engleski

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place atool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neuralnetwork, more specifically, a feed-forward type, multilayer perceptron (MLP), is trained, so that it could be used tocalculate the inverse kinematics for a robotic manipulator. First, direct kinematics of a robotic manipulator are determinedusing Denavit–Hartenberg method and a dataset of 15, 000 points is generated using the calculated homogenous trans-formation matrices. Following that, multiple MLPs are trained with 10, 240 different hyperparameter combinations to findthe best. Each trained MLP is evaluated using theR2and mean absolute error metrics and the architectures of the MLPsthat achieved the best results are presented. Results show a successful regression for the first five joints (percentage errorbeing less than 0.1%) but a comparatively poor regression for the final joint due to the configuration of the roboticmanipulator.

aartificial intelligence, artificial neural network, inverse kinematics, machine learning, multilayer perceptron, roboticmanipulator

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