Browsing by Autor "Javier Melendez-Campos"

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    Modeling of Inverse Kinematic of 3-DoF Robot, Using Unit Quaternions and Artificial Neural Network
    (Cambridge University Press, 2021) Eusebio Jiménez López; Daniel Servín de la Mora-Pulido; Luis Alfonso Reyes-Ávila; Raúl Servín de la Mora-Pulido; Javier Melendez-Campos; Aldo López-Martínez
    SUMMARY 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.