Wyk Karl Van, Falco Joe, Cheok Geraldine
National Institute of Standards and Technology, Gaithersburg, MD, USA.
IEEE Trans Autom Sci Eng. 2019;1. doi: 10.48550/arXiv.1908.07273.
The advancement of simulation-assisted robot programming, automation of high-tolerance assembly operations, and improvement of real-world performance engender a need for positionally accurate robots. Despite tight machining tolerances, good mechanical design, and careful assembly, robotic arms typically exhibit average Cartesian positioning errors of several millimeters. Fortunately, the vast majority of this error can be removed in software by proper calibration of the so-called "zero-offsets" of a robot's joints. This research developed an automated, inexpensive, highly portable, calibration method that fine tunes these kinematic parameters, thereby, improving a robot's average positioning accuracy four-fold throughout its workspace. In particular, a prospective low-cost motion capture system and a benchmark laser tracker were used as reference sensors for robot calibration. Bayesian inference produced optimized zero-offset parameters alongside their uncertainty for data from both reference sensors. Relative and absolute accuracy metrics were proposed and applied for quantifying robot positioning accuracy. Uncertainty analysis of a validated, probabilistic robot model quantified the absolute positioning accuracy throughout its entire workspace. Altogether, three measures of accuracy conclusively revealed multi-fold improvement in the positioning accuracy of the robotic arm. Bayesian inference on motion capture data yielded zero-offsets and accuracy calculations comparable to those derived from laser tracker data, ultimately proving this method's viability towards robot calibration.
仿真辅助机器人编程的进步、高公差装配操作的自动化以及实际性能的提升，使得对位置精确的机器人产生了需求。尽管加工公差严格、机械设计良好且装配仔细，但机器人手臂通常表现出几毫米的平均笛卡尔定位误差。幸运的是，通过对机器人关节的所谓“零偏移”进行适当校准，软件中可以消除绝大部分这种误差。本研究开发了一种自动化、低成本、高度便携的校准方法，该方法对这些运动学参数进行微调，从而在机器人的整个工作空间中将其平均定位精度提高了四倍。具体而言，一种预期的低成本运动捕捉系统和一个基准激光跟踪仪被用作机器人校准的参考传感器。贝叶斯推理针对来自两个参考传感器的数据生成了优化的零偏移参数及其不确定性。提出并应用了相对和绝对精度指标来量化机器人的定位精度。对经过验证的概率机器人模型进行不确定性分析，量化了其在整个工作空间中的绝对定位精度。总之，三种精度测量方法最终确凿地表明机器人手臂的定位精度有了数倍的提高。对运动捕捉数据进行贝叶斯推理得出的零偏移和精度计算结果与从激光跟踪仪数据得出的结果相当，最终证明了该方法在机器人校准方面的可行性。
