AquaNova: An AI-Powered, CNN-Based Aquatic Trash Collector with Whale-Inspired Suction, Solar-Battery Operation, Using Arduino, Waypoint Navigation, and Dijkstra’s Algorithm

Mismanaged solid waste, especially plastic, remains a major ecological threat, with the Philippines contributing heavily to marine pollution due to its sachet-driven consumption and highly contaminated river systems. This study responds to the need for an automated solution that reduces environmental and health risks caused by floating waste.
The research developed and evaluated AquaNova, an autonomous, solar-powered floating robot equipped with an AI vision system, GPS waypoint navigation, and a whale-inspired suction mechanism for efficient waste detection and collection. Built using the Prototype SDLC, the system integrates CNN-based detection through YOLOv8 and OpenCV on a Raspberry Pi, with performance assessed through quantitative metrics on detection accuracy, navigation, and operational reliability.
The AI detection model achieved 80 percent overall accuracy, including 100 percent precision for plastic and correct identification of non-trash items in 8 out of 10 cases. Reliability tests showed 92.6 percent system availability, confirming stable operation and dependable GPS waypoint navigation.
AquaNova’s successful implementation demonstrates the feasibility of autonomous, sustainable aquatic waste cleanup. By combining accurate AI-based detection with reliable navigation, it reduces waste accumulation and helps prevent blockages. Future improvements should enhance metal detection performance and add night vision to support continuous operation.

Trash Collection, Autonomous Robot, AI Detection, Water Pollution Robot, Autonomous Navigation

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