Unmanned Surface Vehicle Research Articles

Progressive deep reinforcement learning for intelligent collision avoidance in unmanned surface vehicles

Progressive deep reinforcement learning for intelligent collision avoidance in unmanned surface vehicles

Predefined performance course tracking control of unmanned surface vehicle via the upper bounded and dynamic event-triggered technique

Predefined performance course tracking control of unmanned surface vehicle via the upper bounded and dynamic event-triggered technique

A real-time lightweight object detection algorithm based on improved you only look once version 8 for unmanned surface vehicle

A real-time lightweight object detection algorithm based on improved you only look once version 8 for unmanned surface vehicle

Robust sliding mode model predictive control and thrust allocation methods for autonomous berthing of water-jet propulsion unmanned surface vehicles

Robust sliding mode model predictive control and thrust allocation methods for autonomous berthing of water-jet propulsion unmanned surface vehicles

Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles

Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles

An autonomous surface vehicle for acoustic tracking, bathymetric and photogrammetric surveys

An autonomous surface vehicle for acoustic tracking, bathymetric and photogrammetric surveys

Numerical study of unmanned surface vessel's dynamic characteristics in rough sea conditions

Numerical study of unmanned surface vessel's dynamic characteristics in rough sea conditions

Unmanned surface vessel routing and unmanned aerial vehicle swarm scheduling for off-shore wind turbine blade inspection

Unmanned surface vessel routing and unmanned aerial vehicle swarm scheduling for off-shore wind turbine blade inspection

Robust control method for automatic berthing of unmanned ships based on weight optimal loop shaping

Robust control method for automatic berthing of unmanned ships based on weight optimal loop shaping

Cybersecurity Threats in Maritime Autonomous Surface Ships Navigating Canals and Narrow Channels: A Risk Assessment Using STPA-Safety/Security and Fuzzy-AHP

Abstract Maritime canals and narrow channels are critical for global trade, yet their confined nature poses significant risks, especially with the increasing reliance on digital technologies in ship navigation. This study investigates cybersecurity threats to Maritime Autonomous Surface Ships (MASS) operating in these environments, focusing on potential cyber-attacks that could lead to accidents such as grounding, collisions, and loss of propulsion control. Utilizing the System-Theoretic Process Analysis for Safety and Security (STPA-Safety/Security) combined with Fuzzy Analytic Hierarchy Process (F-AHP), the study identifies and prioritizes key threats, including GPS/AIS spoofing, communication jamming, and thruster override. Expert input via the Delphi method validates the threat scenarios, providing a comprehensive risk assessment. The findings highlight the urgent need for enhanced cybersecurity measures, such as redundant navigation systems, secure communication channels, and improved operator training. The study contributes to maritime cybersecurity literature by offering a structured methodology for assessing and mitigating cyber risks in autonomous ship operations, particularly in confined waterways.

Design and development of a new autonomous transportation robot for finished vehicles docking transportation in RO/RO logistics terminal

Design and development of a new autonomous transportation robot for finished vehicles docking transportation in RO/RO logistics terminal

Inland waterway autonomous transportation: System architecture, infrastructure and key technologies

Inland waterway autonomous transportation: System architecture, infrastructure and key technologies

AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) operating in coordination with an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads (CHs) deployed across an uneven seafloor. The proposed approach employs a VoI model that captures both the importance and timeliness of sensed data, guiding the AUV to collect and deliver critical information before its value significantly degrades. A forward Dynamic Programming (DP) algorithm is used to jointly optimize the AUV’s trajectory and the USV’s start and end positions, with the objective of maximizing the total residual VoI upon mission completion. The trajectory design incorporates the AUV’s kinematic constraints into travel time estimation, enabling accurate VoI evaluation throughout the mission. Simulation results show that the proposed strategy consistently outperforms conventional baselines in terms of residual VoI and overall system efficiency. These findings highlight the advantages of VoI-aware planning and AUV–USV collaboration for effective data collection in challenging underwater environments.

Energy-Aware Drone Path Finding with a Fixed-Trajectory Ground Vehicle

Rotary-wing Unmanned Aerial Vehicles (commonly referred to as drones) are versatile autonomous transportation platforms that can be used for a variety of data collection applications including emergency response, environmental monitoring, surveillance, and many others. In this work, we investigate how to plan efficient paths that minimize mission completion time for drone data collection where the drone must rendezvous with a moving ground vehicle (GV) that cannot stop and wait for the drone. Moreover, we address the limited onboard energy storage issue by adapting drone speed. We propose a mixed-integer nonlinear program (MINLP) solution to solve this problem to optimality and provide two heuristics-based alternative solutions (the k -TSP and D -TSP approaches) that are more computationally tractable. We evaluate these approaches in extensive simulations using real drone characteristics to highlight their tradeoffs. Our results show that the k -TSP algorithm performs well when data collection points are closer to the GV, averaging within 4.5% of the optimal solution, while the D -TSP approach is more versatile, finding solutions in situations where the k -TSP algorithm tends to fail. Furthermore, we show that adapting drone speed can improve solution quality by up to 47.1% compared to fixed-speed approaches. In summary, this article serves as an exploratory study in energy-aware planning and scheduling for drones and other autonomous transportation systems.

An Enhanced Navigation System With Predictive Motion Planning for Unmanned Surface Vehicles in GNSS‐Attenuated Dynamic Urban Waterways

ABSTRACTUnmanned surface vehicles (USVs) applied in urban waterways may suffer from inaccurate localization due to the Global Navigation Satellite System (GNSS) attenuation, and be susceptible to collision threats from vessels of human‐induced violations and piloting errors. This paper proposes an enhanced navigation framework capable of stable continuous localization, dynamic obstacle perception, and collision‐free motion planning. A tightly coupled LiDAR‐Visual‐Inertial Odometry via Smoothing and Mapping (LVI–SAM) is selected as the fundamental framework of localization and mapping subsystem. An incrementally mapping data structure is incorporated to improve the computation efficiency and accuracy of the LiDAR odometry optimization process. To mitigate the long‐term accumulating odometry drift, valid GNSS measurements are introduced to provide absolute reference in the factor graph optimization framework, which can achieve optimum state estimation by maximum a posteriori given all the noisy measurements from multiple sensors. Furthermore, a dynamic occupancy grid map framework, based on sequential Monte Carlo and probability hypothesis density method, is developed to enhance situational awareness of USVs for risk anticipation of dynamic obstacles and facilitate predictive avoidance. Extensive real‐world experiments have been carried out to demonstrate that the proposed autonomous navigation system is capable of robust and accurate localization over long‐term urban waterway navigation, and dynamic obstacle avoidance through a safer predictive strategy.

Improved Model Predictive Control Algorithm for the Path Tracking Control of Ship Autonomous Berthing

To address the issues of path tracking accuracy and control stability in autonomous ship berthing, an improved algorithm combining nonlinear model predictive control (NMPC) and convolutional neural networks (CNNs) is proposed in this paper. A CNN is employed to train on a large dataset of ship berthing trajectories, combined with the rolling optimization mechanism of NMPC. A high-precision path tracking control method is designed, which accounts for ship motion constraints and environmental disturbances. Simulation results show an 88.24% improvement in tracking precision over traditional MPC. This paper proposes an improved nonlinear model predictive control (NMPC) strategy for autonomous ship berthing. By integrating convolutional neural networks (CNNs) and moving horizon estimation (MHE), the method enhances robustness and path-tracking accuracy under environmental disturbances. The amount of system overshoot is reduced, and the anti-interference capability is notably improved. The effectiveness, generalization, and applicability of the proposed algorithm are verified.

Policy frameworks for green innovation in maritime trade and sustainable development

ABSTRACT The import/export industry is vital to global trade and economies, but maritime business contributes to environmental issues like carbon emissions, marine pollution, and resource consumption. With growing pressure to minimize these impacts, green technologies and sustainable practices in shipping have become crucial. This paper discusses green innovation in the maritime sector, emphasizing the need for strict regulatory standards. It examines low-carbon fuels, energy-efficient systems, intelligent transport, and autonomous ships for their role in reducing emissions and improving efficiency. Additionally, it explores measures such as cross-border coordination, policy clarity, and incentives to promote green technologies. Challenges include high acquisition costs, design inconsistencies, and technological constraints. However, increased research, infrastructure investment, and international rule harmonization can mitigate these barriers. Key stakeholders, including global and national organizations, play a role in shaping environmental standards for oil tankers. The future of sustainable maritime trade depends on governments, businesses, and technology providers advancing eco-friendly and efficient shipping. This study calls for exploring relevant theories and formulating effective policies to support maritime trade’s sustainable growth.

HEROES: Humanitarian Emergency Response based on UAV-enabled Integrated Sensing and Communication, Positioning, and Satisfaction Games

In the field of autonomous transportation systems, the integration of Unmanned Aerial Vehicles (UAVs) in emergency response scenarios is important for enhancing the operational efficiency and the victims’ positioning. This article presents a novel Positioning, Navigation, and Timing (PNT) framework, named HEROES , which leverages the UAV and integrated sensing and communication technologies to address the challenges in post-disaster environments. Our approach focuses on a comprehensive post-disaster scenario involving multiple victims, first responders, UAVs, and an emergency control center. HEROES enables UAVs to function as anchor nodes and facilitate the precise positioning of the victims while simultaneously collecting critical data from the disaster area. We further introduce a reinforcement learning model based on the Optimistic Q-learning with Upper Confidence Bound algorithm, enabling the victims and first responders to autonomously select the most advantageous UAV connections based on their channel gain, shadowing probability, and positional characteristics. Furthermore, HEROES is based on a satisfaction game-theoretic model to enhance the sensing, communication, and positioning functionalities. Our analysis reveals the existence of various satisfaction equilibria, including minimum efficient satisfaction equilibrium, ensuring that the UAVs meet their quality of service constraints at minimal operational costs. Extensive experimental results validate the scalability and performance of HEROES, demonstrating significant improvements over existing state-of-the-art methods in delivering PNT services during humanitarian emergencies.

Architectural enhancements, challenges and future trends in real-time IoT applications over 5G networks

The introduction of real-time Internet of Things (IoT) applications has introduced unprecedented demands on communication systems, which require ultra-low latency, high reliability, and massive device connectivity. Fifth-generation (5G) wireless networks represent a foundational shift in network architecture, offering advanced capabilities such as ultra-reliable low-latency communication (URLLC), mobile edge computing (MEC), and network slicing to support time-sensitive IoT services at scale. This review critically examines how these architectural enhancements enable real-time IoT deployment across domains, with inclusion of autonomous transportation, industrial automation, remote healthcare, and smart energy systems. While 5G provides a robust framework, its real-world adoption has faced technical constraints related to interoperability, spectrum management, energy efficiency, and cybersecurity. The paper synthesizes existing research on these challenges, and highlight persistent integration gaps and trade-offs that must be navigated to achieve deterministic performance in complex environments. In response, future research directions are proposed, including AI-driven orchestration, blockchain-based trust models, and emerging sixth-generation (6G) technologies. This work provides a comprehensive foundation for scalable, secure, and latency-guaranteed designs of real-time IoT systems in the 5G era and beyond.

Introduction to Special Issue on Applications-Driven UAV Routing and Scheduling Algorithms for Autonomous Transportation Systems - Part I

Introduction to Special Issue on Applications-Driven UAV Routing and Scheduling Algorithms for Autonomous Transportation Systems - Part I