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Research Papers

Industrial Robot Accuracy Degradation Monitoring and Quick Health Assessment

[+] Author and Article Information
Guixiu Qiao

Mem. ASME
National Institute of Standards and Technology,
100 Bureau Drive, Gaithersburg, MD 20899
e-mail: guixiu.qiao@nist.gov

Brian A. Weiss

Mem. ASME
National Institute of Standards and Technology,
100 Bureau Drive, Gaithersburg, MD 20899
e-mail: brian.weiss@nist.gov

1Corresponding author.

Manuscript received March 18, 2019; final manuscript received April 26, 2019; published online May 14, 2019. Assoc. Editor: Y. Lawrence Yao.

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Manuf. Sci. Eng 141(7), 071006 (May 14, 2019) (7 pages) Paper No: MANU-19-1160; doi: 10.1115/1.4043649 History: Received March 18, 2019; Accepted April 28, 2019

Robot accuracy degradation sensing, monitoring, and assessment are critical activities in many industrial robot applications, especially when it comes to the high accuracy operations which may include welding, material removal, robotic drilling, and robot riveting. The degradation of robot tool center accuracy can increase the likelihood of unexpected shutdowns and decrease manufacturing quality and production efficiency. The development of monitoring, diagnostic and prognostic (collectively known as prognostics and health management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems. PHM can provide the techniques and tools to support the specification of a robot’s present and future health state and optimization of maintenance strategies. This paper presents the robotic PHM research and the development of a quick health assessment at the U.S. National Institute of Standards and Technology (NIST). The research effort includes the advanced sensing development to measure the robot tool center position and orientation; a test method to generate a robot motion plan; an advanced robot error model that handles the geometric/nongeometric errors and the uncertainties of the measurement system, and algorithms to process measured data to assess the robot’s accuracy degradation. The algorithm has no concept of the traditional derivative or gradient for algorithm converging. A use case is presented to demonstrate the feasibility of the methodology.

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Figures

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Fig. 1

Workflow of the robot accuracy degradation quick assessment methodology

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Fig. 2

Fixed-loop measurement plan for UR5

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Fig. 3

Robot fixed-loop motion generation flowchart

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Fig. 4

Six degree-of-freedom errors of the rotation axis

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Fig. 6

Even distribution of measurements in the joint space

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Fig. 7

Robot's error histogram

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Fig. 8

J1's error distribution

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