Design and development of robotic technology through microcontroller system with machine learning techniques

Abstract

Through the integration of AI and IoT, the digital twin transforms industrial sectors by virtually portraying physical systems. Simulation and the application of lifecycle management improve decision-making. In this paper, a virtual prototype system a digital twin framework that integrates robotic devices is proposed. Created using debugging platforms, they track every robotic activity, supported by real-time microcontroller structural design systems. Machine learning methods are the fundamental engine of the digital twin system. Because of this connection, robotic actions may be seamlessly controlled and monitored, guaranteeing effectiveness and adaptability in changing contexts. Robotics has advanced significantly with the combination of digital twin technology, machine learning, and microcontroller systems, offering improved performance and versatility in a range of applications.