The Design and Optimization Technology of Propulsion Systems for Operative Underwater Robots

Research Article
Open access

The Design and Optimization Technology of Propulsion Systems for Operative Underwater Robots

Guo Chen 1*
  • 1 College of Engineering, Ocean University of China, Qingdao, 266000, China    
  • *corresponding author chenguo@stu.ouc.edu.cn
ACE Vol.161
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-155-6
ISBN (Online): 978-1-80590-156-3

Abstract

The importance of underwater robots is evident in ocean exploration, resource development, and environmental monitoring. However, the harsh underwater environment requires higher efficiency, stability, and intelligence from their propulsion systems. The challenges faced by operational underwater robots today include low propulsion efficiency, poor adaptability to extreme environments, and a lack of sufficient autonomous control capabilities. To address these issues, this paper reviews the definition, requirements, core technologies, and key performance indicators of underwater robot propulsion systems by analyzing relevant literature from 2016 to 2024. It emphasizes optimization strategies aimed at enhancing propulsion efficiency, fault diagnosis and identification, reliability, durability, and adaptive control. Besides, it summarizes the current technical challenges and provides a reference for subsequent research. The results show that optimizing the propulsion system of operational underwater robots relies primarily on bionic design, new materials, adaptive control, deep learning, and fault diagnosis technologies to enhance propulsion efficiency, stability, durability, and environmental adaptability. However, optimizing the propulsion system involves challenges such as energy control, cost, and multi-objective optimization. Future research should prioritize efficient, low-energy propulsion, multi-modal perception, and intelligent adaptive control to advance underwater robot technology.

Keywords:

Operational underwater robot, Propulsion system, Fault diagnosis identification, Adaptive control

Chen,G. (2025). The Design and Optimization Technology of Propulsion Systems for Operative Underwater Robots. Applied and Computational Engineering,161,17-23.
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References

[1]. Wang, Z.K., et al. (2019) A review of underwater robot propulsion system. Pearl River Water Transport, 14: 84-85.

[2]. Du, X.Q. (2021) Research on propulsion performance and posture tracking control of UPR-UPU-UR vector propulsion mechanism. Shandong University.

[3]. Zhang, J.W., et al. (2021) Application status and development trend of underwater propulsion. Ship Engineering, 43(06): 61-65+78.

[4]. Zhang, R. Shen, Z. and Wang, Z. (2018) Ostraciiform Underwater Robot With Segmented Caudal Fin. IEEE Robotics and Automation Letters, 3(4): 2902-2909.

[5]. Jin, L., Liang, H. and Yang, C. (2021) Sonar image recognition of underwater target based on convolutional neural network. Journal of Northwestern Polytechnical University, 39(2): 285-291.

[6]. Sun, Z. and Lv, Y. (2022) Underwater attached organisms intelligent detection based on an enhanced YOLO. 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 1118-1122.

[7]. Ji, D., et al. (2021) Model-free fault diagnosis for autonomous underwater vehicles using sequence convolutional neural network. Ocean Engineering,, 232: 108874.

[8]. Zhang, Z., et al. (2023) Fatigue‐resistant conducting polymer hydrogels as strain sensor for underwater robotics. Advanced Functional Materials, 33(42): 2305705.

[9]. Zhang, H. and Guo, Z. (2023) Recent advances in self-healing superhydrophobic coatings. Nano Today, 51.

[10]. Hou, Y.K., et al. (2023) Robust adaptive finite-time tracking control for Intervention-AUV with input saturation and output constraints using high-order control barrier function. Ocean Engineering, 268: 113219.

[11]. Zhang, Y.Q. (2024) Research on anti-water flow trajectory tracking control based on meta-learning and adaptation. Jilin University.

[12]. Aldhaheri, S., et al. (2022) Underwater robot manipulation: Advances, challenges and prospective ventures. Oceans 2022-Chennai. IEEE, 1-7.

[13]. Xu, J.Q. (2021) Overview of the technology of permanent magnet propulsion system for underwater robots. Robot Industry, 04: 58-63.

[14]. Cai, W., et al. (2023) Cooperative Artificial Intelligence for underwater robotic swarm. Robotics and Autonomous Systems, 164: 104410.

Cite this article

Chen,G. (2025). The Design and Optimization Technology of Propulsion Systems for Operative Underwater Robots. Applied and Computational Engineering,161,17-23.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MSS 2025 Symposium: Automation and Smart Technologies in Petroleum Engineering

ISBN：978-1-80590-155-6(Print) / 978-1-80590-156-3(Online)
Editor：Mian Umer Shafiq
Conference website: https://2025.confmss.org
Conference date: 21 May 2025
Series: Applied and Computational Engineering
Volume number: Vol.161
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Wang, Z.K., et al. (2019) A review of underwater robot propulsion system. Pearl River Water Transport, 14: 84-85.

[2]. Du, X.Q. (2021) Research on propulsion performance and posture tracking control of UPR-UPU-UR vector propulsion mechanism. Shandong University.

[3]. Zhang, J.W., et al. (2021) Application status and development trend of underwater propulsion. Ship Engineering, 43(06): 61-65+78.

[4]. Zhang, R. Shen, Z. and Wang, Z. (2018) Ostraciiform Underwater Robot With Segmented Caudal Fin. IEEE Robotics and Automation Letters, 3(4): 2902-2909.

[5]. Jin, L., Liang, H. and Yang, C. (2021) Sonar image recognition of underwater target based on convolutional neural network. Journal of Northwestern Polytechnical University, 39(2): 285-291.

[6]. Sun, Z. and Lv, Y. (2022) Underwater attached organisms intelligent detection based on an enhanced YOLO. 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 1118-1122.

[7]. Ji, D., et al. (2021) Model-free fault diagnosis for autonomous underwater vehicles using sequence convolutional neural network. Ocean Engineering,, 232: 108874.

[8]. Zhang, Z., et al. (2023) Fatigue‐resistant conducting polymer hydrogels as strain sensor for underwater robotics. Advanced Functional Materials, 33(42): 2305705.

[9]. Zhang, H. and Guo, Z. (2023) Recent advances in self-healing superhydrophobic coatings. Nano Today, 51.

[10]. Hou, Y.K., et al. (2023) Robust adaptive finite-time tracking control for Intervention-AUV with input saturation and output constraints using high-order control barrier function. Ocean Engineering, 268: 113219.

[11]. Zhang, Y.Q. (2024) Research on anti-water flow trajectory tracking control based on meta-learning and adaptation. Jilin University.

[12]. Aldhaheri, S., et al. (2022) Underwater robot manipulation: Advances, challenges and prospective ventures. Oceans 2022-Chennai. IEEE, 1-7.

[13]. Xu, J.Q. (2021) Overview of the technology of permanent magnet propulsion system for underwater robots. Robot Industry, 04: 58-63.

[14]. Cai, W., et al. (2023) Cooperative Artificial Intelligence for underwater robotic swarm. Robotics and Autonomous Systems, 164: 104410.

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