Development of a hybrid autonomous underwater vehicle for benthic monitoring

Cadena, A.; Teran, P.; Reyes, Guarín; Lino, J.; Yaselga, V.; Vera, S.

doi:10.1109/iccar.2018.8384715

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…In this paper, authors refined the loss function to fit for multiple fish application. e proposed loss function is regularized to reduce the small dataset and overfitting problem, L2 regularization is to add a regularization function a er the cost function which is listed in the Equations (6) and (7). GPU with 4G memory.…”

Section: Refine Loss Function Yolo Improved Loss Function Frommentioning

confidence: 99%

“…A er this the AUV retired from the competition, authors realized it was time to revise the system to conquer real life tasks. As of now, most of the robot control platforms were shi ing to Systems-On-Chip (SOC) [6,7]. To move forward and add more functionalities to the AUV, one goal is to switch from a clear swimming pool environment to a real ocean water condition.…”

Section: Introductionmentioning

confidence: 99%

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Fish Detection Using Deep Learning

Cui

Zhou

Wang

et al. 2020

Applied Computational Intelligence and Soft Computing

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Recently, human being’s curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being’s learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed. The training data set was collected from the Gulf of Mexico by a digital camera. To fit into this unique need, three optimization approaches were applied to the CNN: data augmentation, network simplification, and training process speed up. Data augmentation transformation provided more learning samples; the network was simplified to accommodate the artificial neural network; the training process speed up is introduced to make the training process more time efficient. Experimental results showed that the proposed model is promising, and has the potential to be extended to other underwear objects.

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Section: Refine Loss Function Yolo Improved Loss Function Frommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fish Detection Using Deep Learning

Cui

Zhou

Wang

et al. 2020

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

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Towards Energy-Optimized On-Board Computer Vision for Autonomous Underwater Vehicles

Gregorek

Srinivas

Nasrulla

et al. 2022

OCEANS 2022, Hampton Roads

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TRiForm: Formation Control for Underwater Sensor Networks With Measurement Errors

Yang

Cao

et al. 2020

IEEE Trans. Veh. Technol.

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The formation control of mobile underwater wireless sensor networks (MUWSNs) is difficult due to the severe errors in distance and motion measurements. To address this problem, we propose a new scheme, TRiForm, for the distributed formation control of an MUWSN. TRiForm constructs a rigid graph virtual structure from triangles to improve the reliability and efficiency. TRiForm effectively utilizes anchor information to detect and compensate for measurement errors. We have proven the correctness of the rigidity construction by TRiForm. We have also performed extensive simulations to evaluate TRiForm in various application scenarios using the measured parameters from real underwater nodes. The results show that TRiForm can successfully maintain the formations of an MUWSN and control it to arrive at the destination under distance and motion measurement errors.

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Development of a hybrid autonomous underwater vehicle for benthic monitoring

Cited by 5 publications

References 7 publications

Fish Detection Using Deep Learning

Fish Detection Using Deep Learning

Towards Energy-Optimized On-Board Computer Vision for Autonomous Underwater Vehicles

TRiForm: Formation Control for Underwater Sensor Networks With Measurement Errors

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