Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network
Mai Ngoc Anh, Duong Xuan Bien
Abstract
This study presents the construction of a Vietnamese voice recognition module and inverse kinematics control of a redundant manipulator by using artificial intelligence algorithms. The first deep learning model is built to recognize and convert voice information into input signals of the inverse kinematics problem of a 6-degrees-of-freedom robotic manipulator. The inverse kinematics problem is solved based on the construction and training. The second deep learning model is built using the data determined from the mathematical model of the system’s geometrical structure, the limits of joint variables, and the workspace. The deep learning models are built in the PYTHON language. The efficient operation of the built deep learning networks demonstrates the reliability of the artificial intelligence algorithms and the applicability of the Vietnamese voice recognition module for various tasks.