Advances in Autonomous Underwater Drones

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

Deadline for manuscript submissions: 16 April 2025 | Viewed by 4608

Special Issue Editors


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Guest Editor
Ocean College, Zhejiang University, Hangzhou, China
Interests: control of marine robotics
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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

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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

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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

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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

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Published Papers (2 papers)

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Research

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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 755
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 2364
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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