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Analytical and Deep Learning Approaches for Solving the Inverse Kinematic Problem of a High Degrees of Freedom Robotic Arm by Nesrine Wagaa; Hichem Kallel; Nédra Mellouli is a Computer Science article available to read on EtoBox.

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Inverse kinematics is the basis for controlling the motion of robotic manipulators. It defines the required joint variables for the robotic end-effector accurately reach the desired location. Due to the derivation difficulty, computation complexity, singularity problem, and redundancy, analytical Inverse kinematics solutions pose numerous challenges to the operation of many robotic arms, especially for a manipulator with a high degree of freedom. This paper develops different Deep Learning networks for solving the Inverse kinematics problem of six- Degrees of Freedom robotic manipulators. The implemented neural architectures are Artificial Neural Network, Convolutional Neural Network, Long–Short Term Memory, Gated Recurrent Unit, and Bidirectional Long–Short Term Memory. In this context, we associate the proposed results with a specific tuning of Deep Learning network hyper-parameters (number of hidden layers, learning rate, Loss function, optimization algorithm, number of epochs, etc.). The Bidirectional Long–Short Term Memory network outperformed all proposed architectures. To be close as possible to the experimental results, we have included two types of noise in the training da

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Author

Nesrine Wagaa; Hichem Kallel; Nédra Mellouli

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)