Inverse Kinematic Modelling of a 3-DOF Robotic Manipulator using Hybrid Deep Learning Models

Abstract

As the degrees of freedom (DOF) for a manipulator rise, so does the complexity of inverse kinematic modeling. This research provides an inverse kinematic model mapped with the aid of a Multilayer Deep Neural Network (DNN) trained using a unique meta-heuristic approach, namely the Gannet Optimization Algorithm (GOA), to decrease the computational weight and time lag for desired output transformation. The suggested design can automatically pick up on the kinematic characteristics of the manipulator. The sole observational basis for repeated learning is the link between input and output. Using the Robot Operating System (ROS), related simulations on a 3-DOF manipulator are performed. The simulation-generated dataset is split 65:35 for the purpose of training and testing the suggested model. Cost, time for the training data, mean relative error, normal mean square error, and mean absolute error for the test data are the metrics utilized for model validation. The efficacy and superiority of the suggested method are demonstrated by a comparison of the GOA-DNN model with the particle swarm optimization (PSO)-DNN and Grey Wolf Optimization (GWO)-DNN meta-heuristic DNN models.