Design and Implementation of Unmanned Surface Vehicle for Water Quality Monitoring

Cao, Huiru; Zhongwei, Guo; Gu, Yu‐Cheng; Zhou, Jiaxin

doi:10.1109/iaeac.2018.8577260

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…As for the underwater environment, the wind speed is 0.2m/s w = and the shipping activity is 0.5  = . The above parameter settings are referenced and based on [20][21][22][26][27][28][29][30]. We then simulated and analyzed the proposed scheme based on the MATLAB platform.…”

Section: Simulation Settings and Resultsmentioning

confidence: 99%

Fisher-Information-Matrix-Based USBL Cooperative Location in USV–AUV Networks

Wang,

Xu,

Feng

et al. 2023

Sensors

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With the development of underwater technology and the increasing demand for ocean development, more and more intelligent equipment is being applied to underwater scientific missions. Specifically, autonomous underwater vehicle (AUV) clusters are being used for their flexibility and the advantages of carrying communication and detection units, often performing underwater tasks in formation. In order to locate AUVs with high precision, we introduce an unmanned surface vehicle (USV) with global positioning system (GPS) and propose a USV–AUV network. Furthermore, we propose an ultra-short baseline (USBL) acoustic cooperative location scheme with an orthogonal array, which is based on underwater communication with sonar. Based on the derivation of the Fisher information matrix formula under Cartesian parameters, we analyze the positioning accuracy of AUVs in different positions under the USBL positioning mode to derive the optimal array of the AUV formation. In addition, we propose a USV path planning scheme based on Dubins path planning functions to assist in locating the AUV formation. The simulation results verify that the proposed scheme can ensure the positioning accuracy of the AUV formation and help underwater research missions.

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Section: Simulation Settings and Resultsmentioning

confidence: 99%

Fisher-Information-Matrix-Based USBL Cooperative Location in USV–AUV Networks

Wang,

Xu,

Feng

et al. 2023

Sensors

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“…Some sensors are even mounted on drones (unmanned surface vehicles) supplied with GPS systems which allow for the mapping of water quality over large pond surfaces (e.g. Cao et al 2018). They can provide farmers with real-time data on water quality (Reis et al 2020) and give warning signals on smartphones when readings exceed or go below predefined limits (e.g.…”

Section: Telemetrymentioning

confidence: 99%

Monitoring methods of feeding behaviour to answer key questions in penaeid shrimp feeding

Tailly

Keitel

Owen

et al. 2021

Reviews in Aquaculture

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The penaeid shrimp farming industry is a fast-growing sector which continues to suffer from significant feeding inefficiencies. Shrimp are slow to feed on pellets, with consumption dependent on a wide range of environmental and physiological parameters. Feed management on farms remains mainly based on feeding trays which can be difficult to observe and often result in overfeeding. While our understanding of shrimp feeding behaviour is beginning to improve under laboratory conditions, much less is known about shrimp behaviour in production ponds. Consequently, there is a growing interest within the industry to improve observations of shrimp feeding behaviour in situ, although this can be difficult due to high water turbidity and the benthic nature of shrimp. This review identifies key questions that remain unanswered in relation to shrimp feeding behaviour under commercial aquaculture conditions, and considers how they could be addressed using state-of-the-art applications based on three technologies commonly used in other areas of aquaculture. The use of passive acoustics, computer vision and telemetry are highlighted, alongside their potential to help farmers achieve better feeding efficiencies and sustainability as well as to help understand shrimp feeding behaviour in relation to various biotic and abiotic parameters.

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“…In response to above drawbacks, many studies have been made on the application of unmanned vessels for water quality monitoring. Cao et al designed a 4G technology-based automatic navigation water quality monitoring unmanned boat, which can navigate to the preset monitoring point and collect water quality information at the location [6]. Siyang and Kerdcharoen realized the upload and storage of water quality monitoring data from unmanned boat based on Zigbee network, and the effective monitoring distance is about within 300 m [7].…”

Section: Related Workmentioning

confidence: 99%

The Mobile Water Quality Monitoring System Based on Low‐Power Wide Area Network and Unmanned Surface Vehicle

Chen

Hao

Yan

et al. 2021

Wireless Communications and Mobile Computing

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The increasingly serious water pollution problem makes efficient and information-based water quality monitoring equipment particularly important. To cover the shortcomings of existing water quality monitoring methods, in this paper, a mobile water quality monitoring system was designed based on LoRa communication and USV. In this system, the USV carrying water quality sensors was used as a platform. Firstly, the LoRa network is used to monitor water quality over a large area. Secondly, the unmanned surface vessel controls the position error within ±20 m and the velocity error within ±1 m/s based on the Kalman filter algorithm. Thirdly, the genetic algorithm based on improved crossover operators is used to determine the optimal operational path, which effectively improves the iterative efficiency of the classical genetic algorithm and avoids falling into local convergence. In the actual water surface test, its packet loss probability within a working range of 1.5 km was below 10%, and the USV could accurately navigate according to the preset optimal path. The test results proved that the system has a relatively large working range and high efficiency. This study is of high significance in water pollution prevention and ecological protection.

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Design and Implementation of Unmanned Surface Vehicle for Water Quality Monitoring

Cited by 8 publications

References 2 publications

Fisher-Information-Matrix-Based USBL Cooperative Location in USV–AUV Networks

Fisher-Information-Matrix-Based USBL Cooperative Location in USV–AUV Networks

Monitoring methods of feeding behaviour to answer key questions in penaeid shrimp feeding

The Mobile Water Quality Monitoring System Based on Low‐Power Wide Area Network and Unmanned Surface Vehicle

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