Department of Mechatronics, Robotics and Digital Manufacturing, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania.
Sensors (Basel). 2022 May 21;22(10):3911. doi: 10.3390/s22103911.
Recent industrial robotics covers a broad part of the manufacturing spectrum and other human everyday life applications; the performance of these devices has become increasingly important. Positioning accuracy and repeatability, as well as operating speed, are essential in any industrial robotics application. Robot positioning errors are complex due to the extensive combination of their sources and cannot be compensated for using conventional methods. Some robot positioning errors can be compensated for only using machine learning (ML) procedures. Reinforced machine learning increases the robot's positioning accuracy and expands its implementation capabilities. The provided methodology presents an easy and focused approach for industrial in situ robot position adjustment in real-time during production setup or readjustment cases. The scientific value of this approach is a methodology using an ML procedure without huge external datasets for the procedure and extensive computing facilities. This paper presents a deep q-learning algorithm applied to improve the positioning accuracy of an articulated KUKA youBot robot during operation. A significant improvement of the positioning accuracy was achieved approximately after 260 iterations in the online mode and initial simulation of the ML procedure.
最近的工业机器人涵盖了制造业和其他人类日常生活应用的广泛领域；这些设备的性能变得越来越重要。在任何工业机器人应用中，定位精度和可重复性以及运行速度都是至关重要的。由于其来源的广泛组合，机器人的定位误差非常复杂，无法使用常规方法进行补偿。一些机器人定位误差只能使用机器学习 (ML) 程序进行补偿。强化机器学习可以提高机器人的定位精度并扩展其实施能力。所提供的方法为在生产设置或重新调整情况下实时进行工业现场机器人位置调整提供了一种简单而集中的方法。该方法的科学价值在于使用 ML 程序而无需外部大量数据集和广泛的计算设施的方法。本文提出了一种深度 Q 学习算法，用于提高关节式 KUKA youBot 机器人在运行过程中的定位精度。在线模式下大约经过 260 次迭代以及 ML 程序的初步模拟后，定位精度得到了显著提高。
