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An Active Learning Based Robot Kinematic Calibration Framework Using Gaussian Processes

Daş, Ersin and Burdick, Joel W. (2023) An Active Learning Based Robot Kinematic Calibration Framework Using Gaussian Processes. . (Unpublished)

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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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URLURL TypeDescription Paper
Daş, Ersin0000-0003-1291-3803
Burdick, Joel W.0000-0002-3091-540X
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
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120110
Deposited By: George Porter
Deposited On:17 Mar 2023 00:45
Last Modified:17 Mar 2023 00:45

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