Dynamic Risk Analysis of Operation of the Autonomous Underwater Vehicle (AUV)

Yang, Ruochen; Utne, Ingrid Bouwer; Liu, Yiliu; Paltrinieri, Nicola

doi:10.3850/978-981-14-8593-0_4118-cd

Cited by 12 publications

(5 citation statements)

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“…The field of AUV operation in polar environments is complex and fraught with risks, as delineated in prior studies using various methodologies such as Bayesian Belief Networks (BBN) and Fuzzy System Dynamics Risk Analysis (FuSDRA) [2,4,12,[15][16][17][18][19]. Table 1 below summarizes the different approaches used in related research works and their respective strengths and weaknesses, including the method proposed in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Elsewhere, Thieme and Utne [15] and Hegde et al [4] proposed BBN risk models to calculate the probability of monitoring success or mission abortion, considering a wide range of influential factors, from technical and human aspects to organizational and environmental aspects. Yang et al [16] applied a dynamic Bayesian approach to capture the influence of environmental changes during AUV missions. Even though their primary focus was not environmental factors, they incorporated real-time data from the AUV's operating environment to determine dynamic risk values, highlighting the versatility of the Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

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Risk Analysis of Autonomous Underwater Vehicle Operation in a Polar Environment Based on Fuzzy Fault Tree Analysis

Noh,

Kang,

Park

2023

JMSE

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Autonomous underwater vehicles have long been used in marine explorations, and their application in recent polar expeditions is particularly noteworthy. However, the complexity and extreme conditions of the polar environment pose risks to the stable operation of autonomous underwater vehicles. This study adopted the methodology of fuzzy fault tree analysis to deeply analyze the operational risks of autonomous underwater vehicles in polar environments. While traditional fault tree analysis maps the causal relationships and probabilities between basic and intermediate events, fuzzy fault tree analysis models the uncertainty of data and determines the failure probability by integrating expert opinions. This study revealed that polar environment-induced failures play a more substantial role in autonomous underwater vehicle loss in polar regions than inherent system failures. The study identified ‘recovery failure’ and ‘poor communication’ as the major risk factors facing autonomous underwater vehicles in polar environments, exhibiting the highest failure probabilities. Specifically, among various polar environmental factors, ‘large ice concentration’, ‘ice thickness’, and ‘roughness of ice underside’ under ‘bad’ conditions were found to have a significant impact on the autonomous underwater vehicle’s failure probability. The fuzzy fault tree analysis method in this study successfully filled the gap created by the absence of historical data by effectively incorporating expert opinions, enabling a quantitative presentation of the impact of polar environments, which has been previously difficult to convey in qualitative terms.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Analysis of Autonomous Underwater Vehicle Operation in a Polar Environment Based on Fuzzy Fault Tree Analysis

Noh,

Kang,

Park

2023

JMSE

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“…The constructed risk model further assisted in decision-making for waypoint selections of the vehicle. Yang et al (Yang et al, 2020) provided an approach for dynamic risk analyses of a long-range AUV based on a dynamic BN model. The risk state can be updated online when the vehicle experiences different operating environments, which automatically guides the AUV to avoid hazardous environmental conditions.…”

Section: Methodsmentioning

confidence: 99%

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

et al. 2022

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“…However, the sound that arises from the motion of BOTs and AUVs needs to be reduced as it might hinder the marine ecosystem during sub-sea travel. Also, it must be noted that many biomimicking AUVs have overcome the noise effect and movement disturbances caused by AUVs [4][5][6][7][8]. The art of collecting data from the Warehouse Management system (WMS) is detailed in [9].…”

Section: Introductionmentioning

confidence: 99%

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

Sowmmiya,

Roselyn,

Sundaravadivel

2024

Information

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Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user’s control through a user-friendly mobile/web application (APP). The different features impacting the identification of hotspots are considered, and feature selection is performed using various classifier-based learning algorithms, namely, Naive Bayes, Nearest neighbors, Random Forest and Support Vector Machine (SVM). The performance of classifications are compared. From the real-time results, it is clear that the Naive Bayes classification model is found to provide better accuracy, which is employed in the application platform for predicting the potential fishing zone. After identifying the first catch, the species are classified using an AlexNet-based deep Convolutional Neural Network. Also, the user can fetch real-time information such as the status of fishing through live video streaming to determine the quality and quantity of fish along with information like pH, temperature and humidity. The proposed work is implemented in a real-time boat structure prototype and is validated with data from sensors and satellites.

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Dynamic Risk Analysis of Operation of the Autonomous Underwater Vehicle (AUV)

Cited by 12 publications

References 0 publications

Risk Analysis of Autonomous Underwater Vehicle Operation in a Polar Environment Based on Fuzzy Fault Tree Analysis

Risk Analysis of Autonomous Underwater Vehicle Operation in a Polar Environment Based on Fuzzy Fault Tree Analysis

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification

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