In this manuscript we report on a vision-based data-driven methodology for industrial robot health assessment. We provide an experimental evidence of the usefulness of our methodology on a system comprised of a 6-axis industrial robot, two monocular cameras and five binary squared fiducial markers. The fiducial marker system permits to accurately track the deviation of the end-effector along a fixed non-trivial trajectory. Moreover, we monitor the trajectory deflection using three gradually increasing weights attached to the end-effector. When the robot is loaded with the maximum allowed payload, a deviation of 0.77mm is identified in the Z-coordinate of the end-effector. Tracing trajectory information, we train five supervised learning regression models. Such models are afterwards used to predict the deviation of the end-effector, using the pose estimation provided by the visual tracking system. As a result of this study, we show that this procedure is a stable, robust, rigorous and reliable tool for robot trajectory deviation estimation and it even allows to identify the mechanical element producing non-kinematic errors.

在本手稿中，我们报告了一种基于视觉的数据驱动方法，用于工业机器人健康评估。我们提供了一个实验证据，证明了我们的方法在由6轴工业机器人，两个单眼相机和五个二进制平方基准标记组成的系统上的有效性。基准标记系统允许沿着固定的非平凡轨迹准确跟踪末端执行器的偏差。此外，我们使用附加在末端执行器上的三个逐渐增加的权重来监视轨迹偏转。当机器人装载了最大允许的有效载荷时，Z轴上的偏差为0.77mm-末端执行器的坐标。跟踪轨迹信息，我们训练了五个监督学习回归模型。然后，使用由视觉跟踪系统提供的姿势估计，将此类模型用于预测末端执行器的偏差。这项研究的结果表明，该程序是用于机器人轨迹偏差估计的稳定，鲁棒，严格和可靠的工具，甚至可以识别产生非运动学误差的机械元件。