Enter your email address
Submit
Write your message
1- Sharif University of Technology
Abstract: (19 Views)
With the rapid advancement of intelligent technologies and the growing use of unmanned systems in various land, aerial, and marine applications, research in this field has become increasingly significant. Among the most widely used autonomous marine platforms are Unmanned Surface Vehicles (USVs), which are employed in diverse operations such as hydrographic mapping of dam and lake beds, search and rescue missions, and military applications. One of the key challenges in this area is the design of intelligent path-tracking algorithms for effective obstacle avoidance. In this study, the problem of optimal path planning is addressed with the objective of minimizing energy consumption—thus increasing operational endurance—while simultaneously avoiding collisions with obstacles. The proposed path planning is performed using potential field algorithms and roadmap methods, followed by trajectory planning. The implemented methods are capable of generating collision-free trajectories for an intelligent surface vessel operating in environments with various obstacles, while minimizing both energy consumption and the traveled distance.
Type of Study: Research Paper |
Subject:
Ship Hydrodynamic
Received: 2025/08/12 | Accepted: 2025/10/13
Received: 2025/08/12 | Accepted: 2025/10/13
فایل صوتی چکیده مقاله [M4A 3885 KB] (2 Download)
References
1. Lv, C., Yu, H., Chi, J., Xu, T., Zang, H., lue Jiang, H., & Zhang, Z. (2019). A hybrid coordination controller for speed and heading control of underactuated unmanned surface vehicles system. Ocean Engineering, 176, 222-230. [DOI:10.1016/j.oceaneng.2019.02.007]
2. Palomeras, N., Vallicrosa, G., Mallios, A., Bosch, J., Vidal, E., Hurtos, N., ... & Ridao, P. (2018). AUV homing and docking for remote operations. Ocean Engineering, 154, 106-120. [DOI:10.1016/j.oceaneng.2018.01.114]
3. Xia, Y., Xu, K., Li, Y., Xu, G., & Xiang, X. (2019). Improved line-of-sight trajectory tracking control of under-actuated AUV subjects to ocean currents and input saturation. Ocean engineering, 174, 14-30. [DOI:10.1016/j.oceaneng.2019.01.025]
4. Lee, T., Kim, H., Chung, H., Bang, Y., & Myung, H. (2015). Energy efficient path planning for a marine surface vehicle considering heading angle. Ocean Engineering, 107, 118-131. [DOI:10.1016/j.oceaneng.2015.07.030]
5. Zeng, Z., Lian, L., Sammut, K., He, F., Tang, Y., & Lammas, A. (2015). A survey on path planning for persistent autonomy of autonomous underwater vehicles. Ocean Engineering, 110, 303-313. [DOI:10.1016/j.oceaneng.2015.10.007]
6. Lolla, T., Ueckermann, M. P., Yiğit, K., Haley, P. J., & Lermusiaux, P. F. (2012, May). Path planning in time dependent flow fields using level set methods. In 2012 IEEE International Conference on Robotics and Automation (pp. 166-173). IEEE. [DOI:10.1109/ICRA.2012.6225364]
7. Lolla, T., Haley Jr, P. J., & Lermusiaux, P. F. J. (2015). Path planning in multi-scale ocean flows: Coordination and dynamic obstacles. Ocean Modelling, 94, 46-66. [DOI:10.1016/j.ocemod.2015.07.013]
8. Kularatne, D., Bhattacharya, S., & Hsieh, M. A. (2016, June). Time and Energy Optimal Path Planning in General Flows. In Robotics: science and systems (pp. 1-10). [DOI:10.15607/RSS.2016.XII.047]
9. Singh, Y., Sharma, S., Sutton, R., Hatton, D., & Khan, A. (2018). A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents. Ocean Engineering, 169, 187-201. [DOI:10.1016/j.oceaneng.2018.09.016]
10. Bryson, A. E. (2018). Applied optimal control: optimization, estimation and control. Routledge. [DOI:10.1201/9781315137667]
11. Yilmaz, N. K., Evangelinos, C., Lermusiaux, P. F., & Patrikalakis, N. M. (2008). Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. IEEE Journal of Oceanic Engineering, 33(4), 522-537. [DOI:10.1109/JOE.2008.2002105]
12. Eichhorn, M. (2015). Optimal routing strategies for autonomous underwater vehicles in time-varying environment. Robotics and Autonomous Systems, 67, 33-43. [DOI:10.1016/j.robot.2013.08.010]
13. Carroll, K. P., McClaran, S. R., Nelson, E. L., Barnett, D. M., Friesen, D. K., & William, G. N. (1992, June). AUV path planning: an A* approach to path planning with consideration of variable vehicle speeds and multiple, overlapping, time-dependent exclusion zones. In Proceedings of the 1992 symposium on autonomous underwater vehicle technology (pp. 79-84). IEEE. [DOI:10.1109/AUV.1992.225191]
14. Garau, B., Bonet, M., Alvarez, A., Ruiz, S., & Pascual, A. (2009). Path planning for autonomous underwater vehicles in realistic oceanic current fields: Application to gliders in the western mediterranean sea. Journal of Maritime Research, 6(2), 5-22.
15. Garau, B., Alvarez, A., & Oliver, G. (2005, April). Path planning of autonomous underwater vehicles in current fields with complex spatial variability: an A* approach. In Proceedings of the 2005 IEEE international conference on robotics and automation (pp. 194-198). IEEE. [DOI:10.1109/ROBOT.2005.1570118]
16. Le, A. V., Prabakaran, V., Sivanantham, V., & Mohan, R. E. (2018). Modified a-star algorithm for efficient coverage path planning in tetris inspired self-reconfigurable robot with integrated laser sensor. Sensors, 18(8), 2585. [DOI:10.3390/s18082585] [PMID] []
17. Song, R., Liu, Y., & Bucknall, R. (2019). Smoothed A* algorithm for practical unmanned surface vehicle path planning. Applied Ocean Research, 83, 9-20. [DOI:10.1016/j.apor.2018.12.001]
18. Zadeh, S. M., Powers, D. M., Yazdani, A., Sammut, K., & Atyabi, A. (2016). Differential evolution for efficient AUV path planning in time variant uncertain underwater environment. arXiv preprint arXiv:1604.02523. [DOI:10.48550/arXiv.1604.02523]
19. Niu, H., Ji, Z., Savvaris, A., & Tsourdos, A. (2020). Energy efficient path planning for unmanned surface vehicle in spatially-temporally variant environment. Ocean Engineering, 196, 106766. [DOI:10.1016/j.oceaneng.2019.106766]
20. Lutz, Max, and Thomas Meurer. "Optimal trajectory planning and model predictive control of underactuated marine surface vessels using a flatness-based approach." 2021 American Control Conference (ACC). IEEE, 2021. [DOI:10.23919/ACC50511.2021.9483265]
21. Sayyadi, H., Babakhani, A. (2009). Path planning & trajectory tracking of AUVs in dynamic environments using intelligent converted solution and classical methods. Marine Engineering, 5(9), 19-34. https://dor.isc.ac/dor/20.1001.1.17357608.1388.5.9.2.0
22. Wang, Z., Li, G., & Ren, J. (2021). Dynamic path planning for unmanned surface vehicle in complex offshore areas based on hybrid algorithm. Computer Communications, 166, 49-56. [DOI:10.1016/j.comcom.2020.11.012]
23. Stutters, L., Liu, H., Tiltman, C., & Brown, D. J. (2008). Navigation technologies for autonomous underwater vehicles. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(4), 581-589. [DOI:10.1109/TSMCC.2008.919147]
24. Zhao, L., Bai, Y., & Paik, J. K. (2023). Achieving optimal-dynamic path planning for unmanned surface vehicles: A rational multi-objective approach and a sensory-vector re-planner. Ocean Engineering, 286, 115433. [DOI:10.1016/j.oceaneng.2023.115433]
25. Najari A, Seif M S. (2025). Dynamic modeling of surface vessel maneuvering based on deep learning using recurrent neural networks. marineeng; 21 (46) :80-88 http://dx.doi.org/10.61882/marineeng.21.46.7 [DOI:10.61882/marineeng.21.46.7]
26. Taheri, E., Ferdowsi, M. H., & Danesh, M. (2019). Path planning on line for an autonomous underwater robot in an almost unknown environment using local Rapidly-exploring Random Tree (RRT) method. Darya Fanon (Marine Science Journal), 6(3), 1-14. SID. (In Persian). https://civilica.com/doc/1017855
27. Zhou, X., Wu, P., Zhang, H., Guo, W., & Liu, Y. (2019). Learn to navigate: cooperative path planning for unmanned surface vehicles using deep reinforcement learning. Ieee Access, 7, 165262-165278. [DOI:10.1109/ACCESS.2019.2953326]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
International Journal of Maritime Technology is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License.