Application of Deep Q-Learning for Wheel Mobile Robot Navigation

P. K. Mohanty, Arun Kumar Sah, Vikash Kumar, S. Kundu
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引用次数: 12

Abstract

Autonomous mobile robot has tremendous application in various environment due to the fact that they work without human intervention. Path planning and obstacle avoidance are challenging problem for autonomous mobile robot. This paper explores--- the obstacle avoidance technique for wheeled mobile robot based on Deep-Q-Learning. In this paper, we introduce a log-based reward value field function which is the reward receives by agent based on relative positions of agent, obstacles and goal. We perform the experiment in simulated environment and physical environment. Finally, we measure the accuracy of the performance of the obstacle avoidance ability of the robot based of hit rate metrices. Our presented method achieves high success rate to avoid collisions.
深度q -学习在轮式移动机器人导航中的应用
自主移动机器人由于其工作无需人为干预,在各种环境中有着广泛的应用。路径规划和避障是自主移动机器人面临的难题。本文探讨了基于深度q学习的轮式移动机器人避障技术。本文引入了基于对数的奖励值场函数,该函数是基于智能体、障碍物和目标的相对位置的智能体所获得的奖励。实验分别在模拟环境和物理环境下进行。最后,我们基于命中率度量来衡量机器人避障能力性能的准确性。该方法具有较高的避免碰撞成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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