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Underwater Vehicles
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Underwater Vehicles

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 21451

Special Issue Editors

Prof. Dr. Timothy Sands

E-Mail Website1 Website2
Guest Editor
Department of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
Interests: robotics; deterministic artificial intelligence; motion mechanics; system identification; guidance, navigation, and control; autonomy; technical translation; nuclear deterrence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kevin Bollino

E-Mail Website1 Website2
Guest Editor
Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: optimal control; autonomous vehicles; orbital mechanics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jamie MacMahan

E-Mail Website1 Website2
Guest Editor
Department of Oceanography, Naval Postgraduate School, Monterey, CA 93940, USA
Interests: rip currents; subzone drifters; cross-shore exchange; Inlet residence time; subtidal and tidal wave propagation; DELFT3D evaluation; coherent riverine motions and scales; DELFT3D modeling; river mixing and transport

Special Issue Information

Dear Colleagues,

Vehicles operating autonomously in challenging ocean environments remain an interesting area of continuous research. This Special Issue includes ocean characterization and other topics of oceanography in addition to topics relating to the vehicle and its operation. The issue is intended to be broad in scope to expand readers’ thoughts beyond their own individual discipline. Authors are urged to consider the cross-disciplinary context and submit their manuscripts for rigorous peer review. Some keywords are included but should not be considered a limiting set.

Prof. Dr. Timothy Sands
Prof. Dr. Kevin Bollino
Prof. Dr. Jamie MacMahan
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. Applied Sciences 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 2400 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

  • Navigation and localization
  • Path planning and control
  • Design and modeling
  • Oceanography
  • Ocean acoustics
  • Sea ice mapping
  • Oil exploration
  • Bioacoustics
  • Communications
  • Marine science
  • Tomography
  • Marine engineering
  • Mechanics
  • Kinematics
  • Dynamics
  • Artificial intelligence

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Published Papers (6 papers)

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Research

24 pages, 35627 KiB  
Article
Preliminary Design and Testing of a Resetting Combination Anchor, Antenna, and Tether Mechanism for a Spherical Autonomous Underwater Vehicle
by Ross Eldred and Douglas L. Van Bossuyt
Appl. Sci. 2022, 12(10), 5072; https://doi.org/10.3390/app12105072 - 18 May 2022
Viewed by 2124
Abstract
This article details the preliminary design and testing of a Resetting Anchor/Antenna Tether Mechanism (RAATM) for an autonomous underwater vehicle (AUV). The proposed mechanism is intended to enable an AUV to secure itself to the seabed, ascend, descend, transmit and receive signals via [...] Read more.
This article details the preliminary design and testing of a Resetting Anchor/Antenna Tether Mechanism (RAATM) for an autonomous underwater vehicle (AUV). The proposed mechanism is intended to enable an AUV to secure itself to the seabed, ascend, descend, transmit and receive signals via the tether, retract the anchor, and re-anchor again as required. The ability of an AUV to passively loiter on station for extended periods preserves power and may otherwise expand mission capabilities for a variety of underwater vehicles. If they are capable of communication through electromagnetic transmission, AUVs equipped with such technology may be utilized to form mobile networks that may, in turn, receive external communications from above the surface. Spherical AUV (SAUV) capabilities may be especially enhanced through the integration of the proposed mechanism. The RAATM was designed for integration with the Wreck Interior Exploration Vehicle (WIEVLE), a small SAUV designed for operations in entanglement-prone, extreme environments, but the RAATM may be used in any suitably-sized underwater vehicle capable of safely contacting the ocean floor. A prototype of the anchoring portion of the mechanism was constructed, and anchoring strength was tested repeatedly in three types of sediment, under varied configurations and loading angles, with promising results. Full article
(This article belongs to the Special Issue Underwater Vehicles)
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63 pages, 3429 KiB  
Article
Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
by Shay Osler and Timothy Sands
Appl. Sci. 2022, 12(6), 2810; https://doi.org/10.3390/app12062810 - 9 Mar 2022
Cited by 14 | Viewed by 2779
Abstract
Unmanned ocean vehicles can be guided and controlled autonomously or remotely, and even remote operation can be automated significantly. Classical methods use trajectory tracking errors in negative feedback. Recently published methods are proposed instead. Deterministic (non-stochastic) artificial intelligence (DAI) combines optimal learning with [...] Read more.
Unmanned ocean vehicles can be guided and controlled autonomously or remotely, and even remote operation can be automated significantly. Classical methods use trajectory tracking errors in negative feedback. Recently published methods are proposed instead. Deterministic (non-stochastic) artificial intelligence (DAI) combines optimal learning with an asserted self awareness statement in the form of the governing mathematical model (based on physics in this instantiation) to allow control that can be alternatively adaptive (i.e., capable of reacting to changing system dynamics) or learning (i.e., able to provide information about what aspects of the system dynamics have changed). In this manuscript, deterministic artificial intelligence is applied to the heading control of a simulated remotely operated underwater vehicle (ROV). Research is presented illustrating autonomous control of a Seabotix vLBV 300 remotely operated vehicle within milli-degrees on the very first step of a shaped square wave command, and error decreased an additional sixty-two percent by the third step of the square wave command. Full article
(This article belongs to the Special Issue Underwater Vehicles)
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17 pages, 3367 KiB  
Article
Model-Validation and Implementation of a Path-Following Algorithm in an Autonomous Underwater Vehicle
by Jose Villa, Guillem Vallicrosa, Jussi Aaltonen, Pere Ridao and Kari T. Koskinen
Appl. Sci. 2021, 11(24), 11891; https://doi.org/10.3390/app112411891 - 14 Dec 2021
Cited by 2 | Viewed by 3160
Abstract
This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including [...] Read more.
This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including the model validation based on field test data. Then, the guidance system incorporates a line-of-sight (LOS) algorithm with a combination of position PID controllers. The GNC architecture includes a modular and multi-layer approach with an LOS-based, path-following algorithm in the AUV platform. Furthermore, the navigation used in the path-following algorithm is developed based on a predefined coverage area. Finally, this study addresses simulation and field test control scenarios to verify the developed GNC architecture. Full article
(This article belongs to the Special Issue Underwater Vehicles)
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16 pages, 29676 KiB  
Article
Comparing Methods of DC Motor Control for UUVs
by Rohan Shah and Timothy Sands
Appl. Sci. 2021, 11(11), 4972; https://doi.org/10.3390/app11114972 - 28 May 2021
Cited by 23 | Viewed by 3674
Abstract
Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods [...] Read more.
Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods such as recursive least squares, extended least squares, and batch least squares. Another approach referred to as deterministic artificial intelligence uses the process dynamics defined by physics to control output to track a necessarily specified autonomous trajectory (sinusoidal versions implemented here). In addition, one instantiation of deterministic artificial intelligence uses 2-norm optimal feedback learning of parameters to modify the control signal, while another instantiation is presented with proportional plus derivative adaption. Model-following and deterministic artificial intelligence are simulated, and respective performance metrics for transient response and input tracking are evaluated and compared. Deterministic artificial intelligence outperformed the model-following approach in minimal peak transient value by a percent range of approximately 2–70%, but model-following achieved at least 29% less error in input tracking than deterministic artificial intelligence. This result is surprising and not in accordance with the recently published literature, and the explanation of the difference is theorized to be efficacy with discretized implementations. Full article
(This article belongs to the Special Issue Underwater Vehicles)
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13 pages, 5613 KiB  
Article
Control of DC Motors to Guide Unmanned Underwater Vehicles
by Timothy Sands
Appl. Sci. 2021, 11(5), 2144; https://doi.org/10.3390/app11052144 - 28 Feb 2021
Cited by 27 | Viewed by 4223
Abstract
Many research manuscripts propose new methodologies, while others compare several state-of-the-art methods to ascertain the best method for a given application. This manuscript does both by introducing deterministic artificial intelligence (D.A.I.) to control direct current motors used by unmanned underwater vehicles (amongst other [...] Read more.
Many research manuscripts propose new methodologies, while others compare several state-of-the-art methods to ascertain the best method for a given application. This manuscript does both by introducing deterministic artificial intelligence (D.A.I.) to control direct current motors used by unmanned underwater vehicles (amongst other applications), and directly comparing the performance of three state-of-the-art nonlinear adaptive control techniques. D.A.I. involves the assertion of self-awareness statements and uses optimal (in a 2-norm sense) learning to compensate for the deleterious effects of error sources. This research reveals that deterministic artificial intelligence yields 4.8% lower mean and 211% lower standard deviation of tracking errors as compared to the best modeling method investigated (indirect self-tuner without process zero cancellation and minimum phase plant). The improved performance cannot be attributed to superior estimation. Coefficient estimation was merely on par with the best alternative methods; some coefficients were estimated more accurately, others less. Instead, the superior performance seems to be attributable to the modeling method. One noteworthy feature is that D.A.I. very closely followed a challenging square wave without overshoot—successfully settling at each switch of the square wave—while all of the other state-of-the-art methods were unable to do so. Full article
(This article belongs to the Special Issue Underwater Vehicles)
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14 pages, 11507 KiB  
Article
Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles
by Shihming Chen, Tsungyin Lin, Kaiyi Jheng and Chengmao Wu
Appl. Sci. 2020, 10(17), 6105; https://doi.org/10.3390/app10176105 - 2 Sep 2020
Cited by 10 | Viewed by 3018
Abstract
Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted [...] Read more.
Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted with vehicles in a real environment. This study used an ROV equipped with a scanning sonar as an experimental platform and applied fuzzy logic control to solve nonlinear and uncertain problems, which are difficult to address using conventional control theory. Using data from the depth and inertial sensors, fuzzy logic control can output defuzzification command values that are passed through a fuzzy inference engine to control ROV motion. Fuzzy logic control was used to evaluate depth and heading degrees in navigation experiments. In heading navigation, scanning sonar was used to detect obstacles in the scanning range. An optimum navigation strategy was also developed to calculate appropriate headings to safely and stably navigate during a mission to attain a predetermined destination. The results indicated that the ROV with fuzzy logic control had superior control stability and obstacle avoidance in an underwater environment. Full article
(This article belongs to the Special Issue Underwater Vehicles)
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