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Drones
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Advances in Autonomy of Underwater Vehicles (AUVs)
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Advances in Autonomy of Underwater Vehicles (AUVs)

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

Deadline for manuscript submissions: 10 March 2025 | Viewed by 2556

Special Issue Editors

Prof. Dr. Neil Bose

E-Mail Website
Guest Editor
Office of the President and Vice-Chancellor, Memorial University, St. John’s, NL A1B 1T5, Canada
Interests: autonomous underwater vehicles; ocean environmental monitoring; adaptivce sampling; marine propulsion; ocean renewable energy
Special Issues, Collections and Topics in MDPI journals
Dr. Shuangshuang Fan

E-Mail Website
Guest Editor
School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, China
Interests: guidance, navigation and control of underwater vehicles in dynamic environments; mission-oriented autonomy development
Special Issues, Collections and Topics in MDPI journals
Dr. Jimin Hwang

E-Mail Website
Guest Editor
Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: autonomous underwater vehicle; ocean environmental protection; adaptive mission control; autonomous water sampling

Special Issue Information

Dear Colleagues,

The exploration and exploitation of underwater environments have long been of interest to scientists and engineers, driven by the need to understand marine ecosystems, exploit underwater resources, and secure maritime interests. Underwater vehicles, both manned and unmanned, have become indispensable tools in these endeavors. Among them, autonomous underwater vehicles (AUVs) have garnered significant attention due to their capability to operate without human intervention, enabling extended missions in complex and often hazardous underwater environments.

The scientific and technological advancements in underwater vehicle autonomy are rooted in several key areas: artificial intelligence (AI), machine learning, robotics, and sensor technology. The development of sophisticated algorithms allows AUVs to perform complex tasks such as navigation, obstacle avoidance, and data collection with minimal human input. Machine learning and AI enable these vehicles to adapt to dynamic underwater conditions, improving their efficiency and reliability. Advances in sensor technology, including sonar, optical cameras, and environmental sensors, enhance the vehicle’s ability to perceive and interpret its surroundings.

The autonomy of underwater vehicles represents a critical frontier in marine science and engineering, with significant implications for both scientific research and practical applications. Autonomous systems can conduct extensive surveys of the ocean floor, monitor marine life, and collect data in environments that are otherwise inaccessible or too dangerous for human operators. This capability is particularly crucial for deep-sea or under-ice exploration, where the extreme conditions challenge even the most advanced manned submersibles.

The goal of this Special Issue is to collect and present a comprehensive collection of papers, including original research articles and review papers, that provide in-depth insights into the latest advancements in the autonomy of underwater vehicles. This Special Issue aims to highlight the cutting-edge developments in autonomous systems, focusing on the technological innovations, algorithms, and practical applications that are driving the next generation of underwater vehicles. By bringing together contributions from leading researchers and experts in the field, this Special Issue seeks to showcase the state-of-the-art in underwater vehicle autonomy, explore emerging trends, and identify future research directions. Through these collected works, we hope to provide a valuable resource for academics, engineers, and industry professionals involved in marine robotics and underwater technology, fostering further innovation and collaboration in this rapidly evolving area.

This Special Issue will welcome manuscripts that link the following themes:

  • Advanced autonomy algorithms: innovations in AI, machine learning, and decision-making processes that enhance the autonomy of underwater vehicles, including navigation, path planning, and real-time adaptive behaviors.
  • Sensor integration and perception: the development and integration of advanced sensors for environmental perception, obstacle detection, and situational awareness in complex underwater environments.
  • Energy efficiency and power management: research on power systems, energy storage, and energy-efficient algorithms that extend the operational duration and range of autonomous underwater vehicles.
  • Communication and data transmission: advances in underwater communication technologies and data transmission methods that support real-time control and data sharing between autonomous vehicles and surface stations.
  • Multi-vehicle coordination and swarming: studies on the coordination of multiple autonomous underwater vehicles, including swarm intelligence, collaborative task execution, and distributed control systems.
  • Autonomy in challenging environments: exploration of how autonomous underwater vehicles operate in extreme or dynamic environments, such as deep-sea, polar regions, or turbulent waters, and the specific challenges involved.
  • Applications in scientific research: case studies and applications where autonomous underwater vehicles have been successfully deployed for oceanographic research, environmental monitoring, marine biology, and other scientific explorations.
  • Industrial and commercial applications: the role of autonomous underwater vehicles in industrial applications, including offshore oil and gas exploration, undersea infrastructure inspection, and resource mapping.
  • Simulation and testing platforms: development of simulation environments, testing protocols, and validation methods for assessing the performance and reliability of autonomous underwater vehicles.
  • Ethical and regulatory considerations: discussions on the ethical, legal, and regulatory aspects of deploying autonomous systems in underwater environments, addressing issues such as safety, environmental impact, and governance.

This Special Issue encourages submissions that provide novel insights, propose new methodologies, or present significant case studies that link these themes, contributing to the broader understanding and advancement of underwater vehicle autonomy.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Neil Bose
Dr. Shuangshuang Fan
Dr. Jimin Hwang
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.

Keywords

  • autonomous underwater vehicles (AUVs)
  • underwater gliders
  • guidance, navigation, and control
  • informative planning
  • AI and machine learning
  • online decision-making
  • adaptive sampling
  • sensor fusion
  • underwater communication
  • multi-vehicle coordination
  • mission-oriented autonomy

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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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

19 pages, 10755 KiB  
Article
Enhancing the Performance of Novel Archimedes Spiral Hydrokinetic Turbines Utilizing Blade Winglets in Deep-Sea Power Generation for Autonomous Underwater Vehicles
by Ke Song, Huiting Huan, Liuchuang Wei and Chunxia Liu
Drones 2025, 9(1), 72; https://doi.org/10.3390/drones9010072 - 18 Jan 2025
Viewed by 434
Abstract
Deep-sea exploration relies heavily on autonomous underwater vehicles (AUVs) for data acquisition, but their operational endurance is limited by battery constraints. The Archimedes spiral hydrokinetic turbine (ASHT), as a novel type of horizontal-axis hydrokinetic turbine, has emerged as a promising solution for the [...] Read more.
Deep-sea exploration relies heavily on autonomous underwater vehicles (AUVs) for data acquisition, but their operational endurance is limited by battery constraints. The Archimedes spiral hydrokinetic turbine (ASHT), as a novel type of horizontal-axis hydrokinetic turbine, has emerged as a promising solution for the harnessing of localized energy in the deep sea to power AUVs. This study explores the application of winglets on an ASHT to enhance its performance through computational fluid dynamics (CFD). The analysis focuses on the effects of the winglet angle and height ratio on the power and thrust, as well as the pressure distribution and flow characteristics. The findings indicate that strategically designed winglets, particularly those with angles greater than 90° and larger height ratios, can significantly improve the ASHT’s performance. This enhancement can be attributed to the winglets’ capacity to effectively reduce tip loss and expand the turbine’s swept area, thereby enhancing power extraction. The optimal configuration, determined at a winglet angle of 135° and a height ratio of 12–14%, demonstrates significant enhancements, including a minimum increase of 12.0% in power efficiency compared to the original ASHT. However, the study also acknowledges potential challenges; winglets with larger angles and height ratios may lead to increased load fluctuations, which require careful structural considerations. This study provides valuable insights into the design and optimization of ASHTs for deep-sea power generation, thereby contributing to the advancement of sustainable energy solutions for AUVs. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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25 pages, 3925 KiB  
Article
Finite-Time Path-Following Control of Underactuated AUVs with Actuator Limits Using Disturbance Observer-Based Backstepping Control
by MohammadReza Ebrahimpour and Mihai Lungu
Drones 2025, 9(1), 70; https://doi.org/10.3390/drones9010070 - 18 Jan 2025
Viewed by 238
Abstract
This paper presents a three-dimensional (3D) robust adaptive finite-time path-following controller for underactuated Autonomous Underwater Vehicles (AUVs), addressing model uncertainties, external disturbances, and actuator magnitude and rate saturations. A path-following error system is built in a path frame using the virtual guidance method. [...] Read more.
This paper presents a three-dimensional (3D) robust adaptive finite-time path-following controller for underactuated Autonomous Underwater Vehicles (AUVs), addressing model uncertainties, external disturbances, and actuator magnitude and rate saturations. A path-following error system is built in a path frame using the virtual guidance method. The proposed cascaded closed-loop control scheme can be described in two separate steps: (1) A kinematic law based on a finite-time backstepping control (FTBSC) is introduced to transform the 3D path-following position errors into the command velocities; (2) The actual control inputs are designed in the dynamic controller using an adaptive fixed-time disturbance observer (AFTDO)-based FTBSC to stabilize the velocity tracking errors. Moreover, the adverse effects of magnitude and rate saturations are reduced by an auxiliary compensation system. A Lyapunov-based stability analysis proves that the path-following errors converge to an arbitrarily small region around zero within a finite time. Comparative simulations illustrate the effectiveness and robustness of the proposed controller. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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19 pages, 2324 KiB  
Article
Safety-Critical Trajectory Tracking Control with Safety-Enhanced Reinforcement Learning for Autonomous Underwater Vehicle
by Tianli Li, Jiaming Tao, Yu Hu, Shiyu Chen, Yue Wei and Bo Zhang
Drones 2025, 9(1), 65; https://doi.org/10.3390/drones9010065 - 16 Jan 2025
Viewed by 546
Abstract
This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the safety and performance of AUVs [...] Read more.
This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the safety and performance of AUVs operating in complex underwater environments. The RL framework can learn the inherent model uncertainties that affect the constraints in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). These learned uncertainties are subsequently integrated for formulating a novel RL-CBF-CLF Quadratic Programming (RL-CBF-CLF-QP) controller. Corresponding simulations are demonstrated under diverse trajectory tracking scenarios with high levels of model uncertainties. The simulation results show that the proposed RL-CBF-CLF-QP controller can significantly improve the safety and accuracy of the AUV’s tracking performance. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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29 pages, 11493 KiB  
Article
Three-Dimensional Path Following Control for Underactuated AUV Based on Ocean Current Observer
by Long He, Ya Zhang, Shizhong Li, Bo Li and Zeihui Yuan
Drones 2024, 8(11), 672; https://doi.org/10.3390/drones8110672 - 13 Nov 2024
Viewed by 912
Abstract
In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based [...] Read more.
In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based on a current observer and nonlinear disturbance observer (NDO) has been developed, addressing the 3D path-following issue for AUVs operating in the ocean environment. Accounting for uncertainties like variable ocean currents, this research establishes the AUV’s kinematics and dynamics models and formulates the tracking error within the Frenet–Serret coordinate system. The kinematic controller is designed through the line-of-sight method and the backstepping method, and the dynamic controller is developed using the nonlinear disturbance observer and the integral sliding mode control method. Furthermore, an ocean current observer is developed for the real-time estimation of current velocities, thereby mitigating the effects of ocean currents on navigational performance. Theoretical analysis confirms the system’s asymptotic stability, while numerical simulation attests to the proposed method’s efficacy and robustness in 3D path following. Full article
(This article belongs to the Special Issue Advances in Autonomy of Underwater Vehicles (AUVs))
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