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Papers
61,005 resultsShowing papers similar to Autonomous Design of a Green Sea-Cleaner Boat
Clear“WAVECLEAN” – An Innovation in Autonomous Vessel Driving Using Object Tracking and Collection of Floating Debris
Researchers designed an autonomous vessel called WAVECLEAN that uses object-tracking technology to identify and collect floating marine debris, including plastics. The system combines camera-based detection with machine learning to navigate waterways and gather waste without human operation. The study demonstrates a technology-driven approach to addressing plastic pollution in harbors, rivers, and coastal areas.
Smart 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.
Autonomous Beach Cleaner Robot: A Mechatronic and Control Approach for Sustainable Coastal Pollution Management at Peru
Researchers designed an autonomous solar-powered beach cleaning robot for Peru that uses ultrasonic sensors and a sieving mechanism to detect and collect microplastics and other coastal debris, following a V-model design methodology.
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.
Effect of Monohull Type and Catamaran Hull Type on Ocean Waste Collection Behavior Using OpenFOAM
Researchers used OpenFOAM computational fluid dynamics simulations to compare ocean waste collection behavior of a round-bilge monohull versus an inner flat-type catamaran fitted with forward conveyors, finding that the catamaran allowed marine debris to approach the collection conveyor more easily due to favorable flow characteristics, while the monohull moved debris toward the conveyor faster once in proximity.
Multiobjective Environmental Cleanup with Autonomous Surface Vehicle Fleets Using Multitask Multiagent Deep Reinforcement Learning
Autonomous surface vehicles were programmed for multi-objective environmental cleanup operations targeting floating debris and microplastics in water bodies. The study demonstrates how robotics and AI can be applied to scale up active microplastic removal from surface waters.
Enhancing marine debris identification with convolutional neural networks
A deep learning model was developed to identify and classify marine debris components captured by underwater remotely operated vehicle imagery, addressing the challenge of widely distributed ocean waste including microplastics. The convolutional neural network demonstrated improved accuracy for debris detection and classification compared to conventional image analysis methods.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
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.
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.
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.
Underwater Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning
Researchers reviewed machine learning techniques for underwater image detection to support water pollution cleanup, focusing on convolutional neural networks and region-based CNN methods for identifying surface mucilage and debris. The study evaluated supervised classification algorithms as the most effective approach for automated aquatic waste detection systems.
The Effect Of Wave Length And Amplitude on The Hydrodynamic Characteristics of Waste Collection Vessels Using Computational Fluid Dynamics (CFD)
This computational fluid dynamics study examined how wave conditions affect the hydrodynamic performance of a vessel designed to collect marine debris from the water surface. Efficient marine debris collection vessels are important for removing plastic waste before it degrades into microplastics in the ocean.
Deep-Sea Debris Identification Using Deep Convolutional Neural Networks
Researchers developed a deep convolutional neural network classifier to identify and distinguish deep-sea debris from seafloor imagery, demonstrating that automated AI-based detection can support submersible clean-up operations targeting marine debris in deep-sea environments.
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.
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.
Detection of Trash in Sea Using Deep Learning
Researchers developed a deep learning convolutional neural network (CNN) model to detect and classify trash in marine and aquatic environments from underwater images, aiming to overcome the limitations of manual debris detection for objects that may be submerged or partially obscured.
Detection and Recognition of Ocean Garbage Using DIY ROV-Mounted DNN-Based Classification of Laser Images
Researchers designed a low-cost DIY underwater robot equipped with a laser imaging system and deep learning classifier to detect and categorize underwater garbage from microplastics to large debris. A custom-trained convolutional neural network achieved 91% classification accuracy, outperforming transfer learning approaches.
Plastic Waste on Water Surfaces Detection Using Convolutional Neural Networks
Researchers evaluated state-of-the-art convolutional neural network architectures for automatically detecting plastic waste on water surfaces, training models on a dataset representing four categories of plastic litter including plastic bags. The study benchmarked multiple CNN object detection models following extensive dataset preprocessing to determine the most effective approach for automated plastic pollution identification.
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.
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.
A Spiral-Propulsion Amphibious Intelligent Robot for Land Garbage Cleaning and Sea Garbage Cleaning
Not relevant to microplastics research; this paper presents the design and testing of an amphibious robot capable of collecting garbage from beaches, tidal flats, and the ocean surface, but does not analyze microplastic pollution specifically.