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Statistical error analysis and calibration of industrial robots for precision manufacturing

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Abstract

This paper presents a new perspective on the calibration of industrial robotics systems by describing robot position errors as time varying variations of the actual positions of the end-effector from the target position, the error calibration of a robotics manipulator is treated as a “process” rather than a “state”. Therefore the theory of variations is applicable. The importance of distinguishing the two kinds of variations, namely random system variations and assignable variations, in a robot calibration process is demonstrated through a Monte Carlo simulation.

Statistical process control techniques provide a tool for measuring and characterising the position errors of robotics manipulators. Their application in this work, not only focuses on the development of mathematical formulation of calibration models, but provides a deeper understanding of the randomness of the errors in robot systems.

It is also shown that if a robot system is adjusted for its random errors to compensate for undesirable results, the resulting output will be worse than if the robot system alone was not adjusted.

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Authors and Affiliations

  1. Transamerica Insurance Group, Woodland Hills, USA

    G. Z. Qian

  2. Mechanical Engineering Department, University of Connecticut, 191 Auditorium Road, U-139, 06269-3139, Storrs, CT, USA

    K. Kazerounian

Authors
  1. G. Z. Qian
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  2. K. Kazerounian
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Qian, G.Z., Kazerounian, K. Statistical error analysis and calibration of industrial robots for precision manufacturing. Int J Adv Manuf Technol 11, 300–308 (1996). https://doi.org/10.1007/BF01351287

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