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Modeling of Inverse Kinematic of 3-DoF Robot, Using Unit Quaternions and Artificial Neural Network

机译:3-DOF机器人的逆运动学建模,使用单位四边形和人工神经网络

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This paper presents a novel method for modeling a 3-degree of freedom open kinematic chain using quaternions algebra and neural network to solve the inverse kinematic problem. The structure of the network was composed of 3 hidden layers with 25 neurons per layer and 1 output layer. The network was trained using the Bayesian regularization backpropagation. The inverse kinematic problem was modeled as a system of six nonlinear equations and six unknowns. Finally, both models were tested using a straight path to compare the results between the Newton-Raphson method and the network training.
机译:本文介绍了一种使用四元数代数和神经网络建模3度自由开放运动链的新方法,以解决反向运动问题。 网络的结构由3个隐藏层组成,每层具有25个神经元和1个输出层。 使用贝叶斯正则化反向化培训网络。 反向运动问题被建模为六个非线性方程和六个未知数的系统。 最后,使用直接路径测试两种模型,以比较Newton-Raphson方法与网络训练之间的结果。

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