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Teaching ROS1/2 and Reinforcement Learning using a Mobile Robot and its Simulation

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ROBOT2022: Fifth Iberian Robotics Conference (ROBOT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 589))

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Abstract

Robotics is an ever-growing field, used in countless applications, from domestic to industrial, and taught in advanced courses of multiple higher education institutions. Robot Operating System (ROS), the most prominent robotics architecture, integrates several of these, and has recently moved to a new iteration in the form of ROS2. This project aims to design a complete educational package meant for teaching intelligent robotics in ROS1 and ROS2. A foundation for the package was constructed, using a small differential drive robot equipped with camera-based virtual sensors, a representation in the Flatland simulator, and introductory lessons to both ROS versions and Reinforcement Learning (RL) in robotics. To evaluate the package’s pertinence, expected learning outcomes were set and the lessons were tested with users from varying backgrounds and levels of robotics experience. Encouraging results were obtained, especially in the ROS1 and ROS2 lessons, while the feedback from the RL lesson provided clear indications for future improvements. Therefore, this work provides solid groundwork for a more comprehensive educational package on robotics and ROS.

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Notes

  1. 1.

    https://docs.ros.org/en/rolling/Governance.html.

  2. 2.

    https://wiki.ros.org/ROS/Tutorials.

  3. 3.

    https://cyberbotics.com/.

  4. 4.

    https://www.waveshare.com/wiki/AlphaBot2.

  5. 5.

    https://www.lego.com/en-us/themes/mindstorms/about.

  6. 6.

    https://wiki.ros.org/ROS/Tutorials.

  7. 7.

    http://wiki.ros.org/Distributions.

  8. 8.

    http://docs.ros.org/en/galactic/index.html.

  9. 9.

    https://github.com/avidbots/flatland.

  10. 10.

    https://github.com/jorgef1299/SERP.

  11. 11.

    https://github.com/jorgef1299/SERP.

  12. 12.

    https://github.com/BerserkingIdiot/flatland_quick_start_ros1.

  13. 13.

    https://github.com/BerserkingIdiot/flatland_quick_start_ros2.

  14. 14.

    https://github.com/BerserkingIdiot/drl_local_planner_ros_stable_baselines.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Porto (FEUP), 4200-465, Porto, Portugal

    Vítor Ventuzelos, Gonçalo Leão & Armando Sousa

  2. Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465, Porto, Portugal

    Gonçalo Leão & Armando Sousa

Authors
  1. Vítor Ventuzelos
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  2. Gonçalo Leão
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  3. Armando Sousa
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Corresponding author

Correspondence to Vítor Ventuzelos .

Editor information

Editors and Affiliations

  1. Centro Universitario de la Defensa (CUD), Zaragoza, Spain

    Danilo Tardioli

  2. Grupo de Robótica, Universidad de León, León, Spain

    Vicente Matellán

  3. GRVC Robotics Lab, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain

    Guillermo Heredia

  4. School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Manuel F. Silva

  5. Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal

    Lino Marques

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Ventuzelos, V., Leão, G., Sousa, A. (2023). Teaching ROS1/2 and Reinforcement Learning using a Mobile Robot and its Simulation. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 589. Springer, Cham. https://doi.org/10.1007/978-3-031-21065-5_48

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