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Uncertainty analysis and allocation of joint tolerances in robot manipulators based on interval analysis

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  • Wu, Weidong
  • Rao, S.S.

Abstract

Many uncertain factors influence the accuracy and repeatability of robots. These factors include manufacturing and assembly tolerances and deviations in actuators and controllers. The effects of these uncertain factors must be carefully analyzed to obtain a clear insight into the manipulator performance. In order to ensure the position and orientation accuracy of a robot end effector as well as to reduce the manufacturing cost of the robot, it is necessary to quantify the influence of the uncertain factors and optimally allocate the tolerances. This involves a study of the direct and inverse kinematics of robot end effectors in the presence of uncertain factors. This paper focuses on the optimal allocation of joint tolerances with consideration of the positional and directional errors of the robot end effector and the manufacturing cost. The interval analysis is used for predicting errors in the performance of robot manipulators. The Stanford manipulator is considered for illustration. The unknown joint variables are modeled as interval parameters due to the inherent uncertainty. The cost-tolerance model is assumed to be of an exponential form during optimization. The effects of the upper bounds on the minimum cost and relative deviations of the directional and positional errors of the end effector are also studied.

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  • Wu, Weidong & Rao, S.S., 2007. "Uncertainty analysis and allocation of joint tolerances in robot manipulators based on interval analysis," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 54-64.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:1:p:54-64
    DOI: 10.1016/j.ress.2005.11.009
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    Cited by:

    1. Savage, Gordon J. & Zhang, Xufang & Son, Young Kap & Pandey, Mahesh D., 2016. "Reliability of mechanisms with periodic random modal frequencies using an extreme value-based approach," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 65-77.
    2. Zeng, Chen-dong & Qiu, Zhi-cheng & Zhang, Fen-hua & Zhang, Xian-min, 2023. "Error modelling and motion reliability analysis of a multi-DOF redundant parallel mechanism with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Nawfal BAHHA & Imane El KARTIT, 2021. "How to Reduce Uncertainty in Supply Chains? The Role of the Interactive Control Lever," International Business Research, Canadian Center of Science and Education, vol. 14(6), pages 1-68, June.
    4. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    5. Zhang, Dequan & Shen, Shuoshuo & Wu, Jinhui & Wang, Fang & Han, Xu, 2023. "Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Jayaraman, Deepan & Ramu, Palaniappan, 2023. "L-moments and Bayesian inference for probabilistic risk assessment with scarce samples that include extremes," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Huang, Peng & Gu, Yingkui & Li, He & Yazdi, Mohammad & Qiu, Guangqi, 2023. "An Optimal Tolerance Design Approach of Robot Manipulators for Positioning Accuracy Reliability," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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