Abstract
Design and operation of Autonomous Underwater Vehicles (AUVs) are relatively new topics, and building a functional one often requires an iterative approach. Because of the risk of damaging costly hardware, the limited availability of infrastructure suitable for testing AUVs, and the significant time investment required, it is often impractical to conduct thorough live experiments for every incremental change. In this paper, the benefits of using a simulated environment for running software tests, training neural networks, and testing the AUV’s performance in arbitrary scenarios are explored. By utilizing Unity3D, a cross-platform game engine, a customized framework was developed, allowing for setting up environments for simulating various aspects of underwater operation such as buoyancy and caustics. This framework supports communication with AUV systems (collecting observations through simulated sensors and controlling simulated actuators), gathering datasets for offline training, and randomizing parts of the environment to test the system’s robustness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Karachevtseva, I.P., Kokhanov, A.A., Zubarev, A.E., Rodionova, Z.F., Matveev, E.V., Garov, A.S.: Modern methodology and new tools for planetary mapping. In: Gartner, G., Jobst, M., Huang, H. (eds.) Progress in Cartography. Lecture Notes in Geoinformation and Cartography. Springer, Cham (2016)
Petillot, Y.R., Antonelli, G., Casalino, G., Ferreira, F.: Underwater robots: from remotely operated vehicles to intervention-autonomous underwater vehicles. IEEE Robot. Autom. Mag. 26(2), 94–101 (2019). https://doi.org/10.1109/MRA.2019.2908063
Barrett, N., Seiler, J., Anderson, T., Williams, S., Nichol, S., Nicole Hill, S.: Autonomous Underwater Vehicle (AUV) for mapping marine biodiversity in coastal and shelf waters: implications for marine management. In: OCEANS’10 IEEE SYDNEY, pp. 1–6 (2010). https://doi.org/10.1109/OCEANSSYD.2010.5603860
Manley, J.E.: Autonomous underwater vehicles for Ocean Exploration. In: Oceans 2003. Celebrating the Past... Teaming Toward the Future (IEEE Cat. No. 03CH37492), vol. 1, pp. 327–331 (2003). https://doi.org/10.1109/OCEANS.2003.178578
Mindell, D., Bingham, B.: New archaeological uses of autonomous underwater vehicles. In: MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No. 01CH37295), vol. 1, pp. 555–558 (2001). https://doi.org/10.1109/OCEANS.2001.968781
Senke, W.: Applications of autonomous underwater vehicles (AUVs) in ocean mining exploration. In: 2013 OCEANS - San Diego, pp. 1–3 (2013). https://doi.org/10.23919/OCEANS.2013.6740944
Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), vol. 3, pp. 2149–2154 (2004). https://doi.org/10.1109/IROS.2004.1389727
Todorov, E., Erez, T., Tassa, Y.: MuJoCo: a physics engine for model-based control. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026–5033 (2012). https://doi.org/10.1109/IROS.2012.6386109
Rohmer, E., Singh, S.P.N., Freese, M.: CoppeliaSim (formerly V-REP): a Versatile and Scalable Robot Simulation Framework. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013. www.coppeliarobotics.com
Technologies, U.: Unity - Manual: Unity Manual [online]. Docs.unity3d.com (2005). Available at: http://docs.unity3d.com/Manual/index.html. Accessed 10 Feb 2022
Yang, K., Jie, J., Haihui, S.: Study on the virtual natural landscape walkthrough by using Unity 3D. In: 2011 IEEE International Symposium on VR Innovation, pp. 235–238 (2011). https://doi.org/10.1109/ISVRI.2011.5759642
Kuang, Y., Bai, X.: The research of virtual reality scene modeling based on Unity 3D. In: 2018 13th International Conference on Computer Science & Education (ICCSE), pp. 1–3 (2018). https://doi.org/10.1109/ICCSE.2018.8468687
Yong-kang, J., Yong, C., Daquan, T.: Design of an UAV simulation training and assessment system based on Unity3D. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), pp. 163–167 (2017). https://doi.org/10.1109/ICUS.2017.8278334
Xueying, L., Shi, M., Li, C.: Design and implement of swarm self-organization model based on Unity3D. In: 2017 IEEE 2nd Information Technology, Networking. Electronic and Automation Control Conference (ITNEC), pp. 146–149 (2017). https://doi.org/10.1109/ITNEC.2017.8284926
Song, K., Xu, H., Ding, Y., Li, H.: A target tracking realization method of UAV simulation training system. In: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1–4 (2016). https://doi.org/10.1109/ICARCV.2016.7838616
Nie, Y., Luan, X., Gan, W., Ou, T., Song, D.: Design of marine virtual simulation experiment platform based on Unity3D. In: Global Oceans 2020: Singapore–U.S. Gulf Coast 2020, pp. 1–5 (2020). https://doi.org/10.1109/IEEECONF38699.2020.9389006
Li, D.: Application of 3D virtual ocean image simulation in ship vision. J. Coastal Res. 94, 530 (2019)
Chen, Ge., et al.: Design and implementation of a 3D ocean virtual reality and visualization engine. J. Ocean Univ. China 11(004), 481–487 (2012)
Karthi, M., Muthulakshmi, V., Priscilla, R., Praveen, P., Vanisri, K.: Evolution of YOLO-V5 algorithm for object detection: automated detection of library books and performance validation of dataset. In: 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp. 1–6 (2021). https://doi.org/10.1109/ICSES52305.2021.9633834
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Szlęg, P., Barczyk, P., Maruszczak, B., Zieliñski, S., Szymañska, E. (2023). Simulation Environment for Underwater Vehicles Testing and Training in Unity3D. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_56
Download citation
DOI: https://doi.org/10.1007/978-3-031-22216-0_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22215-3
Online ISBN: 978-3-031-22216-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)