Unmanned Surface Vehicle Research Articles

Fixed-time formation tracking for unmanned surface vehicles: A multi-layer neural networks approach

This paper investigates the distributed fixed-time formation problem of unmanned surface vehicles (USVs) in the presence of external disturbances, model uncertainties, input saturation and quantization constraints. To deal with the problem, a fixed-time sliding-mode control algorithm is proposed, where multi-layer neural networks (MNNs) are designed to approximate the unknown dynamics and composite disturbances of the system. The proposed MNNs combine the advantages of fuzzy neural networks (FNNs) and radial basis function neural networks (RBFNNs), exhibiting robust dynamic characteristics. Furthermore, the non-singular fast terminal sliding mode (NFTSM) is integrated into the fixed-time control framework to improve the robustness and speed of convergence for uncertain USV systems. Comparative simulations conducted with USVs demonstrate the superiority and effectiveness of the proposed algorithm.

A comparison of multi-source power supply systems for autonomous marine vehicles: The SWAMP case study

Extensive research on zero-emissions autonomous surface vehicles has been recently carried out, aiming at increasing autonomy. Due to their small size, unmanned vehicles present unique difficulties in terms of weight and dimensions when powering the vehicle with renewable energy sources. In this paper, photovoltaic source, fuel cell and Li-ion battery multi-source configurations are proposed, demonstrating by physical layout and simulation results the feasibility of the prototype, compliant with strict constraints of the vehicle under test. The energy system model of the vehicle under test is implemented in MATLAB/Simulink. A comparison of multi-source energy system configurations is proposed and validated by simulation results in terms of endurance, weight and size. If compared with the original battery-powered vehicle, as shown by comparing simulation results, the endurance is doubled, extended up to 12 h on the most favorable day of the year. The solution even complies with payload and physical dimensions constraints.

Dynamic event-triggered time-varying formation control for heterogeneous unmanned swarm systems with scaling attacks

This paper addresses the problem of time-varying formation control for heterogeneous unmanned swarm systems (USSs) consisting of multiple unmanned aerial vehicles (UASs) and unmanned surface vehicles (USVs) based on a directional switching topology. Firstly, a novel USSs model is established, involving the effect of multiple network attacks, network bandwidth constraints and input saturation. In particular, a scaling attack model is proposed to account for denial-of-service (DoS) and deception attacks. Secondly, a distributed controller based on a dynamic event triggering mechanism is designed to reduce the communication load by adjusting the communication frequency between individuals, and the Zeno phenomenon is effectively avoided. Finally, the Lyapunov function is used to obtain sufficient conditions for system stability and bounds on the frequency and duration of network attacks. The experiments and comparison results verify the proposed strategy's effectiveness and superiority.

CORAL-Catamaran for Underwater Exploration: Development of a Multipurpose Unmanned Surface Vessel for Environmental Studies.

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats.

USVs Path Planning for Maritime Search and Rescue Based on POS-DQN: Probability of Success-Deep Q-Network

Efficient maritime search and rescue (SAR) is crucial for responding to maritime emergencies. In traditional SAR, fixed search path planning is inefficient and cannot prioritize high-probability regions, which has significant limitations. To solve the above problems, this paper proposes unmanned surface vehicles (USVs) path planning for maritime SAR based on POS-DQN so that USVs can perform SAR tasks reasonably and efficiently. Firstly, the search region is allocated as a whole using an improved task allocation algorithm so that the task region of each USV has priority and no duplication. Secondly, this paper considers the probability of success (POS) of the search environment and proposes a POS-DQN algorithm based on deep reinforcement learning. This algorithm can adapt to the complex and changing environment of SAR. It designs a probability weight reward function and trains USV agents to obtain the optimal search path. Finally, based on the simulation results, by considering the complete coverage of obstacle avoidance and collision avoidance, the search path using this algorithm can prioritize high-probability regions and improve the efficiency of SAR.

Memory-based event-triggered path-following control for a USV in the presence of DoS attack

This paper proposes a novel memory-based event-triggered path-following controller for unmanned surface vehicles (USVs) in the presence of denial of service (DoS) attacks and limited communication resources. In the guidance part, a double distance logical virtual ship (DDLVS) guidance law is developed to eliminate the accumulation error of the yaw angle velocity in the traditional LVS algorithm, and the USV will keep a smoother tracking performance for the curve reference path. In the control part, the memory-based event-triggered fusion control algorithm proposed in this paper incorporates the signal non-transmission phenomenon caused by DoS attacks into the event-triggered interval through a set of adaptive parameters. Consequently, the adverse affect caused by DoS attacks is solved in the process of designing the event-triggered control law. By combining the neural networks (NNs) and the dynamic surface control (DSC) techniques, the updating weights are significantly compressed. The proposed algorithm is with advantages of concise controller structure, low energy cost, and easy engineering implementation. All the error signals of the closed-loop system are proved to be semi-global uniform ultimate bound (SGUUB) by the Lyapunov analyses. Finally, through two numerical examples, including the comparative simulation and path following maneuver in Shuang island (an island distribute in Liaoning Dalian), are provided to verify the effectiveness of the proposed strategy.

Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities

The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry’s environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry.

UYF-Net: A fusion network for feasible domain recognition and obstacle detection based on infrared thermography

Achieving safe navigation of unmanned surface vehicle (USV) in low light conditions remains a focus of research. The key to achieving the intelligent navigation of USV is the precise segmentation of navigable areas and fast obstacle detection. There are several challenges in processing infrared thermal imaging shoreline images using only semantic segmentation networks, including difficulties in extracting shorelines due to blurred details at the edges of infrared images, segmenting small targets, and complex obstacles due to small temperature differences in complex backgrounds. To address these issues, this paper introduces a novel UYF-Net network. The proposed method combines the improved U-Net with the YOLOv5m using a decision-level fusion strategy to achieve a better speed-accuracy tradeoff. Experimental results demonstrate that our method outperforms all comparative approaches discussed in this paper, further confirming the enhanced precision in segmentation, increased detection speed, and more stable model training process of the proposed network.

Multiple unmanned ship coverage and exploration in complex sea areas

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Rapid in-motion heading alignment using time-differenced carrier phases from a single GNSS antenna and a low-grade IMU

Abstract Despite advancements in global navigation satellite system/inertial navigation system (GNSS/INS) integrated systems, the challenge of achieving rapid and accurate initial heading alignment remains, particularly in low-grade IMUs and marine applications with swaying conditions. This study introduces an innovative in-motion heading alignment method utilizing time-differenced carrier phases (TDCP) from a single GNSS antenna and a low-grade IMU based on the fundamental principle of trajectory similarity. The heading angle is determined by comparing the dead reckoning (DR)-derived trajectory with the TDCP-derived trajectory. This is achieved using gyro measurements and the delta position from TDCP solutions. The proposed alignment method can be implemented in real-time and the alignment period is just 1s, which indicates that the method could continuously output heading when the carrier is in motion. We not only validate the effectiveness of the proposed alignment method in land vehicle applications, but also in maritime vessels, like unmanned surface vessels (USV), and unmanned underwater vehicles (UUV). In addition, we evaluated the impact of challenging ocean conditions and mounting angle errors on the proposed alignment method. Experimental results show that the heading could be determined with an accuracy of 0.49º, 3.10º, and 2.31º at a 95% confidence level for the land vehicle, USV, and UUV tests, respectively. Besides, the RMS of the alignment errors calculated by our method reaches 0.24º, 1.42º, and 1.19º for the land vehicle, USV, and UUV tests, respectively.

Disturbance observer based adaptive heading control for unmanned surface vehicle with event‐triggered and signal quantization

SummaryThis article delves into the adaptive heading tracking control of unmanned surface vehicle (USV) by incorporating an event‐triggered mechanism and signal quantization. The primary objective is to save communication resources while alleviating the burden of signal transmission. To address time‐varying external perturbations inherent in the control system, a disturbance observer is employed for precise estimation. Additionally, a linear model is introduced to delineate the procedure of quantization. By furnishing the controller with purpose‐designed quantized control input, the adaptive tracking control system can effectively track desired input without requiring any prior knowledge of the quantized parameters. The article substantiates its claims by demonstrating the system's stability in the absence of quantization considerations and the bounded nature of quantization errors through a series of presented lemmas. Further, the stability of the USV heading control system, integrated with an event‐triggered mechanism and signal quantization, is proofed in accordance with Lyapunov stability theory. Finally, the proposed strategy's efficacy and practical applicability are validated through experimental simulations.

Multitask-Based Evolutionary Optimization for Vehicle Routing Problems in Autonomous Transportation

Multitask-Based Evolutionary Optimization for Vehicle Routing Problems in Autonomous Transportation

Composite Learning Adaptive Intelligent Self-Triggered Fault-Tolerant Control With Improved Performance Assurance for Autonomous Surface Vehicle

Composite Learning Adaptive Intelligent Self-Triggered Fault-Tolerant Control With Improved Performance Assurance for Autonomous Surface Vehicle

Adaptive Fault-Tolerant Fuzzy Containment Control for Networked Autonomous Surface Vehicles: A Noncooperative Game Approach

Adaptive Fault-Tolerant Fuzzy Containment Control for Networked Autonomous Surface Vehicles: A Noncooperative Game Approach

Predefined-Time Bipartite Time-Varying Formation Tracking Control of Networked Autonomous Surface Vehicles via Hierarchical Control Approach

Predefined-Time Bipartite Time-Varying Formation Tracking Control of Networked Autonomous Surface Vehicles via Hierarchical Control Approach

Fusion-Based Event-Triggered H∞ State Estimation of Networked Autonomous Surface Vehicles With Measurement Outliers and Cyber-Attacks

Fusion-Based Event-Triggered <i>H_∞ </i> State Estimation of Networked Autonomous Surface Vehicles With Measurement Outliers and Cyber-Attacks

Task-Based Hazard Identification for a Hypothetical Autonomous Ship

Task-Based Hazard Identification for a Hypothetical Autonomous Ship

V2X and Deep Reinforcement Learning-Aided Mobility-Aware Lane Changing for Emergency Vehicle Preemption in Connected Autonomous Transport Systems

V2X and Deep Reinforcement Learning-Aided Mobility-Aware Lane Changing for Emergency Vehicle Preemption in Connected Autonomous Transport Systems

Polymer colloidal motors with photodynamic-regulated propulsion

Artificial colloidal motors capable of converting various external energy into mechanical motion, have emerged as attractive photosensitizer (PS) nanocarriers with good deliverability for photodynamic therapy. However, photoactivated 3O2-to-1O2 transformation as the most crucial energy transfer of the photodynamic process itself is still challenging to convert into autonomous transport. Herein, we report on PS-loaded thiophane-containing semiconducting conjugated polymer (SCP)-based polymer colloidal motors with asymmetric geometry for photodynamic-regulated propulsion in the liquid. The asymmetrical presence of the SCP phases within the colloidal motors would lead to significant differences in the 3O2-to-1O2 transformation and 1O2 release manners between asymmetrical polymer phases, spontaneously creating asymmetrical osmotic pressure gradients across the nanoparticles for powering the self-propelled motion under photodynamic regulation. This photoactivated energy-converting behavior can be also combined with the photothermal conversion of the SCP phases to create two energy gradients exerting diffusiophoretic/thermophoretic force on the colloidal motors for achieving multimode synergistic propulsion. This unique motile feature endows the light-driven PS nanocarriers with good permeability against various physiological barriers in the tumor microenvironment for enhancing antitumor efficacy, showing great potential in phototherapy.

Transforming the Shipping Industry with Autonomous Ships and Artificial Intelligence

Transforming the Shipping Industry with Autonomous Ships and Artificial Intelligence