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首页> 外文期刊>Engineering Optimization >Self-calibration for kinematic parameters of a redundant planar two-degree-of-freedom parallel manipulator using evolutionary algorithms
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Self-calibration for kinematic parameters of a redundant planar two-degree-of-freedom parallel manipulator using evolutionary algorithms

机译：冗余平面两自由度并联机械手运动学参数的进化算法自校正

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摘要

In this article, the self-calibration problem of a planar two-degree-of-freedom (2-DoF) parallel manipulator with a redundant joint sensor is studied. By eliminating the passive joint positions, a new error function is proposed. Furthermore, by decoupling the kinematic parameters of the error function, the minimization process is simplified. In order to obtain the global optimum, three evolutionary algorithms including genetic algorithm, particle swarm optimization, and differential evolution are applied to minimize the error function. In the application, the performances and effectiveness of the applied algorithms on this specific problem are compared under three different error functions, and the results show that the differential-evolution method under the decoupled error function produces the best result. Finally, actual calibration is carried out based on differential evolution under the decoupled error function, and the result demonstrates that all of the 12 parameters of the manipulator are calibrated with high accuracy.
机译：在本文中，研究了带有冗余关节传感器的平面二自由度（2-DoF）并联机械手的自校准问题。通过消除被动关节位置，提出了一种新的误差函数。此外，通过解耦误差函数的运动学参数，简化了最小化过程。为了获得全局最优，应用了三种进化算法，包括遗传算法，粒子群优化算法和微分进化算法，以使误差函数最小。在该应用中，比较了在三种不同的误差函数下该算法在特定问题上的性能和有效性，结果表明，在解耦误差函数下的微分进化方法取得了最佳效果。最后，基于解耦误差函数下的微分演化进行了实际校准，结果表明该机械手的所有12个参数都可以进行高精度校准。

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