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首页> 外文会议>International Conference on Computer and Automation Engineering >Local Navigation and Docking of an Autonomous Robot Mower Using Reinforcement Learning and Computer Vision
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Local Navigation and Docking of an Autonomous Robot Mower Using Reinforcement Learning and Computer Vision

机译：使用加固学习和计算机愿望的自治机器人割草机的本地导航和对接

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We demonstrate a successful navigation and docking control system for the John Deere Tango autonomous mower, using only a single camera as the input. This vision-only system is of interest because it is inexpensive, simple for production, and requires no external sensing. This is in contrast to existing systems that rely on integrated position sensors and global positioning system (GPS) technologies. To produce our system we combined a state-of-the-art object detection architecture, You Look Only Once (YOLO), with a reinforcement learning (RL) architecture, Double Deep Q-Networks (Double DQN). The object detection network identifies features on the mower and passes its output to the RL network, providing it with a low-dimensional representation that enables rapid and robust training. Finally, the RL network learns how to navigate the machine to the desired spot in a custom simulation environment. When tested on mower hardware, the system is able to dock with centimeter-level accuracy from arbitrary initial locations and orientations.
机译：我们展示了John Deere Tango自治割草机的成功导航和对接控制系统，只使用单个相机作为输入。仅限视觉系统非常感兴趣，因为它廉价，生产简单，不需要外部传感。这与依赖于集成位置传感器和全球定位系统（GPS）技术的现有系统相反。为了生产我们的系统，我们组合了最先进的对象检测架构，您只需一次（YOLO），使用加强学习（RL）架构，双层Q-Network（双DQN）。对象检测网络识别割草机上的功能，并将其输出传递给RL网络，提供具有快速和稳健培训的低维表示。最后，RL网络了解如何在自定义模拟环境中将机器导航到所需的位置。在割草机硬件上测试时，系统能够从任意初始位置和方向靠厘米级精度停靠。

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