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AUTO-CALIBRATION FOR VISION-BASED 6-D SENSING SYSTEM TO SUPPORT MONITORING AND HEALTH MANAGEMENT FOR INDUSTRIAL ROBOTS

Published

Author(s)

Guixiu Qiao, Guangkun Li

Abstract

Industrial robots play an important role in manufacturing automation for smart manufacturing. Some high-precision applications, for example, robot drilling, robot machining, robot high-precision assembly, and robot inspection, require higher robot accuracy compared with traditional part handling operations. The monitoring and assessment of robot accuracy degradation become critical for these applications. A novel vision-based sensing system for 6-D measurement (six-dimensional x, y, z, yaw, pitch, and roll) is developed at the National Institute of Standards and Technology (NIST) to measure the dynamic high accuracy movement of a robot arm. The measured 6-D information is used for robot accuracy degradation assessment and improvement. This paper presents an automatic calibration method for a vision-based 6-D sensing system. Optimization algorithms are developed to achieve high calibration accuracy. Stereo calibration is separated from distortion calibration to speed up the on-site adjustment. The vision-based 6-D sensing system is used on a Universal Robots (UR5) to demonstrate the feasibility of using the system to assess the robot's accuracy degradation.
Proceedings Title
Proceedings 2021 ASME International Manufacturing Science and Engineering Conference (MSEC2021)
Conference Dates
June 21-25, 2021
Conference Location
Cincinnati, OH, US

Keywords

Advanced Sensing, Vision-based System Calibration, Prognostics and Health Management (PHM), Industrial Robot, Accuracy Degradation

Citation

Qiao, G. and Li, G. (2021), AUTO-CALIBRATION FOR VISION-BASED 6-D SENSING SYSTEM TO SUPPORT MONITORING AND HEALTH MANAGEMENT FOR INDUSTRIAL ROBOTS, Proceedings 2021 ASME International Manufacturing Science and Engineering Conference (MSEC2021), Cincinnati, OH, US, [online], https://doi.org/10.1115/MSEC2021-63892, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931513 (Accessed December 3, 2022)
Created August 25, 2021, Updated October 14, 2021