基于游戏的DQN和增强现实的户外机器人导航系统

Sivapong Nilwong, G. Capi
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引用次数: 4

摘要

提出了一种基于深度强化学习的基于视觉信息的机器人户外导航方法。深度q网络(deep q network, DQN)将视觉数据映射到机器人在目标位置到达任务中的动作。该方法的一个优点是,所实现的DQN是在ViZDoom提供的第一人称射击(FPS)游戏模拟环境中训练的。FPS模拟环境减小了训练环境与真实环境的差异,使训练后的dqn具有良好的性能。在我们的实现中,使用基于标记的增强现实算法和简单的对象检测方法来训练DQN。在没有额外训练的情况下,该室外导航系统在仿真和真实机器人中进行了测试。实验结果表明,在游戏模拟中训练的导航系统能够引导真实机器人完成室外目标导向的导航任务。
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Outdoor Robot Navigation System using Game-Based DQN and Augmented Reality
This paper presents a deep reinforcement learning based robot outdoor navigation method using visual information. The deep q network (DQN) maps the visual data to robot action in a goal location reaching task. An advantage of the proposed method is that the implemented DQN is trained in the first-person shooter (FPS) game-based simulated environment provided by ViZDoom. The FPS simulated environment reduces the differences between the training and the real environments resulting in a good performance of trained DQNs. In our implementation a marker-based augmented reality algorithm with a simple object detection method is used to train the DQN. The proposed outdoor navigation system is tested in the simulation and real robot implementation, with no additional training. Experimental results showed that the navigation system trained inside the game-based simulation can guide the real robot in outdoor goal directed navigation tasks.
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