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Intervention AUV

About: Intervention AUV is a research topic. Over the lifetime, 980 publications have been published within this topic receiving 14130 citations.


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Proceedings ArticleDOI
06 Oct 1997
TL;DR: In this paper, the authors describe the new requirements in terms of underwater navigation and communication, and explain how LEGS (low Earth orbital satellites) and wideband underwater acoustics transmissions can offer new perspectives to accurately locate the vehicles and control them from an Earth based remote station, giving them the supervision capability.
Abstract: The past fifteen years have seen the development of a new family of unmanned underwater vehicles without umbilical. Initially, those vehicles were operated in the direct vicinity of the mother-ship. With the increase in range and reliability, users are now willing to operate without support vessels. This creates new challenges as traditional equipment cannot cope with the constraints attached to these new missions. After describing the new requirements in terms of underwater navigation and communication, the authors explain how LEGS (low Earth orbital satellites) and wideband underwater acoustics transmissions can offer new perspectives to accurately locate the vehicles and control them from an Earth based remote station, giving them the supervision capability. During the 1995-96 period, a complete prototype was developed including 4 surface relay GIB (GPS Intelligent Buoys), a local radio network, a remote control station and the vehicle electronics. Results from sea trials performed in 1996-97, in water depth varying from 10 to 300 meters, are presented and discussed. The perspectives and future developments are also described.

2 citations

Dissertation
24 Jan 2008
TL;DR: In this paper, the authors developed an AUV sensing system, including both video and auxiliary sonar, which has the ability to carry out visually guided autonomous tracking of a particular species of fish, Large Mouth Bass.
Abstract: As the largest unexplored area on earth, the underwater world has unlimited attraction to marine scientists. Due to the complexity of the underwater environment and the limitations of human divers, underwater exploration has been facilitated by the use of submarines, Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). In recent years, use of autonomous control systems being integrated with visual sensors has increased substantially, especially in marine applications involving guidance of AUVs. In this work, autonomous fish-tracking via AUV with vision servoing control system is studied with the purpose of assisting marine biologists in gathering detailed information about the behaviors, habits, mobility, and local and global distributions of particular fish species. The main goal of this work in this thesis is to develop an AUV sensing system, including both video and auxiliary sonar, which has the ability to carry out visually guided autonomous tracking of a particular species of fish, Large Mouth Bass. A key in enabling fish-tracking involves the development of a vision-processing algorithm to measure the position of the vehicle relative to the fish. It is challenging because of the complex nature of the underwater environment including dynamic and varied lighting conditions, turbulent water, suspended organic particles and various underwater plants and animals, and the deformable body of fish while swimming. These issues cause target fish identification by computer vision processing extremely difficult. In automated fish-tracking work, we provide two valid and efficient segmentation and recognition vision algorithms to identify a fish from the natural underwater environment: one is a feature extraction algorithm based on Gabor filter texture segmentation and a new approach that we call projection curve recognition. It is able to extract the feature on the fish tail and body and successfully describe the fish as two straight line segments. The second algorithm is SIFT based fish recognition algorithm. The SIFT approach introduced by David Lowe in 1999 extracts distinctive invariant features to scaling, illumination, rotation or translation of the image. The reliable keypoints matching in the database of keypoints from target fish is implemented by Best-Bin-First (BBF) algorithm. Clustering keypoints that agree on the possible object with Hough transform are identified as the object fish, reliable recognition is possible with as few as 3 features. Finally, a dynamic recognition process was designed using continuously updated fish model to match and recognize the target fish from a series of video frames. The SIFT Based recognition algorithm is effective and efficient in identifying Large Mouth Bass in a natural cluttering underwater environment. For a monocular camera system, the depth of field is extremely hard to obtain by vision processing. Hence, the system is augmented with a forward-looking digital image micro sonar. With the sonar image processing algorithm, the target fish is recognized. Sonar can not only provide the relative range between the fish and AUV, but also assist in identifying the target.

2 citations

01 Jan 2016
TL;DR: Remotely Operated Vehicles (ROVs) as mentioned in this paper are underwater robots that perform different kinds of operations, from observation to heavy tasks like drilling, carrying and pulling cables, etc.
Abstract: Remotely Operated Vehicles (ROVs) are underwater robots that perform different kind of operations, from observation to heavier tasks like drilling, carrying and pulling cables, etc. Those ROVs are ...

2 citations

Proceedings ArticleDOI
01 Oct 2012
TL;DR: This paper proposes a control system for networked autonomous underwater vehicles that includes both hardware and software modules and contains a graphical user interface for optional manual remote control and monitoring of AUVs in the network from a base station.
Abstract: Autonomous Underwater Vehicle (AUV) networks are becoming increasingly popular in scientific, commercial, and military applications. AUVs are used in undersea exploration and environmental monitoring for tasks such as detection of oilfields and marine life, distributed tactical surveillance for offshore and seaport defense and mine reconnaissance. AUV networks are also becoming an important interest in an effort to enhance the capabilities of underwater sensor networks (UWSNs). In this paper we propose a control system for networked autonomous underwater vehicles that includes both hardware and software modules. The system integrates various communication and sensor devices, such as an IMU and an OFDM acoustic modem and contains a graphical user interface for optional manual remote control and monitoring of AUVs in the network from a base station.

2 citations

Journal Article
TL;DR: The design class was challenged to modify the AUV Autolycus to incorporate environment-based control and to double the vehicle's maximum controllable velocity, and preliminary successes in wall-following and other environment-triggered behaviors were achieved.
Abstract: The development of underwater robotic vehicles throughout the past 30 years has contributed significantly to various types of underwater search, survey, and recovery applications. To learn about these complex systems, the MIT Design of Ocean Systems Class of 1997 designed and built an inexpensive Autonomous Underwater Vehicle (AUV), Autolycus. The Design Class of 1998 further developed this vehicle, adding a sonar altimeter to its sensor array, as well as integrating a dead reckoning navigation system. In the spring of 1999, the design class was challenged to modify the AUV Autolycus to incorporate environment-based control and to double the vehicle's maximum controllable velocity. To succeed in these modifications, a thorough understanding of state-of-the-art AUV systems, design of new hardware and software systems, and extensive testing and evaluation of these systems were required. Results of these efforts included the addition of a five-channel sonar system, new main thrusters, and a new control algorithm used to control the new hardware. Preliminary successes in wall-following and other environment-triggered behaviors were achieved.

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202311
202220
20211
20201
20192
20183