Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

TL;DR

This paper introduces a Digital Twin-based framework for autonomous reconfiguration of robotic systems, enabling rapid adaptation to environmental changes in smart environments, thereby improving efficiency and operational reliability.

Contribution

It presents a novel method that uses Digital Twins to simulate and optimize robot control parameters for dynamic environments, advancing autonomous robotic reconfiguration.

Findings

Enables quick adaptation to environmental changes

Reduces manual intervention in robot reconfiguration

Improves operational reliability in smart environments

Abstract

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.