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Drones
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Advances in Autonomous Underwater Drones
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Advances in Autonomous Underwater Drones

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 16 April 2025 | Viewed by 6189

Special Issue Editors

Dr. Daxiong Ji

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Hangzhou, China
Interests: control of marine robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Asiya Khan

E-Mail Website
Guest Editor
Multimedia Communication and Intelligent Control, School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
Interests: prediction and control of video quality using AI, ML, cloud computing, fuzzy logic, applying computer vision techniques, and deep learning in pedestrian recognition; disease identification in cotton crops and damage recognition in wind turbines
Special Issues, Collections and Topics in MDPI journals
Dr. Pablo Borja

E-Mail Website
Guest Editor
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
Interests: control theory; passivity-based control; nonlinear control; port-hamiltonian systems
Dr. Dena Bazazian

E-Mail Website
Guest Editor
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
Interests: computer vision; deep learning; image processing; generative models; point clouds
Dr. Mohd Hisham Bin Nordin

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
Interests: intelligent monitoring system

Special Issue Information

Dear Colleagues,

The demand for the autonomous underwater drone market is rising at a considerable rate, and the market value is predicted to reach over USD 2.5 billion by 2028. Underwater drones are not only advanced robots that operate in the ocean, but they are also a new type of vehicle to be used in ocean research. The characteristics of water and air are significantly different. Therefore, the resistance and other characteristics affecting underwater drones have significant differences. The traditional underwater drones have a torpedo-like shape due to their low resistance and suitability for long-distance trajectories. However, even if the configuration type is a full-drive unmanned submersible, when it performs high-speed manoeuvring tasks, its auxiliary thrusters cannot meet the expected control requirements and will also enter the under-actuation mode. Recently, many new types of underwater drones have appeared with different shapes and driving units, such as cross-medium, multi-rotor, and soft robotic fish. The navigation, guidance, driving device, and control technology of the unmanned submersible are the basis and prerequisite for completing a series of underwater special operations. Due to the complex underwater operating environment and irregular shapes, it is often difficult to accurately obtain the hydrodynamic coefficients of autonomous underwater drones, resulting in inaccurate system dynamic models and even issues affected by unknown time-varying disturbances such as ocean currents. In addition, there are usually weak observations, weak perceptions, and weak communication constraints, rendering full-state feedback control infeasible. Moreover, the quality and colour of underwater images are usually low due to various environmental factors, such as light. Autonomous underwater drones rely on vision sensors to perceive their surroundings and make appropriate motion decisions when performing underwater movements. Therefore, in-depth research on new types of underwater drones has important engineering value and theoretical significance. The new concept of underwater drones has many scientific issues, e.g., cross-medium mechanisms, new propulsion mechanisms, and new materials that are worthy of in-depth research by scholars.

Advances in sensors, design, power, computer vision, and AI-based technologies can pave the way to operate these drones for extended periods of time and can be used to monitor and assess the health of underwater ecosystems without the need for human divers, which are costly and dangerous. These devices can explore areas that are too deep, too dangerous, or previously untouched. Underwater drones can help cover more ground in a shorter period of time and collect data more accurately. These devices can perform otherwise impossible tasks such as mapping the ocean floor, locating lost or abandoned objects, and finding rare or endangered species and territories. The demand for autonomous underwater drones is increasing in several diverse applications, such as (1) reef monitoring; (2) environmental monitoring and mapping; (3) offshore windfarm inspection; (4) object detection and tracking; (5) simultaneous localisation and mapping (SLAM); (6) bio-inspired design and collaboration; and 6) seabed pipeline inspection.

The purpose of this Special Issue is to publish cutting-edge advances in technologies and applications related to works in the design of drones, driving mechanisms, control algorithms, and navigation algorithms, as well as energy, materials, and other fields, which are in line with the scope of journal submissions. This Special Issue has great appeal and can promote the development of underwater drone technology research.

Dr. Daxiong Ji
Dr. Asiya Khan
Dr. Pablo Borja
Dr. Dena Bazazian
Dr. Mohd Hisham Bin Nordin
Guest Editors

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. Drones is an international peer-reviewed open access monthly 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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32 pages, 12104 KiB  
Article
RBFNN-Based Adaptive Fixed-Time Sliding Mode Tracking Control for Coaxial Hybrid Aerial–Underwater Vehicles Under Multivariant Ocean Disturbances
by Mingqing Lu, Wei Yang, Zhenyu Xiong, Fei Liao, Shichong Wu, Yumin Su and Wenhua Wu
Drones 2024, 8(12), 745; https://doi.org/10.3390/drones8120745 - 10 Dec 2024
Viewed by 285
Abstract
In this study, the design of an adaptive neural network-based fixed-time control system for a novel coaxial trans-domain hybrid aerial–underwater vehicle (HAUV) is investigated. A radial basis function neural network (RBFNN) approximation strategy-based adaptive fixed-time terminal sliding mode control (AFTSMC) scheme is proposed [...] Read more.
In this study, the design of an adaptive neural network-based fixed-time control system for a novel coaxial trans-domain hybrid aerial–underwater vehicle (HAUV) is investigated. A radial basis function neural network (RBFNN) approximation strategy-based adaptive fixed-time terminal sliding mode control (AFTSMC) scheme is proposed to solve the problems of the dynamic nonlinearity, model parameter perturbation, and multiple external disturbances of coaxial HAUV trans-media motion. A complete six-degrees-of-freedom model for a continuous water–air cross-domain model is first established based on the hyperbolic tangent transition function, and, subsequently, based on a basic framework of FTSMC, a fixed-time and fast-convergence controller is designed to track the target position and attitude signals. To reduce the dependence of the control scheme on precise model parameters, an RBFNN approximator is integrated into the sliding mode controller for the online model identification of the aggregate uncertainties of the coaxial HAUV, such as nonlinear unmodeled dynamics and external disturbances. At the same time, an adaptive technique is used to approximate the upper bound of the robust switching term gain in the controller, which further offsets the estimation error of the RBFNN and effectively attenuates the chattering effect. Based on Lyapunov stability theory, it is proven that the tracking error can converge in a fixed time. The effectiveness and superiority of the proposed control strategy are verified by several sets of simulation results obtained under typical working conditions. Full article
(This article belongs to the Special Issue Advances in Autonomous Underwater Drones)
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31 pages, 14091 KiB  
Article
An Enhanced Adaptive Ensemble Kalman Filter for Autonomous Underwater Vehicle Integrated Navigation
by Zeming Liang, Shuangshuang Fan, Jiacheng Feng, Peng Yuan, Jiangjiang Xu, Xinling Wang and Dongxiao Wang
Drones 2024, 8(12), 711; https://doi.org/10.3390/drones8120711 - 28 Nov 2024
Viewed by 494
Abstract
Autonomous Underwater Vehicles (AUVs) rely on integrated navigation systems and corresponding filtering algorithms to ensure mission success and the spatiotemporal accuracy of sampled data. Among these, the ensemble Kalman filter (EnKF) combines Monte Carlo methods with the Kalman filter, which is particularly suited [...] Read more.
Autonomous Underwater Vehicles (AUVs) rely on integrated navigation systems and corresponding filtering algorithms to ensure mission success and the spatiotemporal accuracy of sampled data. Among these, the ensemble Kalman filter (EnKF) combines Monte Carlo methods with the Kalman filter, which is particularly suited for nonlinear systems. This study proposes an enhanced adaptive EnKF algorithm to improve the smoothness and accuracy of the filtering process. Instead of the conventional Gaussian distribution, this algorithm employs a Laplace distribution to construct the system state vector and observation vector ensembles, enhancing stability against non-Gaussian noise. Additionally, the algorithm dynamically adjusts the number of vector members in the ensemble using adaptive mechanisms by specifying thresholds during filtering to adapt the requirements of real-world observational settings. Using field trial data from DVL, GPS, and electronic compass measurements, we optimize the algorithm’s parameter settings and evaluate the overall performance of the algorithm. Results indicate that the proposed adaptive EnKF achieves superior accuracy and smoothness performance. Compared to the conventional EnKF and EKF, it not only reduces the average positioning error by 30% and 44%, respectively, but also significantly improves the filtering smoothness and stability, highlighting its advantages for AUV navigation. Full article
(This article belongs to the Special Issue Advances in Autonomous Underwater Drones)
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23 pages, 2805 KiB  
Article
Autonomous Underwater Vehicle Docking Under Realistic Assumptions Using Deep Reinforcement Learning
by Narcís Palomeras and Pere Ridao
Drones 2024, 8(11), 673; https://doi.org/10.3390/drones8110673 - 13 Nov 2024
Viewed by 1086
Abstract
This paper addresses the challenge of docking an Autonomous Underwater Vehicle (AUV) under realistic conditions. Traditional model-based controllers are often constrained by the complexity and variability of the ocean environment. To overcome these limitations, we propose a Deep Reinforcement Learning (DRL) approach to [...] Read more.
This paper addresses the challenge of docking an Autonomous Underwater Vehicle (AUV) under realistic conditions. Traditional model-based controllers are often constrained by the complexity and variability of the ocean environment. To overcome these limitations, we propose a Deep Reinforcement Learning (DRL) approach to manage the homing and docking maneuver. First, we define the proposed docking task in terms of its observations, actions, and reward function, aiming to bridge the gap between theoretical DRL research and docking algorithms tested on real vehicles. Additionally, we introduce a novel observation space that combines raw noisy observations with filtered data obtained using an Extended Kalman Filter (EKF). We demonstrate the effectiveness of this approach through simulations with various DRL algorithms, showing that the proposed observations can produce stable policies in fewer learning steps, outperforming not only traditional control methods but also policies obtained by the same DRL algorithms in noise-free environments. Full article
(This article belongs to the Special Issue Advances in Autonomous Underwater Drones)
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Review

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51 pages, 4761 KiB  
Review
Polar AUV Challenges and Applications: A Review
by Shuangshuang Fan, Neil Bose and Zeming Liang
Drones 2024, 8(8), 413; https://doi.org/10.3390/drones8080413 - 22 Aug 2024
Cited by 1 | Viewed by 2750
Abstract
This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, [...] Read more.
This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, identifying and addressing the key technological challenges these vehicles face. These include advanced navigation techniques, strategic path planning, efficient obstacle avoidance, robust communication, stable energy supply, reliable launch and recovery, and thorough risk analysis. Furthermore, this study categorizes the typical capabilities and applications of AUVs in polar contexts, such as under-ice mapping and measurement, water sampling, ecological investigation, seafloor mapping, and surveillance networking. We also briefly highlight existing research gaps and potential future challenges in this evolving field. Full article
(This article belongs to the Special Issue Advances in Autonomous Underwater Drones)
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