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Volume 14
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10.3390/technologies14010007
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Vision-Based Autonomous Underwater Cleaning System Using Multi-Scale A* Path Planning

by
Erkang Chen
1,2,
Zhiqi Lin
1,
Jiancheng Chen
3,
Zhiwei Shen
1,2,*,
Peng Chen
1,2 and
Xiaofeng Fu
4
1
School of Ocean Information Engineering, Jimei University, Xiamen 361021, China
2
Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing, Xiamen 361021, China
3
Xiamen Intretech Inc., Xiamen 361026, China
4
Institute of System Science, National University of Singapore, Singapore 119615, Singapore
*
Author to whom correspondence should be addressed.
Technologies 2026, 14(1), 7; https://doi.org/10.3390/technologies14010007 (registering DOI)
Submission received: 24 November 2025 / Revised: 17 December 2025 / Accepted: 19 December 2025 / Published: 21 December 2025
(This article belongs to the Special Issue Intelligent Vision Systems for Remote Sensing and Environmental Monitoring)
Versions Notes

Abstract

Autonomous underwater cleaning in water pools requires reliable perception, efficient coverage path planning, and robust control. However, existing autonomous underwater vehicle (AUV) cleaning systems often suffer from fragmented software frameworks that limit end-to-end performance. To address these challenges, this paper proposes an integrated vision-based autonomous underwater cleaning system that combines global-camera AprilTag localization, YOLOv8-based dirt detection, and a multi-scale A* coverage path planning algorithm. The perception and planning modules run on a host computer system, while a NanoPi-based controller executes motion commands through a lightweight JSON-RPC protocol over Ethernet. This architecture ensures real-time coordination between visual sensing, planning, and hierarchical control. Experiments conducted in a simulated pool environment demonstrate that the proposed system achieves accurate localization, efficient planning, and reliable cleaning without blind spots. The results highlight the effectiveness of integrating vision, multi-scale planning, and lightweight embedded control for autonomous underwater cleaning tasks.
Keywords: autonomous underwater vehicle (AUV); vision-based localization; YOLOv8 dirt detection; multi-scale A* coverage path planning; hierarchical control; autonomous underwater cleaning system autonomous underwater vehicle (AUV); vision-based localization; YOLOv8 dirt detection; multi-scale A* coverage path planning; hierarchical control; autonomous underwater cleaning system

Share and Cite

MDPI and ACS Style

Chen, E.; Lin, Z.; Chen, J.; Shen, Z.; Chen, P.; Fu, X. Vision-Based Autonomous Underwater Cleaning System Using Multi-Scale A* Path Planning. Technologies 2026, 14, 7. https://doi.org/10.3390/technologies14010007

AMA Style

Chen E, Lin Z, Chen J, Shen Z, Chen P, Fu X. Vision-Based Autonomous Underwater Cleaning System Using Multi-Scale A* Path Planning. Technologies. 2026; 14(1):7. https://doi.org/10.3390/technologies14010007

Chicago/Turabian Style

Chen, Erkang, Zhiqi Lin, Jiancheng Chen, Zhiwei Shen, Peng Chen, and Xiaofeng Fu. 2026. "Vision-Based Autonomous Underwater Cleaning System Using Multi-Scale A* Path Planning" Technologies 14, no. 1: 7. https://doi.org/10.3390/technologies14010007

APA Style

Chen, E., Lin, Z., Chen, J., Shen, Z., Chen, P., & Fu, X. (2026). Vision-Based Autonomous Underwater Cleaning System Using Multi-Scale A* Path Planning. Technologies, 14(1), 7. https://doi.org/10.3390/technologies14010007

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