# Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments

arXiv: https://arxiv.org/abs/2511.00094 · HTML: https://mlsanity.com/paper/2511.00094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://mlsanity.com/paper/2403.06341/) (2024)
- [OpenUAV Cloud Testbed: a Collaborative Design Studio for Field Robotics](https://mlsanity.com/paper/1910.00739/) (2019)
- [Implementation Analysis of Collaborative Robot Digital Twins in Physics Engines](https://mlsanity.com/paper/2504.18200/) (2025)

## Built on by (most cited first, 1 of 1)

- [Agentic AI-enabled Semantic Commissioning of a Cognitive Digital Twin for Reconfigurable Manufacturing](https://mlsanity.com/paper/2609.09503/) (2026)

## Abstract (arXiv)

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.