Unmanned Surface Vehicle Research Articles

Development of a calculation power model of a vacuum automounting device

One of the ways to improve navigation safety during water transport operation is to develop automated devices and systems that reduce the number of tasks for workers. For example, the introduction of vessels with different levels of autonomy into production activities, capable of automatically monitoring the environment or delivering cargo, helps to reduce the size of the vessel due to the absence of a residential superstructure. On inland waterways, one of the limitations of the introduction of autonomous navigation is their safe passage through shipping passages — shipping locks. Mooring a vessel in a lock chamber is very labor-intensive and is usually performed by crew members of the vessel being locked for movable (floating hoops) or fixed (berthing bollards, bollards, hoops) mooring devices, the permissible loads of which are established in regulatory documents. Over the past 10 years, the authors have not found studies describing the results of measuring the loads on mooring devices and ropes arising from the movement of the vessel in the lock chamber. Another constraint to the implementation of autonomous shipping is the lack of requirements for auto-mooring devices that ensure the retention of vessels in the lock chamber. In this regard, the purpose of this study is to compare the standard and actual values of the load on mooring eyes and ropes for the theoretical calculation of the holding forces of the vacuum automooring device model, taking into account the operating conditions in the shipping lock chamber. The measurement results presented in the article were obtained in the course of the research work “Development of scientifically based recommendations for establishing hydraulic modes for filling and emptying the chambers of locks No. 1–8 of the Federal State Budgetary Institution “Volga-Balt Administration” based on in-kind studies of the conditions of ship mooring in lock chambers” commissioned by the Federal Agency for Maritime and River Transport using a strain gauge installed in the mooring rope cut. As a result of the theoretical calculation of the holding force of the vacuum auto-mooring device, the holding forces of the vacuum gripper from the radius of one vacuum suction cup were obtained.

ADP-based adaptive control of ship course tracking system with prescribed performance

In this paper, an adaptive dynamic programing (ADP)-based adaptive control problem is studied for the ship course tracking system with prescribed performance. Firstly, the complex sea conditions will bring difficulty to ship course tracking when unmanned ship sailing on sea. In order to overcome this difficulty, a prescribed performance feedforward term is designed in the controller to improve the course tracking ability of ship. Secondly, by defining a cost function, an ADP technology is exploited to achieve an optimal control of ship course tracking system. Finally, the stability of the closed loop system is turned out by Lyapunov stability theory and all signals are uniformly ultimately bounded (UUB). Simulation results validate the effectiveness of the proposed method.

Motion Control System for USV Target Point Convergence.

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV's positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA).

Unmanned Surface Vessel–Unmanned Aerial Vehicle Cooperative Path Following Based on a Predictive Line of Sight Guidance Law

This paper explores the cooperative control of unmanned surface vessels (USVs) and unmanned aerial vehicles (UAVs) in maritime rescue and coastal surveillance. The USV-UAV system faces challenges of disturbances and substantial inertia-induced overshooting during path following. A novel position prediction line of sight (LOS) guidance law is proposed to address these issues for USV path following control. Radial basis function-based neural networks (RBF-NNs) are used to estimate disturbances, and a high-order differentiator is used to design a velocity observer for unknown USV velocity. The UAV control system employs proportional–derivative (PD) control with feedforward compensation for quadrotor control design and utilizes a finite-time converging third-order differentiator to differentiate non-continuous functions. The simulation results demonstrate strong robustness in the proposed USV-UAV cooperative control algorithm. It achieves path following control in the presence of wind and wave disturbances and exhibits minimal overshoot.

Unmanned surface vehicles for naval warfare and maritime security

There is a growing need to implement maritime defensive systems, covering both medium-range and close-in defenses. This is in response to the escalating threat posed by unmanned surface vehicles (USVs) in a rapidly changing environment. As unmanned technologies become more widespread and advanced, the proactive deployment of these defensive measures is becoming imperative. This strategic approach aims to reduce risks, protect vital assets, and bolster the Navy’s flexibility in adapting to the evolving dynamics of contemporary naval warfare. This initiative aligns with broader goals of upholding maritime security and contributing to regional stability amid rapidly shifting conditions in warfare. This perspective covers the relevant aspects of USV development, and the countermeasures being considered.

Conceptual design of an autonomous single-container vessel

The growth of maritime shipping is leading to the creation of larger vessels. However, this expansion in size brings with it several challenges, including the development of maritime infrastructure, the potential for growth in third-world countries, and the emission of greenhouse gases. In response to these challenges, this research explores the feasibility of designing an autonomous ship capable of transporting a single standardized 40 ft. container overseas using mainly passive propulsion methods. Using advanced design tools, including CAD software and CFD simulations, as well as conducting a comprehensive analysis of relevant literature, the designs for a hull and sails were developed, and an overview of the potential active control systems required for autonomous operation was provided. The study also performed an initial analysis of strength, stability, and velocity to validate the design choices. The ship proves to adhere to the basic strength and stability requirements while reaching its maximum hull velocity at certain wind speeds. The results of the study indicate that it is possible to design and manufacture a mainly passively propelled ship capable of transporting a 40 ft. standardized container overseas and rethink the logistics at scale.

Editorial: Advances in autonomous ships (AS) for ocean observation

Editorial: Advances in autonomous ships (AS) for ocean observation

A Model-Free Adaptive Positioning Control Method for Underactuated Unmanned Surface Vessels in Unknown Ocean Currents

Aiming to address the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations at sea without dynamic positioning control systems, this paper introduces an original approach to positioning control: the virtual anchor control method. This method is applicable in environments with currents that change slowly and does not require prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method comprises four steps. First, a concise linear motion model with unknown disturbances is proposed. Then, a motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed to estimate uncertainties in the motion model. In the last step, based on the observer, a sliding-mode method is used to design a heading control law, and a thrust control law is also designed by applying the Lyapunov method. Numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has a good control effect and is robust.

Evaluating the social acceptance of autonomous ferries: An observation from passengers’ boarding willingness

Ferries are crucial for the transportation of passengers in coastal areas, offering multi-route, high-frequency, and point-to-point services at a lower cost than bridges or tunnels. Recently, many countries have been advocating for autonomous ferries due to their eco-friendliness and potential to mitigate accidents that often occur on traditional manned ferries due to human error. However, the public's acceptance of autonomous transport systems is frequently influenced by scientific evidence that supports their practical applications. This study endeavours to scrutinise the social acceptance of autonomous ferries from the passenger's perspective, employing the perceived value theory. A total of 151 responses have been collated from our questionnaire, aiming to investigate the public's acceptance of autonomous ferries, and to obtain the users' opinions on key influential dimensions such as the perceived price, quality, value, risk, and willingness to use an autonomous ferry. Structural equation modelling (SEM) has been employed to examine the relationship among these dimensions. The findings reveal that the perceived price and quality have a positive effect on the perceived value, which, in turn, has an indirect effect on the willingness to use autonomous ferries. The conjecture that the perceived risk influences the perceived value and willingness to use autonomous ferries is fully supported by our findings. Building on the findings of this research, this study offers specific recommendations to industry operators and government authorities to guide policy formulation and operational decision-making in the development of autonomous ferry.

Seal Pipeline: Enhancing Dynamic Object Detection and Tracking for Autonomous Unmanned Surface Vehicles in Maritime Environments

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability.

A Risk Identification Method for Ensuring AI-Integrated System Safety for Remotely Controlled Ships with Onboard Seafarers

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Robust adaptive event-triggered control for unmanned surface vessel–unmanned aerial vehicle: Application to sea–air synchronized lawn mowing search operation

This paper investigates a robust adaptive event-triggered control algorithm with a consideration of the synchronized lawn mowing search guidance for a heterogeneous system comprising both an unmanned surface vessel (USV) and an unmanned aerial vehicle (UAV). For ensuring a cooperative global search navigation of the USV–UAV, a synchronized lawn mowing search guidance is developed with two key elements: the L1-based virtual ship (L1VS) and L1-based virtual aerial (L1VA), where the former can generate the smooth reference path for a USV according to the search task scope, while the latter can program the reference path for a UAV on the basis of its search requirements (search speed, detected capacity and communication distance). In the control loop, a robust adaptive path following control law is designed by utilizing an event-triggered mechanism and a minimal learning parameter (MLP) technique. In particular, the event-triggered mechanism has a potential role for a reduction of the unnecessary transmission resource usage for the channel of the controller to the actuator. Besides, only one learning parameters on each channel by introducing the MLP technique, implying a lower computational burden. Through the Lyapunov theorem, the semi-global uniform ultimate bounded (SGUUB) stability properties of the proposed control system. Finally, two numerical simulations are carried out to evaluate the effectiveness of the proposed strategy and the advantages compared with existing techniques.

Energy Efficiency Maximization for Multi-UAV-IRS-Assisted Marine Vehicle Systems

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks.

Multiport event-triggered finite-time PID control for underactuated unmanned surface vessels under cyber attacks

Multiport event-triggered finite-time PID control for underactuated unmanned surface vessels under cyber attacks

Deep learning-powered visual place recognition for enhanced mobile multimedia communication in autonomous transport systems

The progress of autonomous transport systems (ATS) involves efficient multimedia communication for real-time data tradeoffs and environmental issues. Deep learning (DL) powered visual place recognition (VPR) was developed as an effective tool to improve mobile multimedia communication in ATS. VPR relates to the capability of a method or device to recognize and identify particular places or locations from the visual scene. This procedure involves inspecting visual data, like images or video frames, to control the unique features or features connected with diverse locations. By leveraging camera sensors, VPR allows vehicles to detect their surroundings, enabling context-aware communication and enhancing the entire system's performance. DL-empowered VPR offers a transformative manner to improve mobile multimedia communication in ATS. By identifying and understanding their situation, autonomous vehicles can communicate most effectively and operate reliably and safely, paving the way for a future characterized by seamless and intelligent transportation. This article develops a novel Deep Learning-Powered Visual Place Recognition for Enhanced Multimedia Communication in Autonomous Transport Systems (DLVPR-MCATS) methodology. The main aim of the DLVPR-MCATS methodology is to recognize visual places or not utilize optimal DL approaches. For this purpose, the DLVPR-MCATS approach utilizes a bilateral filtering (BF) based preprocessing model. For the feature fusion model, the DLVPR-MCATS approach follows three models: residual network (ResNet), EfficientNet, and MobileNetv2. Moreover, the hyperparameter tuning method uses the Harris Hawks Optimization (HHO) model. Finally, the bidirectional long short-term memory (BiLSTM) technique is implemented to recognize visual places. A wide range of simulations is executed to validate the solution of the DLVPR-MCATS method. The experimental validation of the DLVPR-MCATS method portrayed a superior performance over other models concerning various aspects.

Evaluation of Autonomous Navigation and Path Accuracy for Unmanned Surface Vehicles Using IMU Sensor and GPS Data

The development of autonomous systems for environmental monitoring is becoming increasingly important. This project focuses on creating a robotic marine vessel, Aqua Sense, for efficient water quality monitoring. Existing methods are often labour-intensive, time-consuming, and lack real-time data capability. The objective is to develop a robotic vessel with autonomous navigation for water quality applications. A comprehensive design approach, including a literature review and careful component selection, was employed. Key components include the Arduino Mega, Blue Robotics T200 thruster, GlobalSat BU-353N5 GPS sensor, BNO055 Inertial Measurement Unit (IMU), and a Li-po 11.1v battery. In this paper, several field trials data collection is presented to evaluate the navigation algorithm. These findings validate the feasibility of Aqua Sense for real-world applications. The varying distance errors at points B and C demonstrate an error range, with a maximum error of approximately 1.130m at point B (about 0.11% of the target location) and a significant variation at point C, reaching up to 1.660m (approximately 0.15%).

Lidar-Based Obstacle Detection and Path Prediction for Unmanned Surface Vehicles

Abstract Unmanned Surface Vehicles (USVs) represent an advanced technology with a wide range of applications in maritime contexts, and they offer a promising alternative to manned systems, particularly for the provision of services such as riverbed mapping. Furthermore, the use of USVs in combination with other robotic systems, such as flying drones, remotely operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs), opens up innovative ways to inspect and monitor maritime infrastructure. However, the use of these systems in dynamic environments, such as ports or shipping lanes, requires a high degree of adaptability and precision in navigation to manoeuvre safely between other traffic participants and obstacles to prevent collisions. To address this problem, USVs require sophisticated perception systems for the precise detection and classification of nearby objects. Leveraging Light Detection and Ranging (LiDAR) technology, which is renowned for its efficacy in obstacle avoidance in robotics, this paper outlines the adaptation of a LiDAR sensor for USVs. The proposed pipeline enables the detection of other moving vessels and the accurate determination of their positions and trajectories up to 150 m from the USV. This paper introduces an obstacle detection system and validates it within a port setting. A methodical approach to LiDAR data preprocessing, clustering, and point cloud extraction will be outlined. Furthermore, it explores the application of an Unscented Kalman Filter (UKF) for the projection of motion paths of identified entities. This advanced form of data analysis provides the ability to predict the future position and path of potential obstacles, optimize decision-making for autonomous navigation algorithms and significantly improve the safety of USV operations. The pipeline is tested experimentally in sea trials by enacting five different scenarios that USV may encounter in a port setting. The results of the pipeline’s tracking capabilities are compared to the ground truth data collected by the tracked vessel and plotted to visualize the error.

Research on Autonomous Collision Avoidance of USV Based on Improved APF Algorithm

Abstract Aiming at the problem of Unmanned Surface Vessel (USV) avoiding dynamic vessels on the sea surface, an Artificial Potential Field method based on Velocity Obstacle Method (VO-APF) was studied. Under the premise of complying with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the overlapping of ship domains is minimized. The Quaternion ship domain was introduced into VO to make it more suitable for collision avoidance judgment in the ship domain, and the collision avoidance time of the ship was predicted, and the reasonable obstacle avoidance point was finally obtained. The attraction function of the obstacle avoidance target point and the repulsion function of the ship domain based on the APF are set, and the USV is subjected to the repulsion force and attraction in the APF, and finally completes the whole dynamic ship collision avoidance task. In the simulation environment, VO, APF and VO-APF were compared in three different encounter situations. The results show that VO-APF can help USV to complete the collision avoidance task under the premise of complying with COLREGs, and the overlap time between USV’s own ship domain and the other ship domain is the shortest, and the safety degree is the highest.

Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ~ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 lags of observation. However, we found that diel patterns in fish detection strongly affected the abundance estimates due to diel vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs even further. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

The economic and environmental viability of green and autonomous ships in inland shipping ecosystems

Abstract This paper explores the potential of using green, autonomous ships in revitalising inland shipping in Europe against the backdrop of declining market share and the dominance of “economy-of-scale” in waterborne freight transportation. It assesses the economic and environmental viability of converting freight from road- to waterborne modalities in broader business ecosystems, specifically along the Rotterdam-Ghent corridor. The analysis leverages operational and commercial insights from logistics firms, ports and terminal operators, combined with data on European goods flows by road, and accounts for operational, financial and environmental variables including realistic scenario building and ecosystem implications. Findings indicate that inland shipping in general and green, autonomous shipping in particular offer both economically and environmentally viable alternatives to road transport. The study calls for further research into green, autonomous ships from an ecosystem perspective as a potential solution to current challenges in sustainable freight transportation.