病态辨识矩阵下三自由度并联机构的运动自标定

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI:10.1109/RCAR52367.2021.9517431

X. Duan, Lixing Jin, Changsheng Li, Rui He, Quanbin Lai, Rui Ma

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引用次数: 0

摘要

运动学标定是提高机构精度的有效手段。冗余驱动并联机构具有运动自标定的潜力。研究了三自由度并联机构的自标定算法。由于运动学参数之间存在多重共线性，导致自标定雅可比矩阵存在病态，用最小二乘法计算存在发散。利用截断奇异值分解(TSVD)、脊回归(RR)和Liu估计算法解决了这一问题，并比较了不同误差参数下的识别性能。

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Kinematic Self-calibration of a 3-DOF Parallel Mechanism with Ill-conditioned Identification Matrix

Kinematics calibration is an effective means to improve the accuracy of the mechanism. Parallel mechanisms with redundant actuation have potential for kinematic self-calibration. In this paper, the self-calibration algorithms of a parallel mechanism with 3-DOF are studied. The Jacobian matrix for self-calibration is ill-posed/ill-condition caused by multicollinearity between kinematics parameter, and the calculation with least square method diverges. Truncated singular value decomposition (TSVD), ridge regression (RR) and Liu estimation algorithm are utilized to address this problem, and the identification performances under different error parameters are compared.

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2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)

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