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Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

Angelos Alexopoulos, Agorakis Bompotas, Nikitas Rigas Kalogeropoulos, Panagiotis Kechagias, Athanasios P. Kalogeras, Christos Alexakos

TL;DR

The paper addresses autonomous reconfiguration of robotic controllers in dynamic smart environments by introducing a Digital Twin–driven framework that closes the loop between virtual simulation and physical execution. It leverages a domain-specific language and AutomationML to configure a Unity-based DT and uses ROS, MoveIt!, and OMPL to compute and deploy updated trajectories, enabling real-time adaptation to topology and environmental changes. A robotic arm use case demonstrates real-time synchronization, trajectory re-planning, and operator-approved deployment, validating the closed-loop integration of DT and physical systems. The work highlights potential for AI-driven autonomy and scalability to large, heterogeneous smart environments such as cities and automated manufacturing plants.

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.

Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

TL;DR

The paper addresses autonomous reconfiguration of robotic controllers in dynamic smart environments by introducing a Digital Twin–driven framework that closes the loop between virtual simulation and physical execution. It leverages a domain-specific language and AutomationML to configure a Unity-based DT and uses ROS, MoveIt!, and OMPL to compute and deploy updated trajectories, enabling real-time adaptation to topology and environmental changes. A robotic arm use case demonstrates real-time synchronization, trajectory re-planning, and operator-approved deployment, validating the closed-loop integration of DT and physical systems. The work highlights potential for AI-driven autonomy and scalability to large, heterogeneous smart environments such as cities and automated manufacturing plants.

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.

Paper Structure

This paper contains 11 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Background & Related Work
  3. Robotic Simulation Platforms and Digital Twin Environments
  4. Trajectory Planning in Smart Robotic Systems
  5. Robotics Reconfiguration
  6. Digital Twin Driven Approach
  7. Setting up of the DT
  8. Calculating Trajectories
  9. Reconfiguring
  10. Use Case: Reconfiguration of a Robotic Arm
  11. Conclusion

Figures (4)

  • Figure 1: Trajectory planning on Digital Twin
  • Figure 2: Reconfiguration cycle
  • Figure 3: Scene topology
  • Figure 4: Visualized Trajectory