Inverse Kinematic of Differential Drive Robot Using Neural Networks and Vrep Simulation

This study focuses on the Inverse kinematic modeling of a differential drive robot. A common design in mobile robotics with (CoppeliaSim) is used for modeling the robot's physical structure. The model is controlled MATLAB Simulink to run simulations. To tackle the challenges of establishing inverse kinematics, a neural network-based method is provided. The neural network model is created and tested with simulated data to forecast wheel velocities for various robot trajectories. Its performance is then compared to that of the theoretical inverse kinematic model in terms of accuracy, robustness, and computing time. The findings indicate that the neural network achieves equivalent accuracy while being more responsive to variations in robot parameters and ambient variables. Our paper deals essentially with the need to combine certain approaches centered on artificial intelligence with classical tools.