Unmanned Surface Vehicle Research Articles

An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in Autonomous Vehicles

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.

Design of A Self-Correctable Docking Mechanism in Disturbed Water Surface Environment

The docking mechanism is critical for unmanned surface vehicle (USV) applications, such as traction, dragging, charging, data transmission, building pontoons, etc. These applications are significant for observation, exploration, data acquisition, patrol, rescuing, etc., in aquatic environments. In this paper, a novel autonomous docking mechanism for USVs is proposed. It is capable of (1) docking the USV subject to disturbance efficiently, (2) latching without power consumption after the docking is completed, and (3) guiding the docking head to three specific angles at the end of the docking process. This paper proved the feasibility and rationality of the docking mechanism through theoretical force analysis. Experiments with three different scenarios are implemented to test the mechanism's performance. The experimental results show that the docking success rate is greater than or equal to 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on the disturbed water surface.

Development of an Autonomous Ship Navigation System Based on the A*-AHP Hybrid Path Planning Method

Development of an Autonomous Ship Navigation System Based on the A*-AHP Hybrid Path Planning Method

Cooperative Containment Strategies to Pursue One Faster Evader with Collision Avoidance

On the surface of complex sea areas with obstacles, the encirclement mission against one hostile unmanned surface vehicle (USV) with high capability is usually extremely challenging. Especially when the evasion USV is endowed with a higher speed, the pursuit USVs are more likely to fail. In this work, the containment strategies by multiple low-speed pursuit USVs to capture one high-speed evasion USV considering collision avoidance are studied. On the basis of the conventional artificial potential field (APF) method, we first design a virtual target point to replace the actual evasion USV, which will help the pursuit USVs better implement the surrounding behavior. Thereafter, we reset the triggering conditions of the APF method to avoid the unnecessary obstacle avoidance actions. The proposed strategies can help the pursuit USVs better accomplish the containment strategies while bypassing the obstacles. Simulation experiments have verified the effectiveness of the designed cooperative containment strategies.

Deep Reinforcement Learning-enhanced Target Encirclement of Multiple Unmanned Surface Vessels

Deep Reinforcement Learning-enhanced Target Encirclement of Multiple Unmanned Surface Vessels

HALOBATES: An autonomous surface vehicle for high-resolution mapping of the sea-surface microlayer and near-surface layer on essential climate variables

Abstract Essential climate variables (ECVs) are physical, chemical, or biological parameters supporting the characterization of the climate system. The oceanic ECVs temperature and salinity have been defined for the sea surface (upper 1 mm) and bulk water (upper 2 meters). Direct observation and sampling of the sea surface from ships and buoys is limited due to the potential loss of integrity of the sea surface and contamination by the research platform. The design, development, and operation of a research vehicle with autonomous capabilities based on commercially available components are described in this study. The autonomous operation and fully automated sampling enable high-resolution mapping of the sea surface microlayer (SML), i.e., the upper &lt; 1mm layer, and near-surface layer (&lt; 1.2 m). Sampling the SML is based on a rotating glass disk sampler. The autonomous surface vehicle HALOBATES has been equipped with multiple conductivity, temperature, and depth sensors (CTDs) in a flow-through system to measure both sea surface and bulk temperature and salinity. The performance of the autopilot under various scenarios is demonstrated. Typical anomalies of ECVs within the SML and near-surface layer are evident and forced by both atmospheric and oceanic processes. Data analyses reveal HALOBATES' ability to resolve thermohaline structure on different spatial and temporal scales, for example, small-scale freshwater lenses and thermohaline changes across oceanic fronts. The integration of an Acoustic Doppler Current Profiler supports the interpretation of thermohaline structures. HALOBATES is a platform with cutting-edge technology for understanding climate-related processes between the ocean and atmosphere and providing high-resolution sea surface data for validating satellite products of ECVs.

Flow Field Analysis and Development of a Prediction Model Based on Deep Learning

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.

Robust Collision Avoidance and Path‐Following of USVs With Reduced Conservativeness: A Control Barrier Function‐Based Approach

ABSTRACTThe collision avoidance and path‐following problem is fundamental for unmanned surface vehicles (USVs) to accomplish various tasks in different water environments. However, addressing this issue is challenging, as USVs are inevitably affected by environmental disturbances in practice. In this study, we address the robust collision avoidance path‐following problem for USVs with unknown bounded environmental disturbances by using the control barrier function (CBF)‐based approach. To reduce the conservativeness for the collision avoidance actions, the elliptical shape of the USV is considered when designing the control law. Furthermore, a high‐order control barrier function (HOCBF)‐based approach is proposed to achieve the robust collision avoidance with respect to both static and dynamic obstacles. Specifically, a nominal robust path‐following controller is first designed utilizing the predefined‐time observers without considering the collision avoidance requirement. Then, by considering the elliptical USV and the circular obstacles, collision avoidance input constraints are derived by the proper HOCBFs. Finally, a local quadratic programming (QP)‐based controller is proposed to achieve robust collision avoidance and path‐following of the USV. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed control strategy.

Parametric analysis of a fully coupled USV-type wave energy converter: An approach based on wave-to-grid modelling

The integration of the wave energy converter and the unmanned surface vehicle (USV), which has the power generation capability and the mobile capability, is attracting increasing attention in concept design, control, and parametric study, as the integrated system can play a crucial role in the maritime sectors of commerce, science, and military. Enhancing the power generation of a USV-integrated wave energy converter contributes to prolonged USV operations or grid supply, but the power generation performance of this new-type device is significantly influenced by its various coupled subsystems, which requires a systematic methodology for distinguishing key design parameters. To address that problem, a comprehensive wave-to-grid model that reflects all subsystem coupling, energy losses, and constraints is developed to comprehensively describe system dynamics, and then a Taguchi method is employed to investigate the influence of selected design parameters on electric power generation. Results reveal that the piston area of the hydraulic cylinder and the accumulator pressure are the two most influential parameters affecting the time-averaged electric power of the proposed USV-type wave energy converter. The proposed methodology provides guidelines for technicians on how to design wave energy converters optimally.

Motion State Estimation with Bandwidth Constraints and Mixed Cyber-Attacks for Unmanned Surface Vehicles: A Resilient Set-Membership Filtering Framework

Motion State Estimation with Bandwidth Constraints and Mixed Cyber-Attacks for Unmanned Surface Vehicles: A Resilient Set-Membership Filtering Framework

Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review

The development of autonomous vessels and unmanned surface vehicles (USVs) has generated great interest in the scientific community due to their potential and advantages for use in various environments and applications. Several literature review papers have been produced from different perspectives, contributing to a better understanding of the topic and to the analysis of advances, challenges, and trends. We hypothesize that the greatest attention has been focused on the development of high-impact applications in the maritime sector. Additionally, we depart from the need to investigate the potential and advances of USVs in fluvial environments, which involve particular operating conditions, where there are different socio-environmental conditions and restrictions in terms of access to conventional energy sources and communication systems. In this sense, the main objective of this work is to study USVs in the particular context of small craft. The search for records was conducted in Scopus and Web of Science databases, covering studies published from 2000 to 16 May 2024. The methodology employed was based on the PRISMA 2020 guidelines, which is a widely recognized protocol that ensures quality and rigor in systematic reviews and bibliometric analyses. To optimize the data collection and selection process, the semaphore technique was additionally implemented, allowing for an efficient categorization of the studies found. This combined methodological approach facilitated a systematic and transparent evaluation of the literature. This study was developed based on three research questions about the evolution of research topics, areas of application, and types of algorithms related to USVs. The study of the evolution of works on USVs was carried out based on the results of the meta-analysis generated with the Bibliometrix tool. The study of applications and developments was carried out based on information obtained from the papers for six study categories: application environment, level of autonomy, application area, algorithm typology, methods, and electronic devices used. For each of the 387 papers identified in the databases, labeling was performed for the 359 screened papers with six study categories according to the availability of information in the title and abstract. In the categories application sector, autonomy level, application area and algorithm type/task, it was identified that most studies are oriented toward the maritime sector, the developments to achieve full autonomy for USVs, the development of designs or algorithms at the modeling and simulation level, and the development and implementation of algorithms for the GNC subsystems. Nevertheless, this research has revealed a much wider range of environments and applications beyond maritime, military, and commercial sectors. In addition, from the mapping of the types of algorithms used in the GNC architecture, the study provides information that can be used to guide the design of the subsystems that enable USV autonomy for civilian use in restricted environments.

Tree structures may be the leadership structures employed by group motions exhibiting hierarchy

Collective behaviors leading to various fascinating movement patterns are believed to be the product of complex interplay among individuals. Previous studies have identified two types of leadership structures in pigeon flocks, i.e., hierarchical networks and reciprocal relationships. However, both of these leadership structures are predicated based on data analysis and lack substantial empirical evidence. Additionally, it is difficult to delineate a direct correspondence between leadership structures and trajectory data for pigeon flocks because birds cannot report their leadership structure. Herein, based on experiments involving volunteers, we found that tree structures may serve as the leadership structures employed by group motions exhibiting hierarchy. In the tree structure, each follower follows its only leader during collective motions, and the single top leader determines the direction of collective motions. By employing the tree structure, we introduced a model for describing collective motions. A method for obtaining the leadership structure through experimental trajectory data was proposed. This strategy was used to analyze flight trajectory data from a pigeon flock and elucidate the pigeons' leadership relationships. Our model can simulate the collective behavior of pigeon flocks, thus accurately replicating the findings of previous experimental studies. The results of this study provide insights regarding the leadership structure in pigeon flocks and have implications for artificial collective systems, e.g., autonomous formation control of multiple unmanned aerial vehicles or unmanned surface vehicles. Published by the American Physical Society 2024

New trends in the home logistics management model in urban contexts

With the majority of goods delivered ending up in city centers, last mile logistics is known to be the least efficient stage of the supply chain, comprising up to 28% of the total delivery cost. The main objective was to determine how the use of autonomous transportation technologies changes the home logistics management model. Positivist paradigm research, with quantitative method and descriptive-correlational approach, field research and non-experimental-transversal design. The sample was 28 courier companies, with at least one year of experience and a plant that includes at least five (5) full-time workers. The survey technique is used, using a collection instrument with a Likert-type scale. The results suggest that in general terms, the dimension that characterizes the state of implementability of new trends in the package logistics management model is present in this study. In the current context, companies are not yet at the maximum point of possibilities in terms of the implementation of new trends, if there is a will of the union to adapt every component that supports or optimizes the operation, for which there is a margin of opportunity . for the adaptation of these in the local context.

A modified A* algorithm combining remote sensing technique to collect representative samples from unmanned surface vehicles

Ensuring representativeness of collected samples is the most critical requirement of water sampling. Unmanned surface vehicles (USVs) have been widely adopted in water sampling, but current USV sampling path planning tend to overemphasize path optimization, neglecting the representative samples collection. This study proposed a modified A* algorithm that combined remote sensing technique while considering both path length and the representativeness of collected samples. Water quality parameters were initially retrieved using satellite remote sensing imagery and a deep belief network model, with the parameter value incorporated as coefficient Q in the heuristic function of A* algorithm. The adjustment coefficient k was then introduced into the coefficient Q to optimize the trade-off between sampling representativeness and path length. To evaluate the effectiveness of this algorithm, Chlorophyll-a concentration (Chl-a) was employed as the test parameter, with Chaohu Lake as the study area. Results showed that the algorithm was effective in collecting more representative samples in real-world conditions. As the coefficient k increased, the representativeness of collected samples enhanced, indicated by the Chl-a closely approximating the overall mean Chl-a and exhibiting a gradient distribution. This enhancement was also associated with increased path length. This study is significant in USV water sampling and water environment protection.

Neural Approach to Coordinate Transformation for LiDAR–Camera Data Fusion in Coastal Observation

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.

Fixed-time second-order sliding mode output feedback trajectory tracking control for an autonomous surface vehicle with model uncertainties and prescribed performance

This paper investigates the fixed-time trajectory tracking control problem of autonomous surface vehicle system with asymmetric performance constraints. The external disturbances, model uncertainties and the difficulty of velocity measurement are all taken into account in the controller design. Asymmetric performance constraints are firstly studied to improve the transient and steady-state performance of the tracking error system. Next, a fixed-time extended state observer is developed to estimate the unknown velocity and external disturbances. Both the fixed-time first-order and the second-order PID-type sliding surfaces are designed. Further, two continuous output feedback controllers are built to achieve high control accuracy and low energy consumption and to achieve fixed-time stability of the closed-loop system. Finally, abundant simulations for CyberShip II demonstrate the effectiveness of the proposed algorithms.

Learning-based adaptive neural control for safer navigation of unmanned surface vehicle with variable mass

This paper presents a novel approach to the precise control of variable mass unmanned surface vehicles (USVs) during payload deployment, where both mass and draught undergo unpredictable changes. We propose a draught observation method and an adaptive control strategy that leverages the strong coupling between the USV's motion states, mass, and draught. Our method employs a radial basis function neural network (RBF-NN) for real-time draught observation, using an offline training strategy based on gradient descent, combined with an adaptive online training strategy to improve observation accuracy. An adaptive control strategy based on the Backstepping method is then developed, incorporating real-time draught data from the RBF-NN to address unknown variations in mass and draught. The stability of both the RBF-NN observer and the adaptive control algorithm is rigorously verified using the Lyapunov method. Simulation results demonstrate that the proposed draught observation method achieves up to 30% faster convergence compared to traditional methods, with a significant improvement in observation accuracy. Furthermore, the adaptive control strategy effectively manages real-time adjustments in dynamic scenarios, maintaining robust control performance even under significant mass changes, where conventional approaches fail.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Integrating Knowledge Graphs into Autonomous Vehicle Technologies: A Survey of Current State and Future Directions

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature from 2017 to 2023, analyzing over 90 papers to explore the integration of knowledge graphs (KGs) into AV technologies. Our findings indicate that KGs significantly enhance AV systems by providing structured semantic understanding, improving real-time decision-making, and ensuring compliance with regulatory standards. The paper identifies that while KGs contribute to better environmental perception and contextual reasoning, challenges remain in their seamless integration with existing systems and in maintaining processing speed. We also address the ethical dimensions of AV decision-making, advocating for frameworks that prioritize safety and transparency. This review underscores the potential of KGs to address critical challenges in AV technologies, offering a hopeful and optimistic outlook for the development of robust, reliable, and socially responsible autonomous transportation solutions.

Unmanned Ship Collision Avoidance Action Plan Deduction Method under Man–Machine Interactive Negotiation in Collision Avoidance Scenarios

With the development of artificial intelligence technology, the future water traffic environment will present a new pattern of coexistence of manned ships and unmanned ships, because unmanned ships are different from manned ships in situation understanding, collision avoidance decision-making, and so on. Therefore, the obstacle avoidance planning between unmanned ships and manned ships becomes extremely complex, and collision avoidance behavior scheme deduction becomes a key step in solving the problems related to situation understanding and collision avoidance decision-making in collision avoidance scenarios. In this paper, the situation understanding of the pilot for different collision avoidance situations is integrated into the dynamic obstacle avoidance model, and an intelligent navigation collision avoidance system is proposed to assist in deducing the collision avoidance action plan of the unmanned ship in the man–machine coexistence scenario. The intelligent navigation collision avoidance system is divided into two parts, namely a ship situation understanding part and a ship obstacle avoidance part, wherein ship situation understanding is used for realizing the transition of the collision state of the unmanned ship in the deduction process by constructing a collision-state set and a behavior decision set by using a finite state machine (FSM). Regarding ship obstacle avoidance, ship velocity obstacle is calculated based on the reciprocal velocity obstacle method (RVO), and the collision avoidance action is selected by using the behavior decision generated by the FSM to realize the dynamic collision avoidance deduction of the unmanned ship. In this study, the validity and effectiveness of the intelligent navigation collision avoidance system proposed in this paper are verified by case studies in a variety of collision avoidance scenarios. The system successfully solves the problem of intelligent collision avoidance planning, provides reliable support for the intelligent collision avoidance of unmanned ships, provides a feasible solution for safety and efficiency in sea navigation, and provides a valuable reference for the design and development of future intelligent navigation collision avoidance systems for ships.