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.

翻译：机器人系统已成为智能环境不可或缺的组成部分，其应用涵盖城市监控、自动化农业及工业自动化等领域。然而，在动态场景（如智慧城市与精准农业）中，持续变化的地形与环境条件对其有效运行构成了挑战。传统控制系统往往难以快速适应，导致效率低下或运行故障。为克服这一局限，本文提出一种利用数字孪生技术实现机器人控制器自主动态重构的新型框架。该方法通过构建机器人操作环境的虚拟副本，模拟并优化应对现实世界变化的运动轨迹。通过在数字孪生中重新计算路径与控制参数，并将更新后的代码部署至物理机器人，本方法确保了无需人工干预的快速可靠适应。本研究推动了数字孪生在机器人领域的集成，为增强智能动态环境中的自主性提供了可扩展的解决方案。