The week's pick
Random Articles
Reseach Article
The Contributions of AI Usage Over AUVs’ Path-Following and Path-Planning
by
Abeer Ali Sirelkhatim,
Mustafa Osman Ali,
Bei Peng
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 28 |
| Year of Publication: 2025 |
| Authors: Abeer Ali Sirelkhatim, Mustafa Osman Ali, Bei Peng |
10.5120/ijca2025925489
|
Abeer Ali Sirelkhatim, Mustafa Osman Ali, Bei Peng . The Contributions of AI Usage Over AUVs’ Path-Following and Path-Planning. International Journal of Computer Applications. 187, 28 ( Aug 2025), 66-75. DOI=10.5120/ijca2025925489
@article{
10.5120/ijca2025925489,
author = {
Abeer Ali Sirelkhatim,
Mustafa Osman Ali,
Bei Peng
},
title = { The Contributions of AI Usage Over AUVs’ Path-Following and Path-Planning },
journal = {
International Journal of Computer Applications
},
issue_date = { Aug 2025 },
volume = { 187 },
number = { 28 },
month = { Aug },
year = { 2025 },
issn = { 0975-8887 },
pages = {
66-75
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume187/number28/the-contributions-of-ai-usage-over-auvs-path-following-and-path-planning/
},
doi = { 10.5120/ijca2025925489 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-20T21:35:01+05:30
%A Abeer Ali Sirelkhatim
%A Mustafa Osman Ali
%A Bei Peng
%T The Contributions of AI Usage Over AUVs’ Path-Following and Path-Planning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 28
%P 66-75
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The increasing of interesting in exploration of the underwater environment unknown and complexity, accentuated the need of the underwater vehicles with trusted robust control systems. Over a few pasts dedicates, much of researchers and scholars shown a huge racing for designing and implementing navigation systems supporting autonomy for underwater vehicles. This paper will explore the basic concepts of Autonomous Underwater Vehicle (AUV) control systems and terms. Two different criteria of algorithms for AUVs' path trajectory will discussed and explained (Path Following and Path Planning). Also, some path trajectory algorithms which have been designed with aiding of AI techniques are discussed through this research work; Where the study shows the similarities and differences between different types, and then assesses the benefits gained from the use of AI technology.
References
- Zhengzhong Chu, Fulun Wang, and Tingjun Lei, “Path Planning Based on Deep Reinforcement Learning for Autonomous Underwater Vehicles Under Ocean Current Disturbance,” IEEE Transactions on Intelligent Vehicles, vol. 8, NO. 1, pp 108-120, Jan. 2023.
- Wenjie Shi, Shji Song, Cheng Wu, and Philip Chen, “Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, No. 12, pp 3534-3546, Dec 2019.
- Yexin Fan, Hongyang Dong, Xiaowei Zhao, and Peter Denissenko, “Path*Following Control of Unmanned Underwater Vehicle Based on an Improved TD3 Deep Reinforcement Learning,” in Process IEEE Transactions On Control Systems Technology, vol 32, pp 1904-1919, Mar 2024.
- Yuan Fang, Zhenwei Huang, Jinyun Pu, and Jinsong Zhang, “AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method,” Ocean Engineering, vol 245, Feb 2022.
- Qilei Zhang, Jinying Lin, Qinxin Sha, Bo He, and Guangliang Li, “Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle,” IEEE Access, vol 8, pp 24258-24268, Jan 2020.
- Mohammed Imran Chowdhury, and Daniel G. Schwartz, “UUV On-Board Path Replanning using PRM-A,” IEEE Global Oceans 2020: Singapore – U.S. Gulf Coast, Apr 2021.
- Jian Wang, Zhengxing Wu, Shuaizheng, Min Tan, and Junzhi Yu, “Real-Time Path Planning and Following of a Gliding Robotic Dolphin Within a Hierarchical Framework,” IEEE Transactions on Vehicular Technology, vol. 70, No. 4, pp 3243-3255, Apr 2021.
- Juan Li, Duorui Xue, and Jainxin Zhang, “Multi-UUV Formation Coordination Control Based on Combination of Virtual Structure and Leader,” IEEE International Conference on Mechatronics and Automation (ICMA), pp 1574-1579, Oct 2018.
- L. Lapierre, D. Soetanto, and A. Pascoal, “Nonlinear Path Following with Applications to the Control of Autonomous Underwater Vehicles,” 42nd IEEE International Conference on Decision and Control, pp1258-1261, Mar 2004.
- Jiwei Hu, Bo Jin, Huiping Li, Wesheng Yan, Mingyong Liu, and Rongxin Cui, “A DMPC-Based Approach to Circular Cooperative Path-Following Control of Unmanned Underwater Vehicles,” IEEE 28th International Symposium on Industrial Electronics, pp 1207-1212, Jun 2019.
- Zongyu Zuo, Jiawei Song, and Qing-Long Han, “Coordinated Planner Path-Following Control for Multiple Nonholonomic Wheeled Mobile Robots,” IEEE Transactions on Cybernetics, vol. 52, No. 9, pp 9404-9413, Sep 2022.
- Jintao Su, Jianping Lou, and Xiaolu Jiang, “Overview of intelligent vehicle core technology and development,” IOP Conf. Series: Earth and Environmental Science 769, 2021.
- Carig W. Reynolds, “Steering Behaviors for Autonomous Characters,” website [CteSeer]. Available: https://www.researchgate.net/publication/2495826.
- Goncalo Neto, “From Single-Agent to Multi-Agent Reinforcement Learning: Foundational Concepts and Methods,” Neto G. Learning theory course, 2005. Available: https://users.cs.utah.edu/~tch/CS6380/resources/Neto-2005-RL-MAS-Tutorial.pdf.
- Brendan Gogarty, and Isabel Robinson, “Unmanned Vehicles: A (Rebooted) History, Background and Current State of the Art,” Journal of Law and Information Science, vol 21, pp 1-34, 2011.
- Kiruthika D, and Jeevishaa S, “Unmanned Vehicles: Navigating the Environmental Implications,” JSS Journal for Legal Syudies and Research, pp 178-193, 2024.
- Oscar Silva, Ruben Cordera, Esther Gonzalez, and Soledad Nogues, “Environmental impacts of autonomous vehicles: A review of the scientific literature,” Elsevier-Science of The Total Environment, vol 830, pp 1-11, Jul 2022.
- Richard Nunno, “Autonomous Vehicles: State of the Technology and Potential Role as a Climate Solution,” Jun 2021, Available: www.eesi.org/papers.
- Syed Agha Hassnain Mohsan, Nawaf Qasem Hamood Othman, Yanlong Li, Mohammed H. Alsharirif, and Muhammad Asghar Khan, “Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends,” Journal of Intelligent Service Robotics, vol 16, pp 109-137, Mar 2023.
- Michael D. Waston, Stephen B. Johnson, and Luis Trevino, “System Engineering of Autonomous Space Vehicles,” IEEE-International Conference on Prognostics and Health Management, Feb 2015.
- P. Amadieu, G. Beckwith, B. Dore, J. P. Bouchery, and V. Pery, “Automated Transfer Vehicle (ATV) Structural and Thermal Model Testing at ESTEC,” esa bulletin, vol 111, pp 95-104, Aug 2002.
- Salimzhan A. Gafurov, and Evgeniy V. Klochkov, “Autonomous Unmanned Underwater Vehicles Development Tendencies,” Elsevier-Procedia Engineering, vol 106, pp 141-148, 2015.
- Simon Watson, Daniel A. Duecker, and Keir Groves, “Localisation of Unmanned Underwater Vehicles (UUVs) in Complex and Confined Environments: A Review,” Sensors 2020, 20(21), 6203; https://doi.org/10.3390/s20216203, Oct 2020.
- Yexin Fan, Hongyang Dong, Xiaowei Zhao, and Petr Denissenko, “Path-Following Control of Unmanned Underwater Vehicle Based on an Improved TD3 Deep Reinforcement Learning,” IEEE Transactions on Control Systems Technology, vol 32, pp 1904-1919, Sep 2024.
- Behnaz Hadi, Alireza Khosravi, and Pouria Sarhadi, “Deep reinforcement learning for adaptive path planning and control of Autonomous Underwater Vehicle,” Elsevier-Applied Ocean Research, vol 129, Dec 2022.
- Runsheng Yu, Zhenyu Shi, Chaoxing Huang, Tenglong Li, and Qiongxiong ma, “Deep Reinforcement Learning Based Optimal Trajectory Tracking Control of Autonomous Underwater Vehicle,” 36th Chinese Control Conference (CCC), Sep 2017.
- Jens Kober, J. Andrew Bagnell, and Jan Peter, “Reinforcement Learning in Robotics: A Survey,” International Journal of Robotics Research, vol 32, issue 11, pp 1238-1274, Sep 2013.
- Jurgen Schmidhuber, “Deep learning in neural networks: An overview,” Elsevier-Neural Networks, vol 61, pp 85-117, Jan 2015.
- Faraz Ahmed, Tom Creutz, Christian Ernst Siegfried Koch, and Bilal Wehbe, “A Deep-Learning Approach for Visual Detection of an AUV Docking Station,” IEEE: OCEANS 2024 - Halifax, Sep 2024.
Index Terms
Computer Science
Information Sciences
Keywords