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Smart Sensors for Autonomous Underwater Vehicle

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 14956

Special Issue Editor


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Guest Editor
Ricercatore di Misure Elettriche ed Elettroniche, Università degli Studi "Roma Tre", Rome, Italy
Interests: measurement chains; sensors; AUV; reliability; integrated logistic support; neural networks; power quality
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Special Issue Information

Dear Colleagues,

Autonomous underwater vehicles (AUV) are an extremely heterogeneous group of vehicles of various sizes that are able to operate in a submarine environment with a certain degree of autonomy with respect to control by human beings, or being operated remotely. This category is so vast that it is often customary to group completely different systems under this common term, from simple robots of a few decimetres to large complex systems of several meters in length, equipped with energy autonomy and controlled without cables. They are equipped with a battery compartment for propulsion and operation of on-board systems, use wireless communication, and are generally acoustic, to receive instructions or to transfer collected data to the control station. They can be equipped with more or less reduced decision-making autonomy, depending on the model and the construction costs: the simplest AUVs are monitored by an operator who supervises the mission and transmits any changes to the tasks assigned in real time. Other more complex platforms have sophisticated self-navigation systems, using the GPS system or, much more frequently, appropriate acoustic buoys positioned in the area of interest, which continuously provide a reference on the position in three dimensions. Mission data can be pre-set and updated at regular intervals.

Considering their application fields, their use, even their dual use, will increase a lot in the near future.

For systems so complex, which are able to reach great depths, the payload for performing a mission using sensors to obtain the right control is considerable.

The Special Issue focuses on sensors addressed to the control of the AUV, that are able to perform specific tasks for specific missions; moreover, the Guest Editor encourages the scientific community to provide original research, even to correlated arguments.

This Issue will accept high-quality articles that contain original research results, and review articles, and will allow readers to learn more about technologies related to the potentiality of AUVs.

Therefore, articles reporting recent advances in sensor materials, sensor properties, sensor device concepts, sensor fabrication and testing techniques, application-oriented sensor systems, as well as closely-related topics, are welcome.

Prof. Dr. Fabio Leccese
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  •  Sonar
  •  Magnetometer
  •  Barometer
  •  Accelerometer
  •  Conductivity, tachometer, depth (CTD)
  •  Environmental Sensors (temperature, salinity, oxygen, sea-point turbidity, and fluorescence)
  •  Beam transmission
  •  Chemical sensors
  •  Inertial navigation system
  •  Buoyancy system
  •  Attitude system
  •  New sensors for in-depth applications
  •  Sensor network
  •  Calibration, characterization, and testing techniques
  •  Uncertainty evaluation
  •  Reliability and failure analysis
  •  Processes and fabrication technologies
  •  Material research oriented to the development of sensors in water applications
  •  Electronic interfaces, measurement chains, and system integration
  •  Applications and markets, control and measurement systems
  •  Operative scenario description (missions and tasks)
  •  AUV designing
  •  Integration support logistic.

Published Papers (3 papers)

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16 pages, 3616 KiB  
Article
Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion
by Enrico Petritoli, Marco Cagnetti and Fabio Leccese
Sensors 2020, 20(17), 4950; https://doi.org/10.3390/s20174950 - 01 Sep 2020
Cited by 17 | Viewed by 3905
Abstract
The paper shows the simulation of the behavior of a swarm of underwater drones (AUV) diffused in a closed section of the sea and inserted from a single starting point: Based on a few essential rules, we will see how their behavior evolves [...] Read more.
The paper shows the simulation of the behavior of a swarm of underwater drones (AUV) diffused in a closed section of the sea and inserted from a single starting point: Based on a few essential rules, we will see how their behavior evolves and how they manage to spread throughout the area assigned to them. In the first part of this work, after defining the design of the vehicle, we introduce our vision of the swarm, its problems, and its strengths. Later, we show how to spread a series of underwater drones with “diffused intelligence” (swarm) and its microscopic diffusion model. In the last part, we present the simulation that supports our approach to the swarm. Full article
(This article belongs to the Special Issue Smart Sensors for Autonomous Underwater Vehicle)
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29 pages, 12170 KiB  
Article
Study on Dynamic Behavior of Unmanned Surface Vehicle-Linked Unmanned Underwater Vehicle System for Underwater Exploration
by Mai The Vu, Mien Van, Duc Hong Phuc Bui, Quang Thang Do, Tuan-Tu Huynh, Sang-Do Lee and Hyeung-Sik Choi
Sensors 2020, 20(5), 1329; https://doi.org/10.3390/s20051329 - 29 Feb 2020
Cited by 60 | Viewed by 6390
Abstract
This paper focuses on motion analysis of a coupled unmanned surface vehicle (USV)–umbilical cable (UC)–unmanned underwater vehicle (UUV) system to investigate the interaction behavior between the vehicles and the UC in the ocean environment. For this, a new dynamic modeling method for investigating [...] Read more.
This paper focuses on motion analysis of a coupled unmanned surface vehicle (USV)–umbilical cable (UC)–unmanned underwater vehicle (UUV) system to investigate the interaction behavior between the vehicles and the UC in the ocean environment. For this, a new dynamic modeling method for investigating a multi-body dynamics system of this coupling system is employed. Firstly, the structure and hardware composition of the proposed system are presented. The USV and UUV are modeled as rigid-body vehicles, and the flexible UC is discretized using the catenary equation. In order to solve the nonlinear coupled dynamics of the vehicles and flexible UC, the fourth-order Runge–Kutta numerical method is implemented. In modeling the flexible UC dynamics, the shooting method is applied to solve a two-point boundary value problem of the catenary equation. The interaction between the UC and the USV–UUV system is investigated through numerical simulations in the time domain. Through the computer simulation, the behavior of the coupled USV–UC–UUV system is analyzed for three situations which can occur. In particular, variation of the UC forces and moments at the tow points and the configuration of the UC in the water are investigated. Full article
(This article belongs to the Special Issue Smart Sensors for Autonomous Underwater Vehicle)
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21 pages, 3886 KiB  
Article
Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences
by Jie Sun, Feng Hu, Wenming Jin, Jin Wang, Xu Wang, Yeteng Luo, Jiancheng Yu and Aiqun Zhang
Sensors 2020, 20(3), 893; https://doi.org/10.3390/s20030893 - 07 Feb 2020
Cited by 12 | Viewed by 3207
Abstract
An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended [...] Read more.
An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended Kalman filter (EKF)-based method, combining a modified kinematic model of underwater gliders with the travel-time differences between signals received from a single beacon, is proposed for estimating the glider positions in a predict-update cycle. First, to accurately establish a motion model for underwater gliders moving in the ocean, we introduce two modification parameters, the attack and drift angles, into a kinematic model of underwater gliders, along with depth-averaged current velocities. The attack and drift angles are calculated based on the coefficients of hydrodynamic forces and the sensor-measured angle variation over time. Then, instead of satisfying synchronization requirements, the travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To further reduce the EKF estimation error, the Rauch-Tung-Striebel (RTS) smoothing method is merged into the EKF system. The proposed method is tested in a virtual spatiotemporal environment from an ocean model. The experimental results show that the performance of the RTS-EKF estimate is improved when compared with the motion model estimate, especially by 46% at the inflection point, at least in the particular study developed in this article. Full article
(This article belongs to the Special Issue Smart Sensors for Autonomous Underwater Vehicle)
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