Daş, Ersin and Burdick, Joel W. (2023) An Active Learning Based Robot Kinematic Calibration Framework Using Gaussian Processes. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20230316-204100500
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Abstract
Future NASA lander missions to icy moons will require completely automated, accurate, and data efficient calibration methods for the robot manipulator arms that sample icy terrains in the lander's vicinity. To support this need, this paper presents a Gaussian Process (GP) approach to the classical manipulator kinematic calibration process. Instead of identifying a corrected set of Denavit-Hartenberg kinematic parameters, a set of GPs models the residual kinematic error of the arm over the workspace. More importantly, this modeling framework allows a Gaussian Process Upper Confident Bound (GP-UCB) algorithm to efficiently and adaptively select the calibration's measurement points so as to minimize the number of experiments, and therefore minimize the time needed for recalibration. The method is demonstrated in simulation on a simple 2-DOF arm, a 6 DOF arm whose geometry is a candidate for a future NASA mission, and a 7 DOF Barrett WAM arm.
Item Type: | Report or Paper (Discussion Paper) | ||||||
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Additional Information: | Attribution 4.0 International (CC BY 4.0) “This work was supported by NASA Grant 80NSSC21K1032. | ||||||
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Record Number: | CaltechAUTHORS:20230316-204100500 | ||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20230316-204100500 | ||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
ID Code: | 120110 | ||||||
Collection: | CaltechAUTHORS | ||||||
Deposited By: | George Porter | ||||||
Deposited On: | 17 Mar 2023 00:45 | ||||||
Last Modified: | 17 Mar 2023 00:45 |
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