率,提出一种Q-学习覆盖算法(QLCA)。对环境建立栅格模型,在栅格地图中随机部署机器人和障碍位置。机器人根据QLCA自主学习得到的Qtable指导其后续的动作选择和路径规划,减少了机器人移动次数。从机器人数目、环境等方面分析了各类参数变化对该算法的影响。仿真实验结果表明:与随机选择覆盖算法对比,QLCA在完成覆盖的执行步数及冗余效果等方面均有明显优化。

A Q-Learning coverage algorithm( QLCA) is presented to improve the area coverage rates of robots in unknown environments. A grid model is constructed for an environment and the positions of robots and barriers are deployed in the grid map randomly. The subsequent action choices and path plans of the robots are directed by the Qtable gained from the robots' self-learning of the QLCA,and the moving frequencies of robots are decreased. The effects of parameters such as the number of robots,environments on this algorithm are analyzed. The simulation results show: compared with the random chosen coverage algorithm( RSCA),the QLCA optimizes the coverage executing steps and redundancy obviously.