Unmanned surface vehicles (USVs) equipped with integrated sensors are a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The elaboration of environmental monitoring programs has relied so far on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. In the present work, USVs were deployed by the Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyll α, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and the rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for the in situ measurements of copper and lead was also evaluated herewith. The results obtained demonstrate the advantages of USVs, setting the base for their application in real-time monitoring of chemical parameters including metals. Simultaneously, the requirements for accuracy and sensitivity improvement of HM sensors were noted, in order to permit full exploitation of USVs' capacities.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11086208 | PMC |
http://dx.doi.org/10.3390/s24092809 | DOI Listing |
Sci Rep
January 2025
College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Mining electric shovels are one of the core equipments for open-pit mining, and are currently moving towards intelligent and unmanned transformation, with intelligent mining instead of traditional manual operation. In the excavation operation process, due to the complexity and changeability of the material surfaces, different excavation strategies should be adopted to achieve the optimal excavation trajectory. It is an important research direction to realize the unmanned excavation of electric shovels by studying a trajectory planning method that is not limited to fixed resting angle surface, can comprehensively consider the type of material surfaces and aim at the minimum excavation energy consumption per unit volume.
View Article and Find Full Text PDFAnal Chim Acta
February 2025
Instituto de Química, Universidade Federal de Goiás, 74690-900, Goiânia, GO, Brazil; Instituto Nacional de Ciência e Tecnologia de Bioanalítica, Campinas, 13084-971, SP, Brazil. Electronic address:
Background: Distinct classes of environmental contaminants - such as microplastics, volatile organic compounds, inorganic gases, hormones, pesticides/herbicides, and heavy metals - have been continuously released into the environment from different sources. Anthropogenic activities with unprecedented consequences have impacted soil, surface waters, and the atmosphere. In this scenario, developing sensing materials and analytical platforms for monitoring water and air quality is essential to supporting worldwide environmental control agencies.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Computer Engineering, Faculty of Electrical & Electronics, Yildiz Technical University, 34220 Istanbul, Türkiye.
Developing autonomous navigation techniques for surface vehicles remains an important research area, and accurate global path planning is essential. For mobile robots-particularly for Unmanned Surface Vehicles (USVs)-a key challenge is ensuring that sharp turns and sharp breaks are avoided. Therefore, global path planning must not only calculate the shortest path but also provide smoothness.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Electrical and Computer Engineering, Technical University of Crete, Aktotiri Campus, GR-73100 Chania, Greece.
Unmanned Aerial Vehicle (UAV)-based sensing and imaging represent a cutting-edge approach to addressing the critical need for the real-time, accurate, and automated data collection and analysis of physical events on Earth's surface [...
View Article and Find Full Text PDFSci Rep
December 2024
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
With the advancement of artificial intelligence technology, unmanned boats utilizing deep learning models have shown significant potential in water surface garbage classification. This study employs Convolutional Neural Network (CNN) to extract features of water surface floating objects and constructs the VGG16-15 model based on the VGG-16 architecture, capable of identifying 15 common types of water surface floatables. A garbage classification dataset was curated to obtain 5707 images belonging to 15 categories, which were then split into training and validation sets in a 4:1 ratio.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!