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Papers
61,005 resultsShowing papers similar to “WAVECLEAN” – An Innovation in Autonomous Vessel Driving Using Object Tracking and Collection of Floating Debris
ClearSmart Ocean Cleanup: An AI-Integrated Autonomous System for Marine Waste Management
This paper presents an AI-powered autonomous boat system designed to detect and collect marine pollution — including plastics, oil spills, and microplastics — using deep learning image classification, IoT sensors, and robotic collection mechanisms. The system demonstrated over 94% accuracy for pollutant detection and classification across several AI models. While focused more broadly on ocean cleanup technology than on microplastic science specifically, it demonstrates how AI-integrated robotics could help address the practical challenge of removing plastic waste from ocean surfaces before it breaks down further.
Towards Accessible Aquatic Cleanup: A Low-Cost Solution for Floating Waste Extraction
Researchers designed and tested a low-cost autonomous floating waste extractor using a conveyor mechanism to capture lightweight surface pollutants including microplastics, demonstrating high efficiency in capturing debris and offering an affordable solution for resource-constrained settings.
FindingPlastic: Underwater Plastic Bag Detection and Retrieval
Engineers developed an automated system using artificial intelligence to detect, track, and capture floating plastic bags underwater before they break down into microplastics. The system combines modern object detection and tracking algorithms and was successfully tested in a tank environment, offering a potential tool for robotic ocean cleanup efforts.
Ro-Boat: IoT-Based Non-Autonomous Garbage Collector Boat for Organic, Metal, and Non-Metal Waste
Researchers developed Ro-Boat, an IoT-based non-autonomous garbage-collecting vessel designed to remove organic, metal, and non-metal waste from rivers, lakes, and coastal waters. The prototype uses sensor-based waste detection and optimised collection mechanisms to address aquatic plastic and debris pollution in operational water body environments.
Autonomous Design of a Green Sea-Cleaner Boat
Researchers developed an autonomous sea-cleaner boat design incorporating LiDAR sensors and a convolutional neural network camera system for marine debris identification and collection. Parametric studies of catamaran hull configurations found the 'Flat-Outside Model' produced the lowest wave elevation and hydrodynamic force, making it the most environmentally preferable hull design for autonomous marine cleanup operations.
Bio-Inspired Marine Waste Collection System with Adaptive Suction Mechanism: Energy Optimization through Intelligent Waste Dimension Recognition
Researchers designed an autonomous marine waste collection robot inspired by fish feeding biomechanics, integrating AI navigation, renewable energy, and an adaptive suction mechanism for capturing plastic debris. The dual-chamber vacuum system demonstrated energy-efficient marine debris collection, representing a bioinspired approach to ocean plastic remediation.
Unmanned Vehicles System Utilizing Waste Tracking Data to Tackle Plastic Marine Littering on Tourist Islands
This paper proposes using unmanned vehicles guided by waste tracking data to collect plastic marine litter around tourist islands. Autonomous cleanup technology could help remove plastic debris before it breaks down into microplastics and enters the food chain.
Development of Garbage Collecting Robot for Marine Microplastics
Researchers developed a garbage-collecting robot designed to remove plastic debris from coastal areas before it degrades into microplastics, addressing the logistical challenge of cleaning extensive shorelines with minimal human labor and resources.
Improvement and Empirical Testing of a Novel Autonomous Microplastics-Collecting Semisubmersible
Researchers improved an autonomous microplastic-collecting robot, testing design modifications that enhanced sampling efficiency and navigation in surface water environments, moving toward practical automated monitoring of plastic pollution.
AI – Driven Marine Debris Detection for Ocean Conservation
Researchers developed an AI-driven marine debris detection system using the YOLOv8 deep learning model trained to identify plastic waste in challenging underwater conditions including low visibility and complex backgrounds. The system aims to provide scalable, automated monitoring to support ocean conservation and guide debris removal efforts.
Exploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Review
This review explores how autonomous underwater vehicles equipped with sensors could detect microplastics directly in the ocean, rather than relying on labor-intensive water sampling. Current detection methods are slow and expensive, making real-time monitoring difficult. Advances in onboard sensing technology could dramatically improve our understanding of where microplastics concentrate in marine environments.
Exploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Systematic Review
This systematic review explores how autonomous underwater vehicles (AUVs) could be used to detect microplastics in the ocean in real time, replacing slower traditional sampling methods. While promising, the technology is still developing and faces challenges with sensor accuracy and deep-water operation. Better detection tools like these could help scientists understand how widespread microplastic contamination really is in marine environments.
LIZARD: Pervasive Sensing for Autonomous Plastic Litter Monitoring
Researchers developed LIZARD, a pervasive sensing system designed for autonomous vehicles to detect and monitor plastic litter in the environment. The system uses an innovative sensing pipeline that can identify and classify plastic debris in real time as the vehicle moves through an area. The technology could significantly reduce the labor and cost of monitoring plastic pollution by automating what has traditionally been a manual survey process.
Design and Development of Smart Beach Debris Collection and Segregation System
Researchers designed and built a smart automated system for collecting and segregating beach debris, using sensors and robotics to identify and sort plastic waste from natural material on shorelines. The system demonstrated effective separation of plastic debris in field tests.
Automatic Beach Cleaning Robot
Researchers designed a portable automatic beach cleaning robot for collecting plastic debris from sandy beaches to reduce marine pollution and protect aquatic ecosystems.
Development of Drifting Debris Detection System using Deep Learning on Coastal Cleanup
Researchers developed a deep learning-based system to detect litter on beaches using images and automated object recognition. Efficient litter detection tools could help coastal cleanup programs identify and remove plastic debris before it breaks down into microplastics.
Aquatic Trash Detection and Classification: a Machine Learning and Deep Learning Perspective
This review examines machine learning and deep learning approaches for detecting and classifying aquatic trash in waterways, evaluating how computer vision algorithms trained on underwater and surface imagery can automate pollution monitoring for faster, more scalable ocean cleanup.
Development of Garbage Collecting Robot for Marine Microplastics
Researchers designed and developed an autonomous cleaning robot for collecting marine microplastics scattered on beaches, using a conveyor belt and tray system to mechanically gather and retain small plastic particles. The study addresses the practical difficulty of manually collecting dispersed microplastics and demonstrates the robot's configuration and operational concept for beach remediation.
Towards Underwater Macroplastic Monitoring Using Echo Sounding
Researchers investigated using echo sounding (sonar) technology to detect and monitor underwater macroplastics in rivers and coastal environments, presenting this acoustic approach as a promising tool for measuring submerged plastic loads that surface trawling misses.
Robotic Vacuum Cleaner for Microplastics
Researchers developed a robotic device capable of vacuuming up tiny plastic particles floating on the surface of water bodies, offering a new tool for cleaning up microplastic pollution in lakes, ponds, or coastal areas. The device represents a step toward automated, scalable approaches for removing microplastics from aquatic environments.
Particle Swarm Optimization Based Efficient Path Planning in Autonomous Marine Trash Collection
Researchers developed a marine trash-collecting robot guided by Particle Swarm Optimization (PSO) and GPS, which uses a conveyor-based collection mechanism and sensor input to navigate waterways and efficiently collect floating plastic debris.
Cleaning up the world’s oceans with underwater laser imaging
Researchers proposed using underwater LiDAR (Light Detection and Ranging) technology to detect and map submerged plastic debris in the oceans, arguing this approach offers higher resolution and greater safety for marine life compared to sonar, and could enable targeted cleanup of the estimated 70% of ocean plastic that lies below the surface.
Removing Plastic Waste from Rivers: A Prototype-Scale Experimental Study on a Novel River-Cleaning Concept
Researchers tested a prototype-scale river-cleaning system designed to capture plastic waste across the full width and depth of a river. The study demonstrated that the novel approach can effectively intercept floating and submerged plastic debris, addressing the critical role that rivers play as transport pathways carrying mismanaged plastic waste from land to oceans.
Design and Validation of an Eco-Compatible Autonomous Drone for Microplastic Monitoring in Port Environments
Researchers designed and tested an autonomous drone system for monitoring microplastic pollution in port environments, where plastic tends to accumulate in semi-enclosed waters. The drone collected water surface samples and transmitted data in real time, demonstrating a practical tool for high-frequency environmental monitoring in busy maritime settings.