SUMMARYThis 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.

中文翻译：

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三自由度机器人逆运动学建模，使用单位四元数和人工神经网络

摘要本文提出了一种利用四元数代数和神经网络对三自由度开放运动链进行建模以解决逆运动学问题的新方法。网络结构由 3 个隐藏层组成，每层 25 个神经元和 1 个输出层。该网络使用贝叶斯正则化反向传播进行训练。逆运动学问题被建模为六个非线性方程和六个未知数的系统。最后，两个模型都使用直线路径进行了测试，以比较 Newton-Raphson 方法和网络训练之间的结果。