Abstract
Parallel kinematic machines (PKMs) have several advantages, like high stiffness. However, its calibration methods are not as good as series kinematic machines (SKMs). In this paper, the position prediction models of a PRPaR mechanism which has a better movement stability was established by two methods, which are interpolation model and kinematic error model respectively. We proposed a computational flow according to the advantages and disadvantages of the two models. The process is calculated using kinematics model at first. Then, the estimated difference was considered as residual error and performing interpolation model calculations. The estimated difference of each model is nearly 60% of the actual error. The proposed computational flow can reduce the final error to 30%. The result is better than each error model. It can even reduce half of the estimated difference of each error model again. The importance of the new computational flow can also be seen.
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Ke, JD., Wang, YJ., Chiu, YJ., Sung, CK. (2022). Error Models and Position Estimations of PRPaR Mechanisms. In: Kim, J., et al. Robot Intelligence Technology and Applications 6. RiTA 2021. Lecture Notes in Networks and Systems, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-030-97672-9_20
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DOI: https://doi.org/10.1007/978-3-030-97672-9_20
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