Preview

Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova

Advanced search

Development of an autonomous vessel control system based on the ROS2 operating system

https://doi.org/10.21821/2309-5180-2025-17-1-105-114

EDN: MKJPRZ

Abstract

The research focuses on the development of a control system for an autonomous vessel using the Robot Operating System 2 (ROS2). Autonomous vessels are highlighted as a promising class of robotic systems designed for various tasks in the maritime environment, including area monitoring, scientific research, search and rescue, and transport operations. Their key advantages lie in their autonomy, operational flexibility, and ability to operate in conditions hazardous to humans. The study explores the use of the modern Robot Operating System 2 for robotics, which ensures modularity, scalability, and a high degree of distributed computing. This software provides ready-made algorithms for navigation, sensor data processing, and motion control, as well as tools for testing, debugging, and data visualization, thereby accelerating development and enhancing system reliability. As part of the research, a structural diagram of the autonomous vessel control system was developed, incorporating subsystems for navigation, computer vision, motion control, and operator interaction via a shore-based console, illustrating the composition and interaction of individual operating system nodes. A device diagram is presented, showing the distribution of computing resources between the nodes. The proposed control system demonstrates the possibility of integrating various autonomous vessel subsystems into a unified system based on the common interaction logic and data exchange of the Robot Operating System 2. The application of this operating system enhances development speed, improves software reliability, simplifies the integration of complex algorithms, and ensures system flexibility for future improvements.

About the Authors

A. A. Dyda
Maritime State University named after Admiral G.I. Nevelskoy
Russian Federation

Dyda Aleksandr A. - Doctor of Technical Sciences, Professor of the Department of Automatic and Information Systems

690059, Russia, Vladivostok, st. Verkhneportovaya, 50a



I. I. Pushkarev
"CASHALOT" LLC
Russian Federation

Pushkarev Igor I. - lead software engineer

690091, Vladivostok, st. Uborevicha 7, room IX



References

1. Barrera, C., I. Padron, F. S. Luis and O. Llinas. “Trends and challenges in unmanned surface vehicles (USV): From survey to shipping.” TransNav: International Journal on Marine Navigation and Safety of Sea Transportation Vol. 15 No. 1 (2021): 135–142.

2. Karetnikov, V. V., I. V. Paschenko and A. I. Sokolov. “Prospects of introducing unmanned navigation on inland waterways of the Russian Federation.” Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota im. admirala S. O. Makarova 9.3 (2017): 619–627. DOI: 10.21821/2309-5180-2017-9-3-619-627.

3. Bonci, A., F. Gaudeni, M. C. Giannini and S. Longhi. “Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey.” Applied Sciences 13.23 (2023). DOI: 10.3390/app132312796.

4. Pushkarev, I. I. “A control system for movement and divergence of unmanned ship according to COLREG.” Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota im. admirala S. O. Makarova 14.6 (2022): 837–848. DOI: 10.21821/2309-5180-2022-14-6-837-848.

5. Julier, S. J. and J. K. Uhlmann. “Unscented filtering and nonlinear estimation.” Proceedings of the IEEE 92.3 (2004): 401–422. DOI: 10.1109/JPROC.2003.823141.

6. Moore, T. and D. Stouch. “A Generalized Extended Kalman Filter Implementation for the Robot Operating System.” Intelligent Autonomous Systems 13Springer International Publishing, 2016: 335–348.

7. Fedorenko, R. and B. Gurenko. “Local and Global Motion Planning for Unmanned Surface Vehicle.” MATEC Web of Conferences 42 (2016): 01005. DOI: 10.1051/matecconf/20164201005.

8. Alsadik, B. and S. Karam. “The Simultaneous Localization and Mapping (SLAM)-An Overview_2021.” Journal of Applied Science and Technology Trends 2.02 (2021): 147–158. DOI: 10.38094/jastt204117.

9. Hu, S., S. Tian, J. Zhao and R. Shen. “Path Planning of an Unmanned Surface Vessel Based on the Improved A-Star and Dynamic Window Method.” Journal of Marine Science and Engineering 11.5 (2023). DOI: 10.3390/jmse11051060.

10. Belsare, K. et al. “Micro-ROS.” Robot Operating System (ROS): The Complete Reference (Volume 7) Springer International Publishing, 2023: 3–55. DOI: 10.1007/978–3–031–09062–2_2.

11. Smolentsev, S. V. et al. “Algorithm for analyzing the automatic identification system data to identify typical scenarios for vessel divergence and testing the systems of autonomous shipping.” T-Comm 18.3 (2024): 50–59. DOI: 10.36724/2072-8735-2024-18-3-50-59.

12. Lu, Y., C. Hancock, et al. “Fusion of Camera-based Vessel Detection and AIS for Maritime Surveillance.” 2021 26th International Conference on Automation and Computing (ICAC)2021: 1–6. DOI: 10.23919/ ICAC50006.2021.9594203.

13. Wang, M. et al. “Fusion Detection Algorithm of Maritime Radar and Electro-Optical Pod for Complex Sea Conditions.” 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP)2023: 1204–1209. DOI: 10.1109/ICMSP58539.2023.10170950.


Review

For citations:


Dyda A.A., Pushkarev I.I. Development of an autonomous vessel control system based on the ROS2 operating system. Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova. 2025;17(1):105-114. (In Russ.) https://doi.org/10.21821/2309-5180-2025-17-1-105-114. EDN: MKJPRZ

Views: 146


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2309-5180 (Print)
ISSN 2500-0551 (Online)