Efficiently Improving and Quantifying Robot Accuracy In Situ

TL;DR

This paper presents an automated, low-cost in situ calibration method that significantly improves robot positioning accuracy by fine-tuning kinematic parameters using Bayesian inference with affordable sensors.

Contribution

The study introduces a novel, portable calibration approach utilizing Bayesian inference and inexpensive sensors to enhance robot accuracy four-fold in real-world environments.

Findings

Four-fold improvement in robot positioning accuracy.

Bayesian inference yields zero-offsets comparable to laser tracker calibration.

Affordable sensors effectively calibrate robots in situ.

Abstract

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, in situ 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.