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IJAT Vol.19 No.3 pp. 204-215
doi: 10.20965/ijat.2025.p0204
(2025)

Research Paper:

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Visual-Based Joint Compliance Calibration Using Measurement Pose Optimization

Xiaotian Zhang*1,†, Yusheng Wang*2, Shouhei Shirafuji*3, Naoya Kagawa*4, Noritaka Takamura*4, Keiji Okuhara*4, Hiroyasu Baba*4, and Jun Ota*2

*1Department of Precision Engineering, School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

†Corresponding author

*2Research into Artifacts, Center for Engineering, School of Engineering, The University of Tokyo
Tokyo, Japan

*3Department of Mechanical Engineering, Faculty of Engineering Science, Kansai University
Suita, Japan

*4FA Products Business Unit, Robot Engineering Div. 2, DENSO WAVE Incorporated
Agui, Japan

Received:
November 19, 2024
Accepted:
January 15, 2025
Published:
May 5, 2025
Creative Commons License
Keywords:
robot calibration, perspective-n-point (PnP), observability index, joint compliance, robotics
Abstract

Accurate calibration of robot joint compliance poses a significant challenge with limited existing research. For camera-based calibration, concurrently identifying both joint offsets and compliance errors becomes intricate due to measurement inaccuracies. To overcome this problem, this paper proposes an innovative approach that leverages measurement pose optimization. By leveraging the local product of exponentials (POE) model, our method enables the simultaneous identification of the geometric parameters (joint offsets) and non-geometric parameters (joint compliance). The introduction of a modified visual observability index minimizes sensitivity to camera errors during joint compliance calibration. Experimental results conducted on a 6R serial robot show superior accuracy compared to existing indices, validating the effectiveness of our approach.

Cite this article as:
X. Zhang, Y. Wang, S. Shirafuji, N. Kagawa, N. Takamura, K. Okuhara, H. Baba, and J. Ota, “Visual-Based Joint Compliance Calibration Using Measurement Pose Optimization,” Int. J. Automation Technol., Vol.19 No.3, pp. 204-215, 2025.
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Last updated on May. 08, 2025