IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v246y2024ics095183202400156x.html
   My bibliography  Save this article

An extended moment-based trajectory accuracy reliability analysis method of robot manipulators with random and interval uncertainties

Author

Listed:
  • Huang, Peng
  • Li, He
  • Gu, Yingkui
  • Qiu, Guangqi

Abstract

This paper proposes a novel trajectory accuracy reliability analysis method with random and interval variables to evaluate the impact of mixed uncertainties on the motion performance of robot manipulators. To effectively and accurately solve the hybrid reliability model, the interval analysis is first conducted on the positional error model established by differential kinematics, and a maximum positional error searching algorithm is developed based on geometric transformation and cell enumeration. Then, the trajectory accuracy reliability model is reconstructed by the eigen-decomposition technique, which further incorporates the adaptive weight vector-based anisotropic sparse-grid quadrature approach to derive the statistical moments of maximum positional error. Afterward, by matching with the cumulants of the original reliability model, a novel approximation method is proposed based on the Weibull distribution and minimized matching error model to complete the trajectory accuracy reliability analysis. The practicality and advantages of the proposed method are demonstrated by two illustrative examples of 6-degrees-of-freedom robot manipulators. Comparative results affirm that the proposed method outperforms existing state-of-the-art algorithms in terms of accuracy and efficiency for trajectory accuracy reliability analysis encompassing random and interval uncertainties. Overall, the outcomes of this paper contribute significantly to the design and analysis of safety and reliability in moving machinery.

Suggested Citation

  • Huang, Peng & Li, He & Gu, Yingkui & Qiu, Guangqi, 2024. "An extended moment-based trajectory accuracy reliability analysis method of robot manipulators with random and interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:reensy:v:246:y:2024:i:c:s095183202400156x
    DOI: 10.1016/j.ress.2024.110082
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202400156X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110082?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chang, Qi & Zhou, Changcong & Wei, Pengfei & Zhang, Yishang & Yue, Zhufeng, 2021. "A new non-probabilistic time-dependent reliability model for mechanisms with interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Liu, Yaru & Wang, Lei & Ng, Bing Feng, 2024. "A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved Kalman filter algorithm," Applied Energy, Elsevier, vol. 359(C).
    3. 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.
    4. Wu, Jinhui & Tao, Yourui & Han, Xu, 2023. "Polynomial chaos expansion approximation for dimension-reduction model-based reliability analysis method and application to industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Meng, Zeng & Zhao, Jingyu & Chen, Guohai & Yang, Dixiong, 2022. "Hybrid uncertainty propagation and reliability analysis using direct probability integral method and exponential convex model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(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).
    8. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    3. Zhang, Zheng & Wang, Pan & Hu, Huanhuan & Li, Lei & Li, Haihe & Yue, Zhufeng, 2022. "Efficient reliability-based design optimization for hydraulic pipeline with adaptive sampling region," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Xiao, Tianli & Park, Chanseok & Lin, Chenglong & Ouyang, Linhan & Ma, Yizhong, 2023. "Hybrid reliability analysis with incomplete interval data based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Li, Xiaoke & Zhu, Heng & Chen, Zhenzhong & Ming, Wuyi & Cao, Yang & He, Wenbin & Ma, Jun, 2022. "Limit state Kriging modeling for reliability-based design optimization through classification uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. Hong, Fangqi & Wei, Pengfei & Fu, Jiangfeng & Beer, Michael, 2024. "A sequential sampling-based Bayesian numerical method for reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. Zhang, Xiaobo & Lu, Zhenzhou & Cheng, Kai, 2021. "Reliability index function approximation based on adaptive double-loop Kriging for reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Zhang, Ruijing & Dai, Hongzhe, 2022. "A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Nan, Hang & Liang, Hao & Di, Haoyuan & Li, Hongshuang, 2024. "A gradient-assisted learning strategy of Kriging model for robust design optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    10. Zhang, Kun & Chen, Ning & Zeng, Peng & Liu, Jian & Beer, Michael, 2022. "An efficient reliability analysis method for structures with hybrid time-dependent uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Hong, Linxiong & Li, Huacong & Fu, Jiangfeng & Li, Jia & Peng, Kai, 2022. "Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Keshun, You & Guangqi, Qiu & Yingkui, Gu, 2024. "Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Zhang, Yu & Dong, You & Frangopol, Dan M., 2024. "An error-based stopping criterion for spherical decomposition-based adaptive Kriging model and rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    14. 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.
    15. Zhu, Chun-Yan & Li, Zhen-Ao & Dong, Xiao-Wei & Wang, Ming & Li, Qing-Da, 2024. "Collaborative modeling-based improved moving Kriging approach for low-cycle fatigue life reliability estimation of mechanical structures," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    16. Ajenjo, Antoine & Ardillon, Emmanuel & Chabridon, Vincent & Cogan, Scott & Sadoulet-Reboul, Emeline, 2023. "Robustness evaluation of the reliability of penstocks combining line sampling and neural networks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    17. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    18. Song, Zhouzhou & Zhang, Hanyu & Liu, Zhao & Zhu, Ping, 2023. "A two-stage Kriging estimation variance reduction method for efficient time-variant reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. 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).
    20. Alimohammadi, H.R. & Naseh, H. & Ommi, F., 2023. "An integrated methodology applied for reliability based multi-disciplinary design optimization in EPFE with LOX/kerosene," Reliability Engineering and System Safety, Elsevier, vol. 240(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:246:y:2024:i:c:s095183202400156x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.