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The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist

机译:基于机器学习的优化算法与非球形手腕两幅画机器人的逆运动学

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This paper studies the inverse kinematics of two non-spherical wrist configurations of painting robot. The simplest analytical solution of orthogonal wrist configuration is deduced in this paper for the first time. For the oblique wrist configuration, there is no analytical solution for the configuration. So it is necessary to solve by general method, which cannot achieve high precision and high speed as analytic solution. Two general methods are optimized in this paper. Firstly, the elimination method is optimized to reduce the solving speed to 20% of the original one, and the completeness of the method is supplemented. Based on the Gauss damped least squares method, a new optimization method is proposed to improve the solving speed. The enhanced step length coefficient is introduced to conduct studies with the machine learning correlation method. It has been proved that, on the basis of ensuring the stability of motion, the number of iterations can be effectively reduced and the average number of iterations can be less than 5 times, which can effectively improve the speed of solution. In the simulation and experimental environment, it is verified.
机译:本文研究了两种非球形手腕配置的绘画机器人的逆运动学。本文首次推导出正交手腕配置的最简单的分析解决方案。对于倾斜手腕配置,没有用于配置的分析解决方案。因此,有必要通过一般方法解决,这不能达到高精度和高速作为分析解决方案。本文优化了两种一般方法。首先,优化消除方法以将求解速度降低到原始的求解速度,并且补充了该方法的完整性。基于高斯阻尼最小二乘法,提出了一种新的优化方法来提高求解速度。引入增强的步长系数以通过机器学习相关方法进行研究。已经证明,在确保运动的稳定性的基础上,可以有效地减少迭代的数量,并且平均迭代次数可以小于5倍,这可以有效提高解决方案的速度。在仿真和实验环境中,验证。

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