摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的研究发现被用在一份新的报告中。根据NewsRx记者从法国巴黎发回的新闻报道，研究称，“本文描述了利用计算机视觉和激光雷达技术的各种方法。”我们的新闻编辑从PSL Research University的研究中获得了一句话：“这些方法包括但不限于基于视觉的算法，如更快的RCNN模型和AprilTag；以及单点探测器(SSD)。在进行对接和充电操作时，为了使移动机器人系统更容易在工业环境中进行自主对接和充电,本文提出了一种利用视觉和激光雷达技术的新方法,提出了YOLOv7深度学习模型来寻找充电站,并进一步简化了与指定无线充电站的对接。使用基于LIDAR的方法来精确地修改机器人的位置。结果和讨论部分介绍了调整后的YOLOv7模型所使用的评估标准和培训程序。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on robotics are disc ussed in a new report. According to news reporting from Paris, France, by NewsRx journalists, research stated, "This article describes various approaches that u tilize computer vision and Lidar technology." Our news editors obtained a quote from the research from PSL Research University : "These approaches include, but not limited to, vision-based algorithms such as the Faster RCNN model and AprilTag; and single shot detectors (SSD). In carryin g out docking and recharging operations, the aforementioned approaches have show n varying degrees of success and accuracy. In order to make it easier for mobile robot systems to perform autonomous docking and recharging (ADaR) in industrial settings, this study presents a new method that employs vision and Lidar techno logy. In this study, we propose the YOLOv7 deep learning model to find charging stations. To further simplify docking with the specified wireless charging stati on, a Lidar-based approach is used to precisely modify the robot's position. An account of the assessment standards and training procedure used for the adjusted YOLOv7 model is provided in the results and discussion section."
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