Visual Servo Control and Implementation of Artificial Neural Networks for Inverse Kinematics Resolution of a Robotic Manipulator with 5 Degrees of Freedom

Abstract

In this study, visual servo control and artificial neural networks (ANNs) are used to address the inverse kinematics problem in a robotic manipulator with five degrees of freedom. The goal is to enable the robot to use visual cues from its surroundings to precisely and adaptively position its end effector. Gathering input-output data, training the ANN, and integrating it with the visual servo control system comprise the suggested methodology. The outcomes demonstrate the effectiveness of the strategy, enabling the manipulator to follow intended trajectories based on visual data and demonstrating resilience to changes in the environment.