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A monocular vision system for online pose measurement of a 3RRR planar parallel manipulator

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Abstract

In this paper, we develop a monocular vision system for online pose measurement of a 3-RRR planar parallel manipulator (PPM). By combining with a camera with global shutter, an active marker array, an industrial personal computer, and a degenerated perspective-n-points (DPnP) algorithm, a monocular vision measurement system (MVMS) is established. To improve measuring accuracy of the MVMS, factors that cause inaccuracy including the lens distortion, non-perpendicular angle, and input parameters’ uncertainty are analyzed and modeled in detail. In the simulation, effects of these error factors on the accuracy of the MVMS are quantitatively displayed, and comparisons between the DPnP algorithms and other state-of-art PnP algorithms are conducted. Experimental tests on the constructed MVMS demonstrate that it not only can accurately and efficiently measure pose of the 3RRR PPM, but possesses a higher operability and stability compared to the laser tracker.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (U1501247, 51605166, 1609206); the Scientific and Technological Research Project of Guangdong Province (2015B020239001, 2014B090917001); the Science and Technology Program of Guangzhou, China (201604010100); the State Key Laboratory of Pulp and Paper Engineering (2017ZD06).

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Precision and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China

    Hai Li, Xian-min Zhang, Lei Zeng & Yan-jiang Huang

  2. State Key Laboratory of Pulp and Paper Engineering, Guangzhou, 510640, China

    Hai Li, Xian-min Zhang, Lei Zeng & Yan-jiang Huang

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  1. Hai Li
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  2. Xian-min Zhang
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  3. Lei Zeng
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  4. Yan-jiang Huang
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Correspondence to Xian-min Zhang.

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Li, H., Zhang, Xm., Zeng, L. et al. A monocular vision system for online pose measurement of a 3RRR planar parallel manipulator. J Intell Robot Syst 92, 3–17 (2018). https://doi.org/10.1007/s10846-017-0720-5

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  • DOI: https://doi.org/10.1007/s10846-017-0720-5

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