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<p>In the context of mobile robotics education, realistic and accessible datasets are fundamental for supporting the development and testing of algorithms. However, collecting real-world data is a limited and challenging task because it is time-consuming and error-prone. Therefore, this paper presents the generation of a synthetic dataset through realistic simulation using the SimTwo environment—a physics-based simulator, and modeling techniques of sensors and actuators. The physical and simulated mobile robot was developed to perform tasks such as following a line, following a wall, and avoiding obstacles. The proposed approach facilitates the creation of customized datasets for training and evaluation algorithms while supporting remote and inclusive learning. Results show that a simulated dataset can effectively replicate real-world behaviors, making them a valuable resource for educational contexts, research, and development. Some emergent machine learning algorithms can be applied to this dataset, being this approach increasingly used to enhance robot localization, by leveraging ML, robots can improve the accuracy, robustness, and adaptability of their localization systems, especially in complex and dynamic environments.</p>

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Realistic simulation for dataset generation in a mobile robotics educational context

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Laiany Brancaliao,
  • Mariano Alvarez,
  • J. A. B. Coelho,
  • Miguel Conde,
  • Paulo Costa,
  • Jose Goncalves

摘要

In the context of mobile robotics education, realistic and accessible datasets are fundamental for supporting the development and testing of algorithms. However, collecting real-world data is a limited and challenging task because it is time-consuming and error-prone. Therefore, this paper presents the generation of a synthetic dataset through realistic simulation using the SimTwo environment—a physics-based simulator, and modeling techniques of sensors and actuators. The physical and simulated mobile robot was developed to perform tasks such as following a line, following a wall, and avoiding obstacles. The proposed approach facilitates the creation of customized datasets for training and evaluation algorithms while supporting remote and inclusive learning. Results show that a simulated dataset can effectively replicate real-world behaviors, making them a valuable resource for educational contexts, research, and development. Some emergent machine learning algorithms can be applied to this dataset, being this approach increasingly used to enhance robot localization, by leveraging ML, robots can improve the accuracy, robustness, and adaptability of their localization systems, especially in complex and dynamic environments.