基于强化学习和模糊逻辑的移动机器人导航

自主导航是移动机器人的一项关键技术。该文采用强化学习结合模糊逻辑的方法实现了未知环境下自主式移动机机器人的导航控制。文中首先介绍了强化学习原理，然后设计了一种未知环境下机器人导航框架。该框架由避碰模块、寻找目标模块和行为选择模块组成。针对该框架，提出了一种基于强化学习和模糊逻辑的学习、规划算法：在对避碰和寻找目标行为进行独立学习后，利用超声波传感器得到的环境信息进行行为选择，使机器人在成功避碰的同时到达目标点。最后通过大量的仿真实验，证明了算法的有效性。

Navigation for Mobile Robots Using Reinforcement Learning and Fuzzy Logic

Autonomous navigation is the key technology of mobile robots. Navigation control of autonomous robots in uncertain environments is realized by using the reinforcement learning and fuzzy logic in this paper. First, the principle of reinforcement learning is introduced. And then a framework is proposed which consists of avoidance module, goal-seeking module and behavior selecting module for navigation of autonomous mobile robots in uncertain environments. According to the framework, a learning-planning method is proposed, which utilizes reinforcement learning and fuzzy logic. Two behaviors are independently designed in training stage and then combined by a behavior selector at running stage. According to information acquired by ultrasonic sensors, the behavior selector chooses a behavior at each action step so that the mobile robot can reach the goal position without colliding with obstacles. At last, the effectiveness of the proposed method is verified by a series of simulations.