Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

2025-10-30

AI Summary

To address poor robot adaptability and the inability of conventional control methods to respond in real time within dynamic environments—such as smart cities and precision agriculture—this paper proposes a digital twin–enabled autonomous dynamic reconfiguration framework. The framework constructs a high-fidelity virtual twin that tightly integrates real-time environmental perception, online path replanning, and remote controller parameter updates, enabling closed-loop autonomous optimization of physical robot motion policies. Its key innovation lies in the first deep integration of digital twin technology into the core robot control loop, supporting millisecond-level, human-in-the-loop-free adaptive reconfiguration. Experimental evaluations in urban surveillance and farmland inspection scenarios demonstrate a 32% improvement in task success rate and a 41% reduction in trajectory tracking error. The framework significantly enhances robustness, autonomy, and scalability in complex intelligent environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Autonomous reconfiguration of robotic controllers in dynamic environments
• Overcoming traditional systems' slow adaptation to changing conditions
• Using Digital Twins to optimize robot trajectories without manual intervention

Innovation

• Digital Twin enables autonomous robotic reconfiguration
• Virtual replica simulates optimized movement trajectories
• Automated code deployment ensures rapid environmental adaptation