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Residual Vibration Reduction of High-Speed Pick-and-Place Parallel Robot Using Input Shaping

Xianlei Shan et al · KeAi Communications Co., Ltd · 2022

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Abstract Because of their elastic links and joints, high-speed parallel robots for pick-and-place operations inevitably suffer from residual vibrations that significantly degrade their positioning accuracy. An effective approach based on the input shaping technique is presented in this paper for suppressing the residual vibration in these parallel robots. After addressing the design principle of an input shaper for a parallel robot with flexible actuated joints, a robust optimal input shaper is developed by considering the configuration-dependent flexible modes and minimizing the maximum percentage of residual vibration at the end-effector. The input shaper allows a good overall performance to be achieved throughout the entire workspace. Experimental results on a 4-DOF SCARA-type parallel robot show that the residual vibration of the end-effector is dramatically reduced and the dynamic positioning accuracy of the robot significantly improved.

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APA 7

al, X. S. E. (2022). Residual Vibration Reduction of High-Speed Pick-and-Place Parallel Robot Using Input Shaping. https://doi.org/10.1186/s10033-022-00679-3

MLA

al, Xianlei Shan et. "Residual Vibration Reduction of High-Speed Pick-and-Place Parallel Robot Using Input Shaping." 2022. https://doi.org/10.1186/s10033-022-00679-3.

Chicago

al, Xianlei Shan et. 2022. "Residual Vibration Reduction of High-Speed Pick-and-Place Parallel Robot Using Input Shaping.". https://doi.org/10.1186/s10033-022-00679-3.

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al, X. S. E. 2022, Residual Vibration Reduction of High-Speed Pick-and-Place Parallel Robot Using Input Shaping, KeAi Communications Co, Ltd, available at: https://doi.org/10.1186/s10033-022-00679-3 [Accessed 8 Aug. 2026].

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Titolo
Residual Vibration Reduction of High-Speed Pick-and-Place Parallel Robot Using Input Shaping
Autore / collaboratori
Xianlei Shan et al
Editore
KeAi Communications Co., Ltd
Anno di pubblicazione
2022
ISSN
1000-9345
ISSN
1000-9345
Lingua
Inglés

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