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

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.