scholarly journals ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

2020 ◽  
Vol 8 (9) ◽  
pp. 633
Author(s):  
Simone Marini ◽  
Nikolla Gjeci ◽  
Shashank Govindaraj ◽  
Alexandru But ◽  
Benjamin Sportich ◽  
...  
Keyword(s):  
Satellite Communication ◽  
Autonomous Underwater Vehicle ◽  
Deep Ocean ◽  
Hydrogen Fuel Cells ◽  
Unmanned Surface Vehicle ◽  
Semantic Classification ◽  
Central Processing ◽  
Land Station ◽  
Extended Duration ◽  
Li Ion

The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data.

Autonomous Underwater Vehicle for surveying deep ocean

2009 ◽  
Author(s):  
Tadahiro Hyakudome ◽  
Satoshi Tsukioka ◽  
Hiroshi Yoshida ◽  
Takao Sawa ◽  
Shojiro Ishibashi ◽  
...  
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Deep Ocean ◽  
Underwater Vehicle

scholarly journals Sizing and Dynamic Modeling of a Power System for the MUN Explorer Autonomous Underwater Vehicle Using a Fuel Cell and Batteries

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Mohamed M. Albarghot ◽  
M. Tariq Iqbal ◽  
Kevin Pope ◽  
Luc Rolland
Keyword(s):  
Fuel Cell ◽  
Power System ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Hydrogen Gas ◽  
System Capacity ◽  
Li Ion Batteries ◽  
Li Ion ◽  
Battery Bank ◽  
Bank System

The combination of a fuel cell and batteries has promising potential for powering autonomous vehicles. The MUN Explorer Autonomous Underwater Vehicle (AUV) is built to do mapping-type missions of seabeds as well as survey missions. These missions require a great deal of power to reach underwater depths (i.e., 3000 meters). The MUN Explorer uses 11 rechargeable Lithium-ion (Li-ion) batteries as the main power source with a total capacity of 14.6 kWh to 17.952 kWh, and the vehicle can run for 10 hours. The drawbacks of operating the existing power system of the MUN Explorer, which was done by the researcher at the Holyrood management facility, include mobilization costs, logistics and transport, and facility access, all of which should be taken into consideration. Recharging the batteries for at least 8 hours is also very challenging and time consuming. To overcome these challenges and run the MUN Explorer for a long time, it is essential to integrate a fuel cell into an existing power system (i.e., battery bank). The integration of the fuel cell not only will increase the system power, but will also reduce the number of batteries needed as suggested by HOMER software. In this paper, an integrated fuel cell is designed to be added into the MUN Explorer AUV along with a battery bank system to increase its power system. The system sizing is performed using HOMER software. The results from HOMER software show that a 1-kW fuel cell and 8 Li-ion batteries can increase the power system capacity to 68 kWh. The dynamic model is then built in MATLAB/Simulink environment to provide a better understanding of the system behavior. The 1-kW fuel cell is connected to a DC/DC Boost Converter to increase the output voltage from 24 V to 48 V as required by the battery and DC motor. A hydrogen gas tank is also included in the model. The advantage of installing the hydrogen and oxygen tanks beside the batteries is that it helps the buoyancy force in underwater depths. The design of this system is based on MUN Explorer data sheets and system dynamic simulation results.

Multimodal learning and inference from visual and remotely sensed data

2016 ◽  
Vol 36 (1) ◽  
pp. 24-43 ◽  
Author(s):  
Dushyant Rao ◽  
Mark De Deuge ◽  
Navid Nourani–Vatani ◽  
Stefan B Williams ◽  
Oscar Pizarro
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Sensory Information ◽  
Remote Sensing Data ◽  
Visual Image ◽  
Remotely Sensed ◽  
Image Features ◽  
Depth Information ◽  
Multimodal Learning ◽  
Remotely Sensed Data ◽  
Semantic Classification

Autonomous vehicles are often tasked to explore unseen environments, aiming to acquire and understand large amounts of visual image data and other sensory information. In such scenarios, remote sensing data may be available a priori, and can help to build a semantic model of the environment and plan future autonomous missions. In this paper, we introduce two multimodal learning algorithms to model the relationship between visual images taken by an autonomous underwater vehicle during a survey and remotely sensed acoustic bathymetry (ocean depth) data that is available prior to the survey. We present a multi-layer architecture to capture the joint distribution between the bathymetry and visual modalities. We then propose an extension based on gated feature learning models, which allows the model to cluster the input data in an unsupervised fashion and predict visual image features using just the ocean depth information. Our experiments demonstrate that multimodal learning improves semantic classification accuracy regardless of which modalities are available at classification time, allows for unsupervised clustering of either or both modalities, and can facilitate mission planning by enabling class-based or image-based queries.

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