论文标题
使用深钢筋学习的低成本摄像机的移动机器人规划师
Mobile Robot Planner with Low-cost Cameras Using Deep Reinforcement Learning
论文作者
论文摘要
这项研究开发了基于深入的强化学习的机器人移动政策。由于传统机器人导航的传统方法取决于准确的地图复制以及需要高端传感器，因此基于学习的方法是积极的趋势，尤其是深度强化学习。该问题是以马尔可夫决策过程（MDP）的形式建模的，代理是移动机器人。它的观点是通过激光调查结果或摄像机等输入传感器获得的，目的是在没有任何碰撞的情况下导航到目标。有许多深度学习方法可以解决这个问题。但是，为了将机器人推向市场，低成本的批量生产也是一个需要解决的问题。因此，这项工作试图根据直接深度矩阵预测从单个相机图像进行构建伪激光调查系统，同时仍保持稳定的性能。实验结果表明，它们使用高价传感器直接与其他传感器可比。
This study develops a robot mobility policy based on deep reinforcement learning. Since traditional methods of conventional robotic navigation depend on accurate map reproduction as well as require high-end sensors, learning-based methods are positive trends, especially deep reinforcement learning. The problem is modeled in the form of a Markov Decision Process (MDP) with the agent being a mobile robot. Its state of view is obtained by the input sensors such as laser findings or cameras and the purpose is navigating to the goal without any collision. There have been many deep learning methods that solve this problem. However, in order to bring robots to market, low-cost mass production is also an issue that needs to be addressed. Therefore, this work attempts to construct a pseudo laser findings system based on direct depth matrix prediction from a single camera image while still retaining stable performances. Experiment results show that they are directly comparable with others using high-priced sensors.
