Control Of Industrial Robots Based On Artificial Intelligence

Industrial robots are vital in developing smart factories, creating the need for more efficient and modern control systems. As a result, investigators and scholars are dedicating great effort to advancing this field et al. [27]. Literature showcases significant progress in various areas, including the control of articulated arms and advancements in human-robot interfaces, self-decision-making, object recognition, decision-making, and routing planning. This manuscript describes a novel technique for predicting the movement of a robotic arm based on artificial neural networks. We have implemented an artificial intelligence method based on artificial neural networks to analyze the possible routing of a robotic arm with the configuration of three degrees of freedom (3-DOF), solving the direct kinematics problem without manually calculating and solving mathematical equations or using large computational centers. By training the artificial intelligence, it can identify and provide the correct solution from a given dataset. The AI was developed in Python using the TensorFlow library but utilizing the Industrial Internet of Things (IIoT), as it uses cloud computing power through Google Colab. The system uses a dataset that collects information from 2,162 angle combinations, which feed and train the artificial neural network. The Case Study features a virtual robot with 3-DOF, with defined dimensions for the base and two arms involving the robot parts. We can also vary the theoretical movement angles of each robot element, i.e., at each joint, thus allowing us to simulate a range of angle combinations and obtain the end-effector's position using the Denavit-Hartenberg methodology. The simulation helped us to train the previously described artificial intelligence, which later will find the end-effector's position without the traditional control method. This research demonstrated how artificial neural networks can help develop control strategies and increase robotics efficiency.