Autonomous Unmanned Underwater Vehicles Development Tendencies

Gafurov, Salimzhan; Klochkov, Evgeniy V.

doi:10.1016/j.proeng.2015.06.017

Cited by 120 publications

(55 citation statements)

References 4 publications

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“…To this purpose we estimate the area covered by a single glider over a month. Following Wood (2010) and Gafurov and Klochkov (2015) typical travel speed for Deepglider is 0.25 m s −1 and assuming a 160 km autocorrelation length scale for oceanic observations (Purkey and Johnson 2010) we find: ∼100,000 km 2 . On the other hand, from ODD we can compute the decrease of uncertainty per area monitored assuming that all depth are monitored as now possible with Deepgliders.…”

Section: Applicability To In Situ Measurementsmentioning

confidence: 64%

“…4b1, c1). For comparison we have computed the glider fleet number required to cover the monitored area in a month, assuming a 0.25 m per second travel speed (Wood 2010;Gafurov and Klochkov 2015) and a 160 km spatial correlation of observations (Purkey and Johnson 2010). (Gliders are assumed to reach the bottom of the ocean, as now possible with Deepgliders.)…”

Section: Normalized Density Distribution For Ohcmentioning

confidence: 99%

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Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

et al. 2017

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In this study, the relation between two approaches to assess the ocean predictability on interannual to decadal time scales is investigated. The first pragmatic approach consists of sampling the initial condition uncertainty and assess the predictability through the divergence of this ensemble in time. The second approach is provided by a theoretical framework to determine error growth by estimating optimal linear growing modes. In this paper, it is shown that under the assumption of linearized dynamics and normal distributions of the uncertainty, the exact quantitative spread of ensemble can be determined from the theoretical framework. This spread is at least an order of magnitude less expensive to compute than the approximate solution given by the pragmatic approach. This result is applied to a state-of-the-art Ocean General Circulation Model to assess the predictability in the North Atlantic of four typical oceanic metrics: the strength of the Atlantic Meridional Overturning Circulation (AMOC), the intensity of its heat transport, the two-dimensional spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the three-dimensional spatially-averaged temperature in the North Atlantic. For all tested metrics, except for SST, ∼ 75% of the total uncertainty on interannual time scales can be attributed to oceanic initial condition uncertainty rather than atmospheric stochastic forcing. The theoretical method also provide the sensitivity pattern to the initial condition uncertainty, allowing for targeted measurements to improve the skill of the prediction. It is suggested that a relatively small fleet of several autonomous underwater vehicles can reduce the uncertainty in AMOC strength prediction by 70% for 1-5 years lead times.

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Section: Applicability To In Situ Measurementsmentioning

confidence: 64%

Section: Normalized Density Distribution For Ohcmentioning

confidence: 99%

Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

et al. 2017

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“…For example, the loss accidents of KAIKO and Nereus [5][6] was regrettable, which brought significant losses to the research development of the two study teams. As described above, improvements in the AUV's operating ability increased the complexity of the AUV system [7] [8], and the AUV's safety and security have received increasing attention. Therefore, the fault diagnosis of propulsion, sensors, and other equipment subsystems of the AUVs is required [9].…”

Section: J He Y LI Propeller Fault Diagnosis Based On a Rank Y Jimentioning

confidence: 99%

Propeller Fault Diagnosis Based on a Rank Particle Filter for Autonomous Underwater Vehicles

He¹,

Li²,

Jiang³

et al. 2018

brod

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SummaryRank particle filtering was applied to fault diagnosis technology. Control force and yaw moment losses that occurred in the corresponding degrees of freedom were estimated, and the trends of change were calculated by the multi-step-ahead prediction process in the rank particle filter. Based on the estimated results in the normal state, the situations involving failure were detected. To achieve this target, a mathematical model of the normal and faulty motion states of an underwater vehicle was first developed. Subsequently, the rank sample method combined with the particle filter was used, to obtain the importance probability density function of the autonomous underwater vehicle status. The rank particle filter obtained above realized the real-time state estimation and trend prediction of the motion state. A modified Bayesian algorithm was used to process the estimated control force and yaw moment losses in a given length of time. Based on the calculation results in the normal situations, a back propagation neural network was trained to obtain the diagnostic values. The condition of the propellers was determined based on the diagnostic values. Fault diagnosis simulation experiments were carried out using both the data obtained from the semi-physical simulation system with a hypothetical propeller failure, and the real sea trail data to verify the performance of the proposed algorithm. The results showed that the rank particle filter method could be applied to the propeller fault diagnosis of autonomous underwater vehicles, and solved the problem that arises when a single degree of freedom of the loss is utilized.

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“…AUV dapat melakukan survey bawah air untuk mengidentifikasi komponen biologi dan fisika bawah air [10]. AUV dapat melakukan pekerjaan yang sulit dilakukan penyelam karena batasan kedalaman dan bahaya yang mengancam nyawa penyelam [11]. Kebanyakan metode yang digunakan untuk mengidentifikasi sedimen di daerah pantai dapat merusak ekosistem dan menghancurkan terumbu.…”

Section: Pendahuluanunclassified

“…Definisi pesan dibuat dalam XML file. Kode pesan kemudian dibangkitkan menggunakan C++ dan Java [11].…”

Section: B Perangkat Lunak Lauvunclassified

Autonomous Underwater Vehicle untuk Survei dan Pemantauan Laut

Manik¹,

Syakti

Jaya

et al. 2017

JRE

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Autonomous Unmanned Underwater Vehicles Development Tendencies

Cited by 120 publications

References 4 publications

Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

Propeller Fault Diagnosis Based on a Rank Particle Filter for Autonomous Underwater Vehicles

Autonomous Underwater Vehicle untuk Survei dan Pemantauan Laut

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