NauSimUn simulador de código abierto para el control, desarrollo y despliegue de drones submarinos

  1. Ortiz Toro, César Antonio 1
  2. Cerrada Collado, Cristina 2
  3. David Moreno Salinas 2
  4. Chaos García , Dictino 2
  5. García Suárez , Karen Lyn 3
  6. Otero Roth, Pablo 4
  7. Vidal Pérez , Juan Manuel 5
  8. Luque Nieto, Miguel Ángel 4
  9. Vázquez , Ana Isabel 5
  10. Fraile Ardanuy, José Jesús 1
  11. Negro Valdecantos, Vicente 1
  12. Jiménez Yguacel , Eugenio 3
  13. Aranda Almansa, Joaquín 2
  14. Zazo Bello, Santiago 1
  15. Zufiria Zatarain , Pedro José 1
  16. Magdalena Layos, Luis 1
  17. Parras Moral, Juan 1
  18. Gutiérrez Martín, Alvaro 1
  1. 1 Universidad Polit´ecnica de Madrid
  2. 2 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

  3. 3 Universidad de Las Palmas de Gran Canaria
    info

    Universidad de Las Palmas de Gran Canaria

    Las Palmas de Gran Canaria, España

    ROR https://ror.org/01teme464

  4. 4 Universidad de Málaga
    info

    Universidad de Málaga

    Málaga, España

    ROR https://ror.org/036b2ww28

  5. 5 Universidad de Cádiz
    info

    Universidad de Cádiz

    Cádiz, España

    ROR https://ror.org/04mxxkb11

Show affiliations +
Journal:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Year of publication: 2024

Issue: 45

Type: Article

DOI: 10.17979/JA-CEA.2024.45.10895 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

This paper introduces NauSim, an open-source simulator for underwater drones, focusing on control software developmentand easy deployment to the target hardware. NauSim provides researchers, developers, and students with a realistic and versatile virtual testing ground, allowing them to evaluate the performance of underwater drones in a variety of scenarios. Key features include customizable scenarios, a modular design for controllers, sensors, and actuators, and support for multi-drone simulations,enabling collaborative robotics and swarm-based research.

Bibliographic References

  • Amer, A., Álvarez-Tuñón, O., Ugurlu, H. I., Sejersen, J. L. F., Brodskiy, Y., Kayacan, E., 2023. Unav-sim: A visually realistic underwater robotics simulator and synthetic data-generation framework. In: 2023 21st International Conference on Advanced Robotics (ICAR). IEEE, pp. 570–576. DOI: 10.48550/arXiv.2310.11927 DOI: https://doi.org/10.1109/ICAR58858.2023.10406819
  • Betancourt, J., Coral, W., Colorado, J., 2020. An integrated rov solution for underwater net-cage inspection in fish farms using computer vision. SN Applied Sciences 2 (12), 1946. DOI: 10.1007/s42452-020-03623-z DOI: https://doi.org/10.1007/s42452-020-03623-z
  • Cerqueira, R., Trocoli, T., Neves, G., Joyeux, S., Albiez, J., Oliveira, L., 2017. A novel gpu-based sonar simulator for real-time applications. Computers & Graphics 68, 66–76. DOI: 10.1016/j.cag.2017.08.008 DOI: https://doi.org/10.1016/j.cag.2017.08.008
  • Cheng, L., Tan, X., Yao, D., Xu, W., Wu, H., Chen, Y., 2021. A fishery water quality monitoring and prediction evaluation system for floating uav based on time series. Sensors 21 (13), 4451. DOI: 10.3390/s21134451 DOI: https://doi.org/10.3390/s21134451
  • Coumans, E., 2015. Bullet physics simulation. In: ACM SIGGRAPH 2015 Courses. p. 1. DOI: 10.1145/2776880.2792704 DOI: https://doi.org/10.1145/2776880.2792704
  • de Cerqueira Gava, P. D., Nascimento Júnior, C. L., Belchior de Franc¸a Silva, J. R., Adabo, G. J., 2022. Simu2vita: A general purpose underwater vehicle simulator. Sensors 22 (9), 3255. DOI: 10.3390/s22093255 DOI: https://doi.org/10.3390/s22093255
  • Fossen, T. I., 2011. Handbook of marine craft hydrodynamics and motion control. John Wiley & Sons. DOI: 10.1002/9781119994138 DOI: https://doi.org/10.1002/9781119994138
  • Goslin, M., Mine, M. R., 2004. The panda3d graphics engine. Computer (10), 112–114. DOI: 10.1109/MC.2004.180 DOI: https://doi.org/10.1109/MC.2004.180
  • Hu, S., Feng, A., Shi, J., Li, J., Khan, F., Zhu, H., Chen, J., Chen, G., 2022. Underwater gas leak detection using an autonomous underwater vehicle (robotic fish). Process Safety and Environmental Protection 167, 89–96. DOI: 10.1016/j.psep.2022.09.002 DOI: https://doi.org/10.1016/j.psep.2022.09.002
  • Liniger, J., Jensen, A. L., Pedersen, S., Sørensen, H., Mai, C., 2022. On the autonomous inspection and classification of marine growth on subsea structures. In: OCEANS 2022-Chennai. IEEE, pp. 1–7. DOI: 10.1109/OCEANSChennai45887.2022.9775295 DOI: https://doi.org/10.1109/OCEANSChennai45887.2022.9775295
  • Loncar, I., Obradovic, J., Krasevac, N., Mandic, L., Kvasic, I., Ferreira, F., Slosic, V., Nad, D., Miskovic, N., 2022. Marus-a marine robotics simulator. In: OCEANS 2022, Hampton Roads. IEEE, pp. 1–7. DOI: https://doi.org/10.1109/OCEANS47191.2022.9976969
  • Madeo, D., Pozzebon, A., Mocenni, C., Bertoni, D., 2020. A low-cost unmanned surface vehicle for pervasive water quality monitoring. IEEE Transactions on Instrumentation and Measurement 69 (4), 1433–1444. DOI: 10.1109/TIM.2019.2963515 DOI: https://doi.org/10.1109/TIM.2019.2963515
  • Manhaes, M. M. M., Scherer, S. A., Voss, M., Douat, L. R., Rauschenbach, T., 2016. Uuv simulator: A gazebo-based package for underwater intervention and multi-robot simulation. In: OCEANS 2016 MTS/IEEE Monterey. Ieee, pp. 1–8. DOI: 10.1109/OCEANS.2016.7761080 DOI: https://doi.org/10.1109/OCEANS.2016.7761080
  • Potokar, E., Ashford, S., Kaess, M., Mangelson, J. G., 2022. Holoocean: An underwater robotics simulator. In: 2022 International Conference on Robotics and Automation (ICRA). IEEE, pp. 3040–3046. DOI: 10.1109/ICRA46639.2022.9812353 DOI: https://doi.org/10.1109/ICRA46639.2022.9812353
  • Prats, M., Perez, J., Fern´andez, J. J., Sanz, P. J., 2012. An open source tool for simulation and supervision of underwater intervention missions. In: IEEE/RSJ international conference on Intelligent Robots and Systems. IEEE, pp. 2577–2582. DOI: 10.1109/IROS.2012.6385788 DOI: https://doi.org/10.1109/IROS.2012.6385788
  • Raschka, S., Patterson, J., Nolet, C., 2020. Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information 11 (4), 193. DOI: 10.3390/info11040193 DOI: https://doi.org/10.3390/info11040193
  • Robotics, B., 2016. Bluerov2: The world’s most affordable high-performance rov. BlueROV2 Datasheet; Blue Robotics: Torrance, CA, USA.
  • Rofallski, R., Tholen, C., Helmholz, P., Parnum, I., Luhmann, T., 2020. Measuring artificial reefs using a multi-camera system for unmanned underwater vehicles. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 43 (B2), 999–1008. DOI: 10.5194/isprs-archives-XLIII-B2-2020-999-2020 DOI: https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-999-2020
  • Smith, R., et al., 2005. Open dynamics engine.
  • Sultonov, S., 2023. Importance of python programming language in machine learning. International Bulletin of Engineering and Technology 3 (9), –30.
  • von Benzon, M., Sørensen, F. F., Uth, E., Jouffroy, J., Liniger, J., Pedersen, S., 2022. An open-source benchmark simulator: Control of a bluerov2 underwater robot. Journal of Marine Science and Engineering 10 (12), 1898. DOI: 10.3390/jmse10121898 DOI: https://doi.org/10.3390/jmse10121898
  • Wu, C.-J., 2018. 6-dof modelling and control of a remotely operated vehicle. Ph.D. thesis.
Data source: Dialnet