Fuel Cell Power Systems for Autonomous Underwater Vehicles: State of the Art

2014 ◽  
Author(s):  
Alejandro Mendez Guevara ◽  
Teresa J. Leo ◽  
Miguel A. Herreros
Keyword(s):  
Fuel Cell ◽  
Power Systems ◽  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles

scholarly journals Current State of Technology of Fuel Cell Power Systems for Autonomous Underwater Vehicles

Energies ◽  
2014 ◽  
Vol 7 (7) ◽  
pp. 4676-4693 ◽  
Author(s):  
Alejandro Mendez ◽  
Teresa Leo ◽  
Miguel Herreros
Keyword(s):  
Fuel Cell ◽  
Power Systems ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Current State

Advances in Direct Fuel Cell/Gas Turbine Power Plants

2003 ◽  
Author(s):  
Hossein Ghezel-Ayagh ◽  
Joseph M. Daly ◽  
Zhao-Hui Wang
Keyword(s):  
Fuel Cell ◽  
Power Systems ◽  
Gas Turbine ◽  
Power Plants ◽  
State Of The Art ◽  
Combined Cycle ◽  
Proof Of Concept ◽  
Hybrid Power Systems ◽  
Fuel Cell Stack ◽  
T System

This paper summarizes the recent progress in the development of hybrid power systems based on Direct FuelCell/Turbine® (DFC/T®). The DFC/T system is capable of achieving efficiencies well in excess of state-of-the-art gas turbine combined cycle power plants but in much smaller size plants. The advances include the execution of proof-of-concept tests of a fuel cell stack integrated with a microturbine. The DFC/T design concept has also been extended to include the existing gas turbine technologies as well as more advanced ones. This paper presents the results of successful sub-MW proof-of-concept testing, sub-MW field demonstration plans, and parametric analysis of multi-MW DFC/T power plant cycle.

Power systems for autonomous underwater vehicles

2001 ◽  
Vol 26 (4) ◽  
pp. 526-538 ◽  
Author(s):  
A.M. Bradley ◽  
M.D. Feezor ◽  
H. Singh ◽  
F. Yates Sorrell
Keyword(s):  
Power Systems ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles

Towards Underwater Inspection Using AUVs

2015 ◽  
Vol 63 (5) ◽  
Author(s):  
Jörg Kalwa
Keyword(s):  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
The State

AbstractAUVs – Autonomous Underwater Vehicles have reached a state of maturity that allows bringing them into applications beyond research. Still being a new “toy” in the toolbox for offshore work the market is somewhat reluctant to employ these robots due to missing field prove. This paper gives an overview about the state of the art of applications and technology and finalizes in a recent example, dmonstrating autonomy and robustness.

scholarly journals A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2068 ◽  
Author(s):  
César Debeunne ◽  
Damien Vivet
Keyword(s):  
Autonomous Navigation ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Simultaneous Localization And Mapping ◽  
Underwater Vehicles ◽  
Visual Features ◽  
Localization And Mapping ◽  
Comprehensive Survey ◽  
Self Driving Cars

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

The Present and Future Capabilities of Deep ROVs

1999 ◽  
Vol 33 (4) ◽  
pp. 26-40 ◽  
Author(s):  
Robert Wernli
Keyword(s):  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Deep Ocean ◽  
Underwater Vehicles ◽  
The State ◽  
Remotely Operated Vehicles ◽  
Early Stages

The following paper will present an overview of Remotely Operated Vehicles (ROVs) and, in particular, their use in the deep ocean, which includes depths beyond 10,000 feet. Although the intent of the paper is to address tethered, free-flying vehicles, the categories of deep towed vehicles and autonomous underwater vehicles (AUVs) will also be included for completeness. And, to properly discuss the state-of-the-art in such deep ocean systems, their capabilities in the depths less than 10,000 ft will also be addressed. An attempt to project their uses in the early stages of the next millennium wiU also be made.

scholarly journals Multiple Receptive Field Network (MRF-Net) for Autonomous Underwater Vehicle Fishing Net Detection Using Forward-Looking Sonar Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1933
Author(s):  
Rixia Qin ◽  
Xiaohong Zhao ◽  
Wenbo Zhu ◽  
Qianqian Yang ◽  
Bo He ◽  
...  
Keyword(s):  
Receptive Field ◽  
Embedded System ◽  
Object Detection ◽  
Autonomous Underwater Vehicle ◽  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Field Network ◽  
Better Than ◽  
Forward Looking

Underwater fishing nets represent a danger faced by autonomous underwater vehicles (AUVs). To avoid irreparable damage to the AUV caused by fishing nets, the AUV needs to be able to identify and locate them autonomously and avoid them in advance. Whether the AUV can avoid fishing nets successfully depends on the accuracy and efficiency of detection. In this paper, we propose an object detection multiple receptive field network (MRF-Net), which is used to recognize and locate fishing nets using forward-looking sonar (FLS) images. The proposed architecture is a center-point-based detector, which uses a novel encoder-decoder structure to extract features and predict the center points and bounding box size. In addition, to reduce the interference of reverberation and speckle noises in the FLS image, we used a series of preprocessing operations to reduce the noises. We trained and tested the network with data collected in the sea using a Gemini 720i multi-beam forward-looking sonar and compared it with state-of-the-art networks for object detection. In order to further prove that our detector can be applied to the actual detection task, we also carried out the experiment of detecting and avoiding fishing nets in real-time in the sea with the embedded single board computer (SBC) module and the NVIDIA Jetson AGX Xavier embedded system of the AUV platform in our lab. The experimental results show that in terms of computational complexity, inference time, and prediction accuracy, MRF-Net is better than state-of-the-art networks. In addition, our fishing net avoidance experiment results indicate that the detection results of MRF-Net can support the accurate operation of the later obstacle avoidance algorithm.

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