Abstract
While there are many works developing methods for modeling and calibrating robot kinematics, assessing the accuracy of those models has received little attention. However, accuracy assessment is critically important for applications where the robot must operate with absolute accuracy over a large region of workspace, such as in robotic machining. When the model of such a system is well calibrated, the remaining deterministic error can be quite complex, owing to complicated gearing errors, deformations, and quasi-static thermal changes. Locating the largest deterministic error requires an exploration over the workspace, but assessing the largest error is complicated by repeatability error and measurement noise. How then to assess the largest error from such a measurement set? This paper evaluates the efficacy of two conventional methods, maximum measured error and outlier rejection, and a novel method based on model invalidation that uses a hypothesis testing framework. A machining robot is used to develop a numerical study for evaluation of these methods under differing magnitude of measurement noise. A high-order kinematic model of the robot is constructed as used as the true robot kinematics, and the workspace for that system is used as the region of interest. A best-fit Denavit-Hartenberg (DH) model is used as the model whose accuracy is to be measured. The study shows that the largest deterministic error can be difficult to locate with just a few percent of points approaching the defining accuracy limit. As expected, the largest measured error provides a poor (over)estimate of the error as noise is increased, but outlier rejection can be equally as bad as rare large deterministic errors can be easily mistaken for low-probability large random error. The novel model invalidation method, however, performs well across all noise levels.
Recommended Citation
M. R. Woodside et al., "An Evaluation of Methods for Assessing Robot Kinematic Model Accuracy in the Presence of Noise," IEEE International Conference on Automation Science and Engineering, pp. 1714 - 1721, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/CASE59546.2024.10711805
Department(s)
Mechanical and Aerospace Engineering
International Standard Serial Number (ISSN)
2161-8089; 2161-8070
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Comments
U.S. Department of Commerce, Grant None