Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments
This work addresses inefficiencies and operational failures in robotic systems for smart environments, offering a scalable solution for enhancing autonomy, though it appears incremental in integrating existing Digital Twin technology.
The paper tackles the problem of robotic systems struggling to adapt in dynamic smart environments like smart cities and precision farming, proposing a Digital Twin-based framework that enables autonomous reconfiguration of controllers for rapid adaptation without manual intervention.
Robotic systems have become integral to smart environments, enabling applications ranging from urban surveillance and automated agriculture to industrial automation. However, their effective operation in dynamic settings - such as smart cities and precision farming - is challenged by continuously evolving topographies and environmental conditions. Traditional control systems often struggle to adapt quickly, leading to inefficiencies or operational failures. To address this limitation, we propose a novel framework for autonomous and dynamic reconfiguration of robotic controllers using Digital Twin technology. Our approach leverages a virtual replica of the robot's operational environment to simulate and optimize movement trajectories in response to real-world changes. By recalculating paths and control parameters in the Digital Twin and deploying the updated code to the physical robot, our method ensures rapid and reliable adaptation without manual intervention. This work advances the integration of Digital Twins in robotics, offering a scalable solution for enhancing autonomy in smart, dynamic environments.