A long term vision for long-range ship-free deep ocean operations: Persistent presence through coordination of Autonomous Surface Vehicles and Autonomous Underwater Vehicles

2012 ◽  
Author(s):  
Christopher R. German ◽  
Micheal V. Jakuba ◽  
James C. Kinsey ◽  
Jim Partan ◽  
Stefano Suman ◽  
...  
Keyword(s):  
Long Range ◽  
Autonomous Underwater Vehicles ◽  
Deep Ocean ◽  
Underwater Vehicles ◽  
Autonomous Surface Vehicles

scholarly journals Feedback Motion Planning for Long-Range Autonomous Underwater Vehicles

2019 ◽  
Author(s):  
Opeyemi S. Orioke ◽  
Tauhidul Alam ◽  
Joseph Quinn ◽  
Ramneek Kaur ◽  
Wesam H. Alsabban ◽  
...  
Keyword(s):  
Motion Planning ◽  
Long Range ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles

Docking Method for Hovering-Type AUVs Based on Acoustic and Optical Landmarks

2018 ◽  
Vol 30 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Toshihiro Maki ◽  
◽  
Yoshiki Sato ◽  
Takumi Matsuda ◽  
Kotohiro Masuda ◽  
...  
Keyword(s):  
Power Transmission ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Human Intervention ◽  
Contact Charging ◽  
Human Control ◽  
Docking Method ◽  
Position And Orientation

Autonomous underwater vehicles (AUVs) have the advantage of not requiring tether cables or human control; however, they have limited energy, and must be recovered before their batteries drain completely. To charge AUV batteries efficiently, in-situ charging systems have attracted much attention. This study proposes a method for hovering-type AUVs to dock at a seafloor station, for long-term deployment of the system with minimum human intervention. In the proposed method, an AUV docks at a seafloor station autonomously, based on both acoustic and optical landmarks attached to the station. The AUV stochastically estimates its position and orientation with regard to the station, and controls itself to land on the exact docking spot at the station. When docking is completed, the station begins electric power transmission via non-contact charging devices. The proposed method was evaluated on the AUV Tri-TON 2, and a seafloor station testbed. The vehicle succeeded in autonomous docking at the station in both the tank and sea trials. Non-contact charging during docking was also verified during the tank experiments, using the non-contact charging devices developed by our group.

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 Towards Energy-Aware Feedback Planning for Long-Range Autonomous Underwater Vehicles

2021 ◽  
Vol 8 ◽  
Author(s):  
Tauhidul Alam ◽  
Abdullah Al Redwan Newaz ◽  
Leonardo Bobadilla ◽  
Wesam H. Alsabban ◽  
Ryan N. Smith ◽  
...  
Keyword(s):  
Long Range ◽  
Autonomous Underwater Vehicles ◽  
Kinematic Model ◽  
Underwater Vehicles ◽  
Ocean Model ◽  
Ocean Dynamics ◽  
Energy Aware ◽  
Goal Location ◽  
Water Currents ◽  
Initial Location

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.

Review of AUV Underwater Terrain Matching Navigation

2015 ◽  
Vol 68 (6) ◽  
pp. 1155-1172 ◽  
Author(s):  
Pengyun Chen ◽  
Ye Li ◽  
Yumin Su ◽  
Xiaolong Chen ◽  
Yanqing Jiang
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Research Area ◽  
Composition Theory ◽  
System Composition ◽  
Key Issues ◽  
Important Research Area ◽  
Underwater Navigation ◽  
Terrain Matching

Underwater terrain matching navigation technology is an important research area for the underwater navigation of Autonomous Underwater Vehicles (AUVs). Terrain matching navigation can realise long-term, subtle, all-weather, and high-precision underwater AUV navigation. In this paper, the research status of the application of AUV underwater terrain matching navigation is reviewed, the system composition, theory and terrain matching methods of underwater terrain matching navigation are summarised and the advantages of a multi-beam bathymetric system in underwater terrain matching navigation are discussed. The current research thoughts are summarised, the key issues are pointed out, and possible future development trends are discussed.

AUV Bathymetric Simultaneous Localisation and Mapping Using Graph Method

2019 ◽  
Vol 72 (06) ◽  
pp. 1602-1622
Author(s):  
Teng Ma ◽  
Ye Li ◽  
Yusen Gong ◽  
Rupeng Wang ◽  
Mingwei Sheng ◽  
...  
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Data Association ◽  
Underwater Vehicles ◽  
Geological Surveys ◽  
Underwater Navigation ◽  
Graph Slam ◽  
Mapping Techniques ◽  
Terrain Matching ◽  
Global And Local

Although topographic mapping missions and geological surveys carried out by Autonomous Underwater Vehicles (AUVs) are becoming increasingly prevalent, the lack of precise navigation in these scenarios still limits their application. This paper deals with the problems of long-term underwater navigation for AUVs and provides new mapping techniques by developing a Bathymetric Simultaneous Localisation And Mapping (BSLAM) method based on graph SLAM technology. To considerably reduce the calculation cost, the trajectory of the AUV is divided into various submaps based on Differences of Normals (DoN). Loop closures between submaps are obtained by terrain matching; meanwhile, maximum likelihood terrain estimation is also introduced to build weak data association within the submap. Assisted by one weight voting method for loop closures, the global and local trajectory corrections work together to provide an accurate navigation solution for AUVs with weak data association and inaccurate loop closures. The viability, accuracy and real-time performance of the proposed algorithm are verified with data collected onboard, including an 8 km planned track recorded at a speed of 4 knots in Qingdao, China.

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