基于强化学习的农业移动机器人视觉导航

以强化学习为基础,结合模糊逻辑理论研究了农业移动机器人通过自主学习获取导航控制策略的方法。首先使用机器视觉检测环境障碍并获取障碍物相对于移动机器人的方向和距离信息。然后应用强化学习设计了机器人自主获取导航控制策略方法,使机器人能够不断适应动态变化的导航环境。最后基于模糊逻辑离散化连续的障碍物方向和距离信息,构建了离散化的环境状态,并据此制定了自主导航学习Q值表。在自制的轮式移动机器人平台上开展了试验,结果表明机器人可以在实际导航环境中自动获取更优的导航策略,完成预期的导航任务。

Vision Navigation of Agricultural Mobile Robot Based on Reinforcement Learning

The method that agricultural mobile robot acquire the navigation strategies through autonomous learning was development based on reinforcement learning and fuzzy logic. Firstly, the machine vision was applied to detect obstacles in the navigation environment, and the corresponding direction and distance between the robot and the obstacle was calculated. Then the algorithm of acquiring the more optimal navigation strategies was introduced with the reinforcement learning, so the capability of the mobile robot of adapting the dynamic navigation environment was improved. Finally, the continuous values of the direction and the distance between the obstacles and the mobile robot were discretized with the fuzzy logic rules, and the discrete navigation environment states were obtained, then the Q value table was designed for the reinforcement learning. The experiment was carried out with the wheeled mobile robot, and the experimental results showed that the mobile robot was able to automatically acquire more optimal navigation strategies in the actual environment, and fulfill the expected navigation tasks.