Advanced Sensor and Target Development to Support Robot Accuracy Degradation Assessment

Published

August 25, 2019

Author(s)

Guixiu Qiao

Abstract

This paper presents a vision-based 6 degree of freedom (DOF) measurement system that can measure robot dynamic motions in real-time. A motorized target is designed as a part of the system to work with a vision-based measurement instrument, providing unique features to stand out from the background and enable the achievement of high accuracy measurement. With the capability to measure the robot's 6 DOF accuracy, the robot's accuracy degradation can be monitored, assessed, and predicted to avoid a costly unexpected shutdown, or decrease of manufacturing quality and production efficiency. The development of monitoring, diagnostic, and prognostic technologies is collectively known as Prognostics and Health Management (PHM). NIST is developing the necessary measurement science to support the monitoring, diagnostics, and prognostics of robot system by providing intelligence to enhance maintenance and control strategies. The robot accuracy degradation research includes modeling and algorithm development for the test method, advanced sensor and target development to measure robot 6 DOF accuracy, and algorithms to analyze the data. This paper focuses on the development of the advanced sensor and target. A use case shows the use of the measurement system on a Universal Robot to support the robot accuracy degradation assessment.

Proceedings Title

IEEE International Conference on Automation Science and Engineering (CASE2019)

Conference Dates

August 22-26, 2019

Conference Location

Vancouver, BC,, CA

Pub Type

Conferences

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Keywords

accuracy degradation assessment, diagnostics, manufacturing, monitoring, prognostics, sensing, industrial robot, prognostic and health management (PHM)

Citation

Qiao, G. (2019), Advanced Sensor and Target Development to Support Robot Accuracy Degradation Assessment, IEEE International Conference on Automation Science and Engineering (CASE2019), Vancouver, BC,, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927700 (Accessed April 26, 2024)