Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

L. Hanh, V. Cong
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引用次数: 1

Abstract

Obstacle avoidance for mobile robot to reach the desired target from a start location is one of the most interesting research topics. However, until now, few works discuss about working of mobile robot in the dynamic and continuously changing environment. So, this issue is still the research challenge for mobile robots. Traditional algorithm for obstacle avoidance in the dynamic, complex environment had many drawbacks. As known that Q-learning, the type of reinforcement learning, has been successfully applied in computer games. However, it is still rarely used in real world applications. This research presents an effectively method for real time dynamic obstacle avoidance based on Q-learning in the real world by using three-wheeled mobile robot. The position of obstacles including many static and dynamic obstacles and the mobile robot are recognized by fixed camera installed above the working space. The input for the robot is the 2D data from the camera. The output is an action for the robot (velocities, linear and angular parameters). Firstly, the simulation is performed for Q-learning algorithm then based on trained data, The Q-table value is implemented to the real mobile robot to perform the task in the real scene. The results are compared with intelligent control method for both static and dynamic obstacles cases. Through implement experiments, the results show that, after training in dynamic environments and testing in a new environment, the mobile robot is able to reach the target position successfully and have better performance comparing with fuzzy controller.
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基于强化学习的动态环境下移动机器人路径跟踪与避障
移动机器人从起始位置到达预定目标的避障问题一直是研究的热点之一。然而,到目前为止,很少有研究讨论移动机器人在动态和不断变化的环境中的工作。因此,这个问题仍然是移动机器人的研究挑战。传统的避障算法在动态、复杂环境中存在许多缺陷。众所周知,作为强化学习的一种,Q-learning已经成功地应用于电脑游戏中。然而,它在实际应用中仍然很少使用。本文以三轮移动机器人为研究对象,提出了一种有效的基于q学习的实时动态避障方法。通过安装在工作空间上方的固定摄像头来识别障碍物的位置,包括许多静态和动态障碍物以及移动机器人。机器人的输入是来自相机的2D数据。输出是机器人的动作(速度、线性和角度参数)。首先对Q-learning算法进行仿真,然后根据训练数据,将Q-table值实现到真实移动机器人上,在真实场景中执行任务。在静态和动态障碍物情况下,与智能控制方法进行了比较。通过实现实验,结果表明,经过动态环境训练和新环境测试,移动机器人能够顺利到达目标位置,并且与模糊控制器相比具有更好的性能。
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