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Review

Formation Control of a Multi-Unmanned Surface Vessel System: A Bibliometric Analysis

by
Jie Xue
1,*,
Yuanming Song
1 and
Hao Hu
1,2
1
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(9), 1484; https://doi.org/10.3390/jmse12091484
Submission received: 22 July 2024 / Revised: 21 August 2024 / Accepted: 23 August 2024 / Published: 27 August 2024
(This article belongs to the Special Issue Unmanned Marine Vehicles: Perception, Planning, Control and Swarm)
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Versions Notes

Abstract

:
This study provides an overview of the literature on multi-unmanned surface vessel (multi-USV) systems, addressing the increasing attention on formation control of USVs due to their enhanced task execution ability, efficiency, and robustness in complex marine environments. Despite numerous studies on USVs covering fields, such as autonomous decision making, motion control, perception, and communication technologies, there is a significant lack of systematic literature review and bibliometric analysis specifically focused on a multi-USV system. This study aims to summarize advancements in multi-USV research, highlighting key aspects, including publication trends, influential scholars and papers, research hotspots, challenges, and future opportunities. By reviewing the current state of multi-USV research, this study contributes to the field as a beneficial reference for researchers, practitioners, and policymakers. It will not only highlight the progress made so far but also shed light on the gap that needs to be addressed to advance the field.

1. Introduction

With the significant potential of unmanned surface vessels (USVs) in both civil and military applications, this field has brought together great ideas and continuous efforts from scientists and engineers of various aspects. USVs are driverless marine vehicles, characterized by high flexibility, less consumption, low cost, shallow draft, and mild noise [1,2,3]. They are also called autonomous/driverless surface vehicles/ships. Driving on the surface of the water is the biggest difference between USVs and unmanned aerial vehicles (UAVs), autonomous vehicles (AVs, often referred to as driverless cars), and autonomous underwater vehicles (AUVs).
USVs can complete hazardous and time-costing operations in uncertain and dynamic maritime conditions without or with limited human intervention [4]. In recent years, USVs have gradually exerted a stronger influence in water-related activities such as water resource monitoring, search and rescue, marine charting, marine environment protection, surveillance, patrol, and target tracking [5,6,7,8,9,10].
Studies on USVs encompass various technical areas like autonomous decision making [11], motion control [12], sensors [13], and communication technologies [14]. A recent review by Yang et al. [15] thoroughly summarized the cutting-edge technologies and the latest developments in USVs. Many other reviews have also pointed out the safety and commercial prospects of USVs [16,17,18]. Despite the influx of new studies, the technique cornerstones underlying USVs are still navigation, guidance, and control technologies [5]. Joint efforts are being made to increase the autonomy of USVs in dynamic, uncertain, and complex environments [19]. At present, USVs usually need remote operation and command from a land-based station. In demanding scenarios, the capability of a single USV is limited [20].
Therefore, increasing attention is paid to multi-USV systems [21,22,23,24]. In a multi-USV system (also called a USV swarm or cluster), plural USVs achieve a goal through teamwork. If USV members in a multi-USV system possess different functional roles from each other, a heterogeneous fleet is formed [25,26,27]. Regarding the complex marine environment, compared to a solitary USV, a multi-USV system can improve task completion, efficiency, and robustness [28,29,30]. It also shows tremendous potential because of the flexible energy supply and wide area coverage [20].
Despite increasing studies on multi-USV systems, there is a significant lack of a systematic literature review and bibliometric analysis regarding multi-USV systems. To better support the development of multi-USV systems, it is very urgent to identify current challenges, analyze crucial techniques, and predict future directions via a thorough review of past research.
Thus, this study seeks to deliver a thorough examination of the literature relevant to multi-USV systems. This study endeavors to summarize the advancement of multi-USV research with a focus on key aspects, including publication trends, scholars, organizations, countries, papers, journals, research hotspots, challenges, and opportunities. By offering a detailed and structured overview of the current state of multi-USV research, this study hopes to serve as a valuable resource for researchers, practitioners, and policymakers. It will not only highlight the progress made so far but also shed light on the gaps that need to be addressed to advance the field further.
Distinguished from earlier works, this study makes three main contributions. (1) This study is the first to employ a combination of bibliometric analysis and a systematic literature review to form a comprehensive understanding of the multi-USV field. (2) In addition to a comprehensive understanding, this study also concentrates on specific challenges and techniques regarding a multi-USV system. (3) Future directions are highlighted, which may help scholars better expand this field.
The remaining paper is organized as follows. Section 2 describes the methods used in this work. Section 3 presents the main results of the bibliometric analysis and systematic literature review. Section 4 discusses challenges and opportunities in the field. Section 5 concludes the whole paper.

2. Methodology

2.1. Research Framework and Data Source

The research framework is shown in Figure 1. The Web of Science (WoS) Core Collection database was chosen as the data source. This well-known platform includes much high-quality academic article information and provides convenient search and data export services. The platform is widely used, and all data are available to subscribers, which is very conducive to the reproducibility of research. To offer a comprehensive understanding of the development of multi-USV research, a systematic retrieval of documents was conducted. The steps of query are shown in Appendix A Table A1.
First, we identified the initial query term as “unmanned surface vessel” since a multi-USV can be considered a subtopic of USVs. After 7 iterations, we finalized the query terms related to multi-USVs, resulting in 1072 hits. A quick review of the topics, titles, keywords, and abstracts helped us filter out irrelevant data, retaining 708 records. Subsequently, we cleaned the 708 records to ensure data uniqueness and completeness, which also reduced the workload for manual scrutiny. Then, detailed manual scrutiny was conducted, examining the full texts to ensure relevance to multi-USVs. Terms with similar meanings were standardized, forming a thesaurus. Finally, 218 valid records and a thesaurus of related terms were identified.

2.2. Bibliometric Method and Visualization Tool

The bibliometric mapping method was applied in this study. Bibliometric mapping employs quantitative methods to graphically represent the relationships between academic literature based on bibliographic information.
VOSviewer 1.6.20 was used to form bibliographic data and visualize the data. It can convert exported bibliometric data into bibliographic data and leverage clustering techniques to visually uncover structural configurations within the data file [31]. Li et al. [32] provided an informative overview of the concepts and techniques underlying bibliometric mapping. In the field of marine science and engineering, this software is widely used for bibliometric analysis [15,33,34,35,36].

3. Results

3.1. Publication Trend

The yearly number of articles with significant pertinence to multi-USVs is depicted in Figure 2. Our findings indicate that research on multi-USVs has risen alongside the surge in USV research. As Yang et al. [15] observed, USV-related studies began to grow rapidly in 2013. Similarly, research on multi-USVs emerged around 2013 and started to accelerate around 2019. The analysis of citation times reveals that the average impact of individual articles is increasing, as the growth rate of citations outpaces that of the number of publications. These trends indicate a growing interest in the field of multi-USVs, as well as an increasing impact of relevant research.

3.2. Social Structure

3.2.1. Authors

Table 1 shows the most influential authors in the field of multi-USVs over the past 20 years. Some authors who are influential in the general USV field may not appear [15], as our focus is specifically on multi-USV domains.
With relation to multi-USVs, we comprehensively consider the number of publications (no less than three), total citations, and citations per publication of an author to identify well-known authors in the field. The top three authors are not only highly productive, with an average of more than ten articles each, but also highly cited, with an average of more than forty-five citations per article. They are all affiliated with Dalian Maritime University, suggesting the university’s leading position in the field of multi-USVs.
Figure 3 shows the author’s collaboration network. The collaboration network indicates that many teams are conducting research in the field of multi-USVs. Chronologically, the team from Dalian Maritime University started early and has accumulated significant influence. It is worth noting that there are many emerging teams in the field whose number of publications is still relatively low, but they have been consistently active in recent years. This reflects the continuous influx of new scholars bringing vitality to the field. The setting of figure parameters is provided in Appendix A Table A2.

3.2.2. Organizations

Table 2 shows the most influential organizations over the past 20 years in the field of multi-USVs. With relation to multi-USVs, we comprehensively consider the number of publications (no less than three), total citations, and citations per publication of an organization to identify highly productive organization’s in the field.
Consistent with the results revealed by the author collaboration network analysis, Dalian Maritime University has the most achievements. Several schools in Shanghai also perform well, possibly reflecting regional advantages. All the listed organizations are in countries with natural marine resources. Among them, the most productive ones are almost all from China.
Figure 4 shows that there are broad cooperative relationships among organizations, but they tend to form specific partnerships. In other words, collaboration between organizations is not widespread but selective. This may be related to geographical factors and the exchange of personnel between organizations. Many organizations are newly active in the field. This can be inferred from their fewer but newer publications. The setting of figure parameters is provided in Appendix A Table A2.

3.2.3. Countries

Table 3 shows the most productive countries over the past 20 years in the field of multi-USVs. China has the largest number of publications and the most recent average publication year, demonstrating strong vitality in the field. The United States, the United Kingdom, Australia, the Netherlands, and Portugal have also produced many significant results, as evidenced by their average citations per publication exceeding 15. In terms of average publication year, Australia, Spain, South Korea, Canada, and the Netherlands have been active in recent years.
It is worth mentioning that the United States is a highly influential country in the field, but no American organizations appear in previous organization analyses. A more detailed analysis reveals that the publication characteristics in the United States are dispersed, with many independent organizations but a relatively low number of publications per organization.
Figure 5 shows that there are broad cooperative relationships among countries, but they also tend to form specific partnerships. Namely, collaboration between countries is not widespread but limited. This reflects the emerging nature of the field of multi-USVs. With increased collaboration and exchange between countries, the immense potential within this field is expected to be unleashed. The setting of figure parameters is provided in Appendix A Table A2.

3.3. Citation Network

3.3.1. Papers

Table 4 shows the most influential papers in the field of multi-USVs over the past 20 years. Formation control dominates the field, with a few papers discussing path planning and collision avoidance.
Figure 6 shows the citation network, which illustrates the connections and distinctions between different research interests. The green clusters mainly discuss formation control issues of multi-USVs. The blue cluster primarily explores cooperative path planning problems, while the red cluster focuses on collision avoidance problems in relation to multi-USVs. It is worth noting that cooperative path planning, collision avoidance, and formation techniques have a lot of overlap as they inevitably affect each other in multi-USV situations. The setting of figure parameters is provided in Appendix A Table A2.

3.3.2. Journals

Table 5 shows the most influential journals in the field. In addition to traditional marine research journals, the multi-USV field intersects with computer science and communication engineering. Moreover, interdisciplinary research is likely to be highly influential.
The journal co-citation network in Figure 7 aligns with the results in Table 5. Based on marine engineering studies, research on multi-USVs has formed an interdisciplinary field. It has attracted a large number of scholars from computer science, cybernetics, and automation and has found new integration points in communication engineering. The setting of figure parameters is provided in Appendix A Table A2.

3.4. Keywords and Terms

3.4.1. Keywords

As shown in Figure 8, the keywords heatmap indicates that the research topics in the field of multi-USVs overlap with mainstream topics in the field of single USVs. These topics include path planning, tracking, collision avoidance, and algorithms. Specific topics for multi-USVs involve formation control, task allocation, cooperative operation, heterogeneous fleets, more advanced algorithms, and artificial intelligence. The setting of figure parameters is provided in Appendix A Table A2.

3.4.2. Terms

Figure 9 shows similar research hotspots from a terminological perspective. It additionally presents some insightful findings; for example, constraints conditions, such as time and environment, computational and communication issues, system stability and robustness, and specific collaborative scenarios, like oil spill confinement and search and rescue. The setting of figure parameters is provided in Appendix A Table A2.

3.5. Supportive Techniques for a Multi-USV System

After conducting the bibliometric analysis, we have summarized key information, including publication trends, influential scholars, organizations, countries, papers, and journals, in the field of multi-USVs. Through the analysis of keywords and terms, we have also identified research hotspots in the field. In the subsequent section, instead of techniques for a single USV, we will delve into the key supporting techniques that are particularly well suited for a multi-USV system.

3.5.1. Cooperative Path Planning

Optimal path planning of USVs can avoid obstacles and navigate between the starting and target points [38,47,48,49]. According to the type of modeling of the configuration space, the methods can be divided into several categories; for instance, mathematical optimization techniques, like grid-based heuristic approaches, such as A* and D*, as well as dynamic programming (DP) and model predictive control (MPC) [19,50], stochastic programming methods, like rapidly exploring random trees (RRTs), and potential field methods, like artificial potential fields (APFs) [20].
When a USV is assigned multiple targets, the order in which the targets are visited needs to be determined. This is called the road network search route planning problem, also known as the traveling salesman problem (TSP) [20,51]. Initially, applying the self-organizing map (SOM) to the TSP did not yield a collision-free route in an obstructed environment [20]. Later researchers improved the algorithm to solve the problem of multi-target path planning [52] and applied it to marine environments with obstacles [53]. However, the multi-target path planning issue in uncertain marine environment remains to be fully studied.
Multi-USV cooperative path planning is distinguishable between centralized and distributed methodologies [20]. The centralized approach requires the aggregation of all USVs and target data for a unified arrangement to achieve the best collision-free route. The centralized method makes it easier to obtain the optimal solution than the distributed method, whereas the centralized path planning must adhere to the spatial distance constraints, which requires both obstacle avoidance and communication maintenance among USVs. Consequently, this leads to inherent disadvantages, including complex computations, elevated communication bandwidth needs, and inadequate real-time response. The distributed method assigns missions to individual USVs, enabling each unit to make decisions and plan paths independently. The distributed approach overcomes the constraints inherent in centralized methodologies. Each USV is capable of making independent decisions, exchanging information, and collaborating, thereby diminishing the computational load and intricacy. Deep reinforcement learning methods are also being used for multi-USV collision-free path planning [54].

3.5.2. Multi-Task Allocation

A proficient task distribution method is essential for the synchronized operation of multiple USVs [55]. Mathematically speaking, the task allocation issue is fundamentally akin to the traveling salesman problem (TSP) [56]. Such problems exhibit NP hard traits and can be addressed using both deterministic and heuristic algorithms.
Algorithms of a deterministic nature, like linear programming (LP) [57], provide exact accuracy yet are sluggish in computation, rendering them unsuitable for problems with many dimensions. Algorithms of a heuristic nature can yield nearly the best outcomes and excel over deterministic methods in computational efficiency. As a result, heuristic strategies, including particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithms (GAs), are extensively applied to the multi-task assignment (MTA) problem [58,59,60,61].
Owing to the evolution of artificial intelligence in recent years, attention has started to be paid to using artificial neural networks and machine learning methods to solve the problem of MTA [62]. Self-organizing maps (SOMs) are widely used in multi-task allocation problems due to their simplicity and good performance [63,64,65]. However, there are relatively few papers on multi-USV multi-task allocation using the SOM approaches. Some researchers have tried to modify the original SOM algorithm in complex marine environments considering obstacles, energy consumption, communication range, etc. [53,56].

3.5.3. Formation Control

Numerous scholars have focused on the cooperative formation control of multiple USVs [37,39,42,43,44,46,66]. There are two main formation control architectures: centralized control and distributed control [5]. The centralized control approach offers strong global regulatory abilities. The aggregate system is centralized in a main control node or command unit, which could be land based, on manned ships, or on unmanned ships. The command level employs comprehensive information to refine decision making and delegate tasks. An individual USV lacks the capacity for independent decision making. The inter-USV system control is orchestrated under the directive of the command-level system. Nevertheless, centralized control exhibits limited robustness. Excessive computation and latency may occur when the number of USVs increases. Distributed control has no central control node. Each subsystem is independent and equal and makes its own decisions and shares information with each other. Consequently, distributed control offers superior fault tolerance and robustness. It apportions the overarching complex issue to individual subsystems for discrete resolution, thus significantly enhancing the efficacy of collaborative formation control.
Leader–follower approach. Leader–follower formation control has been extensively studied due to its simplicity and reliability [67]. Taking into account unknown perturbations, the follower employs an approximation-based control strategy capable of following the intended path predicated on the leader’s location and the preset formation geometry [29]. Under the further condition of limited communication, a nonlinear filter is implemented by the follower to gauge the velocity data compromised by packet losses and noise. Utilizing the dynamic surface control (DSC) method and a neural network (NN), the follower relies solely on the leader’s positional data, resulting in a much simpler formation controller than the traditional backstepping method [37]. In addition, formation control for multiple leaders, also known as containment control, has also been studied [68,69,70]. However, in these pieces of literature on leader–follower-based formation control, it is evident that the followers need to uphold a steady arrangement relative to the leader, which cannot help but be impacted by single-point failures [43,71]. With an increasing number of USVs, additional pre-specified information concerning the established formation pattern is necessary, escalating the complexity of the control system and posing significant challenges in practical deployment. Another drawback is that the networked system’s information sharing is confined by the bounds of communication distance. As a result, when numerous USVs engage in this coordination framework or additional USVs are integrated into the network, the impact on the centralized leader–follower control is substantial. In this light, the potential of distributed control based on local neighborhood information remains to be explored [72,73].
Artificial potential field. Formation control can use attractive and repulsive forces to drive vehicles according to neighboring positions [74,75,76]. However, it should be noted that dynamic control in the presence of unknown time-varying disturbances is often overlooked, which might not suffice for analyzing the actual dynamics and offering technical support [29]. Regarding uncertain dynamics, control methods based on approximation NNs [77], fuzzy systems [73,78], and fuzzy NNs [79,80] are found to be effective and widely used beyond artificial potential field methods.
Behavior-based approach. The fundamental concept of the behavior-based approach involves pre-engineering the specific behavioral regulations and localized control mechanisms for each USV, aligned with the anticipated collective behavior once the formation system is established. In contrast to the leader–follower model, this method achieves cooperation through the exchange of positional and velocity data among USVs, whereas the complexity of integrating sub-behaviors could escalate, potentially failing to satisfy the desired control specifications in intricate conditions [5].
Graph theory method. In a multi-agent system, the interactions among participants are depicted through a graphical representation. Through interaction and sharing of formation layout details, individuals achieve the formation. Each participant in the formation corresponds to a node in the graph, with the edges between nodes indicating the topological relationships among them. The formation’s structural framework can be categorized into directed and undirected graphs based on the connectivity between nodes, with directed graphs indicating one-way relationships and undirected graphs indicating two-way relationships. Methods grounded in graph theory can capitalize on the local communication or sensory capabilities of individuals. By incorporating graph data into the system, both fault tolerance and tracking precision can be enhanced [81].

3.5.4. AI-Powered Multi-USV System

Artificial intelligence techniques can refine the decision-making procedure by excavating and scrutinizing the data embedded in samples and autonomously tuning system parameters to guarantee safety, enhance efficiency, and minimize energy usage [5]. This section encapsulates a selection of the most progressive intelligent methodologies, encompassing reinforcement learning, neural networks, and fuzzy logic.
Reinforcement learning. Reinforcement learning (RL) is a method of learning from the environment to maximize cumulative reward values based on the behavior exhibited. Unlike supervised learning techniques, which rely on true and false examples to guide behavior, RL uses repeated trials to discover the optimal behavior strategy. The USV formation control system is inherently complex, characterized by internal structural uncertainties and unpredictable external navigation conditions. As the number of individuals in the USV formation increases and the formation changes frequently, non-intelligent control methods struggle to make the best decisions. Through ongoing training, RL can comprehend diverse intricate navigational settings and execute final decisions along with control actions. Many studies have attempted to use RL methods to solve the dynamic and collaborative collision avoidance problems of USV swarms [82,83,84,85,86].
Neural networks. Neural networks excel in performance estimation and are adept at handling dynamic environment conditions and model uncertainty within USV formation control systems [87,88,89,90,91]. These networks, through their observer models, can concurrently detect model uncertainty and velocity information. However, the convergence of parameters is contingent upon the presence of continuous excitation conditions. As the number of USVs increases, the computational load of formation control increases. This problem can be solved by integrating neural networks with minimum learning parameter (MLP) techniques [43,92]. Additionally, to reduce the communication burden and actuator execution wear to achieve better resource efficiency, a dynamic event-triggered mechanism featuring adjustable thresholds can be deployed [93].
Fuzzy logic. Fuzzy logic is grounded in fuzzy set theory, fuzzy linguistic variables, and fuzzy logic reasoning. It replicates human thought and decision-making mechanisms. This approach can carry out fuzzy inference and yield fitting actuator inputs by introducing fuzzified signals into the fuzzy rule set [94]. Fuzzy logic utilizes the expertise of specialists to articulate the connections between system variables via control rules, adeptly managing nonlinear and time-variant issues. In the context of USV formation control, which grapples with actuator saturation and unidentified nonlinearities, an adaptive fuzzy strategy can be applied to gauge crucial unknown nonlinear elements [95]. Using fuzzy approximation techniques, the formation control of multiple USVs in a distributed manner, guided by a single parameter path, can be realized with better performance [81]. Additionally, a control design for forward-driven USVs can be formulated using an adaptive fuzzy nonsingular terminal sliding mode method, which also aims to decrease the convergence time [96].

4. Discussion

Multi-USV systems hold significant potential for both civil and military applications, attracting increasing attention from researchers globally. However, the field also faces unique challenges in the cooperative operations of plural USVs. Addressing these existing issues and introducing new scientific and engineering solutions will drive further advancements in the field of multi-USVs.

4.1. Current Challenges

4.1.1. Communication Problem

Achieving formation control for USVs to follow desired trajectories and maintain a specific formation configuration has been a significant research focus in marine engineering [97] and control communities [89]. Effective communication is essential for USVs to collaborate, ensuring information transmission and data exchange [98]. However, USVs operating in complex marine environments often face challenges such as communication losses, time delays [99], and unknown uncertainties [100,101]. Maritime communication technology generally suffers from low efficiency and narrow bandwidth, limiting the distances between USVs [81,102,103]. Addressing these communication constraints is crucial for large-scale cooperative formation control.

4.1.2. System Failure

System breakdowns of instruments are unavoidable because of seawater corrosion and finite lifespan. Improving fault tolerance in multi-USV systems requires robust fault diagnosis and fault-tolerant control. Fault detection, a key technique in fault diagnosis, has garnered significant attention. Many existing approaches design controllers and fault detectors separately, leading to complex systems. Simultaneous fault detection and control modules are essential for effective system fault management. Most current methods are passive, relying on fault residual signals without additional input, resulting in poor diagnosis effects. This approach introduces redundancy and conservatism, complicating detection in complex marine environments. Coordinated task execution by multiple USVs can enhance system fault tolerance, robustness, and task success rates [29,104,105]. However, a problem with a single USV may be likely to spread to the swarm level, causing systemic paralysis. Preventing and responding to swarm-level problems is also a huge challenge.

4.1.3. Dynamic and Uncertain Environmental Conditions

Environmental factors like waves, winds, and sea currents have a substantial effect on USVs. Waves, generated by winds and tides, are typically regarded as stationary stochastic processes in the design of motion control systems. Winds have a direct impact on USV movement, affecting roll and yaw, and occasionally undermining safe navigation. Ocean currents also affect USV motion and dynamic positioning accuracy. To mitigate these disturbances, researchers improve controller robustness [106] and estimate disturbances via observers [107,108]. System uncertainties, due to external disturbances and system characteristics, challenge model accuracy and stability. Methods like model predictive control, neural network control, and sliding mode control are proposed to address these uncertainties, though they may increase controller complexity [109,110,111]. Techniques for parameter identification, such as the least squares approach, Kalman filtering, particle swarm optimization, and machine learning algorithms, are essential for precise system modeling. It is already a major challenge for a single USV to cope with dynamic and uncertain environmental conditions, and it remains unclear whether things will be better or worse when a swarm of USVs deal with these external environmental challenges.

4.1.4. Nonlinearity and Constraints

The efficacy of USV autonomous navigation is contingent upon the precision of control techniques and models. However, because of the constant variations in USV load conditions and environment disruptions, linearized models often fall short of precisely capturing the dynamic response traits. Nonlinearity in models, albeit more precise, may lead to latency and unpredictability. The accuracy of nonlinear mathematical models is called for the demand for advanced control performance. System identification techniques, including least squares, maximum likelihood estimation, model reference adaptive systems, support vector machines, and artificial intelligence, are pivotal in identifying these models. Data-driven control techniques excel in handling unknown nonlinear systems but may lack robustness [112]. Multi-USV systems face constraints like input, state, and output limitations due to actuator and sensor capacities. These constraints affect system performance and stability, with input saturation leading to nonlinear control and instability. Although control methods like model predictive control, auxiliary system methods, and instruction regulators have been proposed to address these challenges [111,113], nonlinear issues and constraints remain significant obstacles in the cooperative operation of multiple USVs.

4.2. Future Directions

4.2.1. Dynamic Formation Switching

Presently, USV formation control under ideal and typical sea conditions ensures stability, precision, and a swift response. However, motion control under complex conditions requires further research to enhance energy efficiency and reliability. In severe conditions, the interaction mechanisms between USVs and water are not well understood, necessitating advanced modeling of complex motion systems. Additionally, the trend of combining different formation structures is growing. Each structure has unique characteristics suited to specific scenarios. Selecting the appropriate formation based on the scenario and requirements is crucial for effective multi-USV operations.

4.2.2. Data Reduction and Fast Transmission

Modeling a fleet of USVs introduces considerable challenges because of intrinsic nonlinearity, uncertainties, and external interference. The modeling process typically requires numerous assumptions, which can result in inaccuracies. In light of the difficulty or impossibility of acquiring exact models, numerous scholars have resorted to data-driven modeling techniques utilizing raw input and output data [12,114]. However, achieving real-time control requires rapidly obtaining data-driven models in complex ocean environments. This presents a significant opportunity for real-time data-driven modeling of USVs in actual scenarios. For example, developing algorithms that can efficiently reduce the volume of data without losing critical information can significantly improve the speed and accuracy of data-driven models. Meanwhile, innovating faster and more reliable communication protocols to ensure seamless data transmission within a multi-USV system and between the swarm and a control center is also a promising direction.

4.2.3. Algorithm Improvement

While artificial intelligence advancements enhance control effectiveness, they also increase algorithm complexity and reduce real-time performance. At present, neural network algorithms are frequently utilized to gauge unknown disturbances, yet they commonly encounter issues with slow convergence and have a propensity to get stuck in local minima. Deeper exploration is needed to enhance the convergence velocity of neural networks and circumvent local minima. Moreover, reinforcement learning confronts constraints due to extended training periods, substantial computational demands, and sluggish model convergence. Careful examination is necessary for the viability and precision of these algorithms across diverse scenarios. Therefore, the opportunity lies in developing hierarchical controllers that leverage the strengths of different control methods. By focusing on improving both the accuracy and response speed of controllers, the overall performance of multi-USV systems in dynamic environments can be enhanced. In addition, the rise of large language models (LLMs) has injected new possibilities into this field. For example, to create a more intelligent thinking machine brain, the human operator may only need to have a natural language conversation with the intelligent brain, which can then automatically control the whole swarm to complete a task. The integration of multi-USV systems with cutting-edge technology may lead to remarkable innovations.

4.2.4. Perception Fusion

In addition to studying multi-USV systems in isolation, there is an increasing interest in combining USVs with unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) to boost task execution capabilities. USVs are enabled to conduct a thorough analysis of their surroundings through the integration of multi-source perception systems. By deploying functions of various sensors, the capabilities of the whole team can be expanded [5]. However, the majority of current research presumes optimal conditions for the reception of sensor data. In fact, USVs often face challenges, such as time delays and noise interference. Techniques like singular value decomposition [115] and adaptive unscented Kalman filters [116] are being utilized to address hydrodynamic coefficient uncertainties caused by noise. The key opportunity lies in advancing perception fusion technologies to overcome these practical challenges, thereby enabling more accurate and reliable environmental analysis for USVs.

4.2.5. Real-World Application Scenarios

In addition to advancing technology, another promising direction is exploring real-world application scenarios for multi-USV systems. The teamwork of multiple USVs has a wide range of potential applications that can significantly enhance maritime operations. One key application is in search and rescue missions, where a fleet of USVs can cover large areas more efficiently and coordinate to locate and assist distressed vessels or individuals [6]. In environmental monitoring, multiple USVs can work together to collect data on water quality, marine life, and pollution levels, providing comprehensive and real-time insights [7,35]. Additionally, in oceanographic research, USVs can perform synchronized sampling and mapping tasks, enabling detailed studies of marine ecosystems and underwater topography [8]. The use of multiple USVs in commercial applications, such as offshore oil exploration and cargo shipping, can improve the efficiency and safety of operations [16,17,36]. Maritime security is another critical area where USVs can patrol and monitor vast oceanic regions, detect suspicious activities, and respond to threats collaboratively [9,10]. Overall, the teamwork of multiple USVs offers enhanced capabilities, redundancy, and flexibility, making them important participants in maritime environments. Future research can consider testing the performance of multi-USV systems in real-world or simulated scenarios and exploring more scenarios with safety, scientific, economic, and military value.

4.3. Limitations

One limitation of the current study is the language bias inherent in databases that predominantly use English. Although English is the most mainstream language for international academic communication, such a bias may result in the neglect of valuable research published in other languages, thereby influencing the analysis. Future research should consider incorporating databases that include non-English research to provide a more comprehensive view. Secondly, the exclusive use of VOSviewer software may limit the generalizability of the findings. To enhance the robustness and validity of future studies, it is recommended to employ multiple tools, such as CiteSpace or Gephi, for cross-validation.
Additionally, several common assumptions underlying bibliometric analysis outcomes should be considered. For instance, assuming that the number of publications and citation counts are proxies for quality can sometimes be biased. Researchers should read relevant articles in depth to fully explore valuable insights and potential. Furthermore, if author and institution attributions are inaccurate, the evaluation of productivity and collaboration networks can be flawed, leading to incorrect assessments of research impact. Moreover, it is assumed that publication trends and citation patterns remain temporally stable, but they may change over time. Therefore, bibliometric analysis needs to keep pace with evolving trends and be reviewed from a developmental perspective.

5. Conclusions

This study provides a comprehensive bibliometric analysis of the literature in the field of multi-USVs. The analysis reveals publication trends and influential scholars, organizations, countries, papers, and journals in the field. Research hotspots, challenges, and future directions are also pointed out. To provide a comprehensive understanding of the field, we visualized these results and presented the networks of the author, organization, and country collaborations, the networks of paper and journal citations, and the heatmaps of concerned keywords and terms. These visual aids serve to demonstrate the utility of bibliometric analysis in gaining insights into the development of multi-USV research.
Research on multi-USVs shows tremendous appeal, as scholars from various disciplines continue to contribute to this interdisciplinary field. Scientists and engineers are working together to optimize the supportive techniques of multi-USV systems, such as cooperative path planning, multi-task allocation, formation control, etc., while also addressing challenges involving algorithms, communication, system stability, and complex environments. The integration of artificial intelligence is expected to bring remarkable sparks. As researchers continue to address the unique challenges and explore innovative solutions, the capabilities and applications of multi-USV systems will expand, unleashing their huge potential in both civil and military aspects and paving the way for future scientific and engineering breakthroughs.
This work hopes to contribute to the field by highlighting the progress made so far in the field and shed light on the gaps that need to be bridged to advance the field further. It can serve as a valuable resource for researchers, practitioners, and policymakers.

Author Contributions

Conceptualization, J.X.; methodology, J.X. and Y.S.; software, J.X. and Y.S.; validation, J.X. and H.H.; resources, J.X. and H.H.; data curation, J.X. and Y.S.; writing—original draft preparation, J.X. and Y.S.; review and editing, J.X. and H.H.; visualization, J.X. and Y.S.; supervision, J.X. and H.H.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 52201413, the Natural Science Foundation of Science and Technology Commission of Shanghai Municipality, grant number 23ZR1434400, the Opening Foundation of Key Laboratory of Safety and Risk Management on Transport Infrastructures for the Ministry of Transport, grant number 2023KFKT015, the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University, grant number SL2022PT107, and the Startup Fund for Young Faculty at Shanghai Jiao Tong University, grant number 22X010503469. The APC was funded by Shanghai Jiao Tong University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available in the Web of Science (WoS) Core Collection database. https://images.webofknowledge.com/WOKRS59B4/help/ja/WOS/hp_whatsnew_wos.html (accessed on 10 July 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Search iterations.
Table A1. Search iterations.
IterationSearch QueryResults
1TS = (unmanned surface vessel)1072
2TS = (unmanned surface vessel) OR TS = (unmanned surface ship)1555
3TS = (unmanned surface vessel) OR TS = (unmanned surface ship) OR TS = (unmanned surface vehicle)8456
4(TS = (marine) OR TS = (ocean) OR TS = (sea) OR TS = (water) OR TS = (waterway)) AND (TS = (unmanned surface vessel) OR TS = (unmanned surface ship) OR TS = (unmanned surface vehicle))2872
5(TS = (marine) OR TS = (ocean) OR TS = (sea) OR TS = (water) OR TS = (waterway)) AND (TS = (USV) OR TS = (unmanned surface vessel) OR TS = (unmanned surface ship) OR TS = (unmanned surface vehicle) OR TS = (driverless surface vessel) OR TS = (driverless surface ship) OR TS = (driverless surface vehicle) OR TS = (automated surface vessel) OR TS = (automated surface ship) OR TS = (automated surface vehicle) OR TS = (automatic surface vessel) OR TS = (automatic surface ship) OR TS = (automatic surface vehicle) OR TS = (autonomous surface vessel) OR TS = (autonomous surface ship) OR TS = (autonomous surface vehicle))5525
6((TS = (marine) OR TS = (ocean) OR TS = (sea) OR TS = (water) OR TS = (waterway)) AND (TS = (USV) OR TS = (unmanned surface vessel) OR TS = (unmanned surface ship) OR TS = (unmanned surface vehicle) OR TS = (driverless surface vessel) OR TS = (driverless surface ship) OR TS = (driverless surface vehicle) OR TS = (automated surface vessel) OR TS = (automated surface ship) OR TS = (automated surface vehicle) OR TS = (automatic surface vessel) OR TS = (automatic surface ship) OR TS = (automatic surface vehicle) OR TS = (autonomous surface vessel) OR TS = (autonomous surface ship) OR TS = (autonomous surface vehicle))) NOT (TS = (air) OR TS = (aerial) OR TS = (underwater) OR TS = (submarine) OR TS = (land) OR TS = (road))2480
7(((TS = (marine) OR TS = (ocean) OR TS = (sea) OR TS = (water) OR TS = (waterway)) AND (TS = (USV) OR TS = (unmanned surface vessel) OR TS = (unmanned surface ship) OR TS = (unmanned surface vehicle) OR TS = (driverless surface vessel) OR TS = (driverless surface ship) OR TS = (driverless surface vehicle) OR TS = (automated surface vessel) OR TS = (automated surface ship) OR TS = (automated surface vehicle) OR TS = (automatic surface vessel) OR TS = (automatic surface ship) OR TS = (automatic surface vehicle) OR TS = (autonomous surface vessel) OR TS = (autonomous surface ship) OR TS = (autonomous surface vehicle))) NOT (TS = (air) OR TS = (aerial) OR TS = (underwater) OR TS = (submarine) OR TS = (land) OR TS = (road))) AND (TS = (swarm) OR TS = (cluster) OR TS = (formation) OR TS = (multiple) OR TS = (cooperative) OR TS = (collaborative) OR TS = (task allocation) OR TS = (dynamic planning))708
Notes: TS = topics; editions: SCIE, SSCI, CPCI-S, CPCI-SSH; timespan: 2004–2024; document types: article, conference proceeding, review.
Table A2. Parameter setting for bibliographic mapping.
Table A2. Parameter setting for bibliographic mapping.
FigureThresholdAttr.Repu.Type
Figure 3. Author collaboration network using analysis of co-authorship.13−2Overlay
Figure 4. Organization collaboration network using analysis of co-authorship.14−1Overlay
Figure 5. Country collaboration network using analysis of co-authorship.14−4Overlay
Figure 6. Citation map for papers cited more than 5 times.51−2Network
Figure 7. Co-citation map for journals cited more than 20 times.201−1Network
Figure 8. Keyword heatmap.54−8Density
Figure 9. Term heatmap.52−5Density

References

  1. Peng, Z.; Wang, J.; Wang, D.; Han, Q.L. An Overview of Recent Advances in Coordinated Control of Multiple Autonomous Surface Vehicles. IEEE Trans. Ind. Inform. 2021, 17, 732–745. [Google Scholar] [CrossRef]
  2. Vasilijevic, A.; Nad, D.; Mandic, F.; Miskovic, N.; Vukic, Z. Coordinated Navigation of Surface and Underwater Marine Robotic Vehicles for Ocean Sampling and Environmental Monitoring. IEEE/ASME Trans. Mechatron. 2017, 22, 1174–1184. [Google Scholar] [CrossRef]
  3. Wang, N.; Sun, J.C.; Er, M.J.; Liu, Y.C. A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles. IEEE Trans. Cybern. 2016, 46, 1106–1117. [Google Scholar] [CrossRef]
  4. Fan, Y.; Huang, H.; Tan, Y. Robust Adaptive Path Following Control of an Unmanned Surface Vessel Subject to Input Saturation and Uncertainties. Appl. Sci. 2019, 9, 1815. [Google Scholar] [CrossRef]
  5. Er, M.J.; Ma, C.; Liu, T.; Gong, H. Intelligent motion control of unmanned surface vehicles: A critical review. Ocean Eng. 2023, 280, 114562. [Google Scholar] [CrossRef]
  6. Zhang, H.; Huang, Y.; Qin, H.; Geng, Z. USV Search Mission Planning Methodology for Lost Target Rescue on Sea. Electronics 2023, 12, 4584. [Google Scholar] [CrossRef]
  7. Babić, A.; Ferreira, F.; Kapetanović, N.; Mišković, N.; Bibuli, M.; Bruzzone, G.; Motta, C.; Ferretti, R.; Odetti, A.; Caccia, M.; et al. Cooperative marine litter detection and environmental monitoring using heterogeneous robotic agents. In Proceedings of the OCEANS 2023-Limerick, Limerick, Ireland, 5–8 June 2023; pp. 1–6. [Google Scholar]
  8. MahmoudZadeh, S.; Abbasi, A.; Yazdani, A.; Wang, H.; Liu, Y. Uninterrupted path planning system for Multi-USV sampling mission in a cluttered ocean environment. Ocean Eng. 2022, 254, 111328. [Google Scholar] [CrossRef]
  9. Xie, J.; Zhou, R.; Luo, J.; Peng, Y.; Liu, Y.; Xie, S.; Pu, H. Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles. J. Mar. Sci. Eng. 2020, 8, 936. [Google Scholar] [CrossRef]
  10. Sinisterra, A.J.; Dhanak, M.R.; Von Ellenrieder, K. Stereovision-based target tracking system for USV operations. Ocean Eng. 2017, 133, 197–214. [Google Scholar] [CrossRef]
  11. Bai, Y.; Wang, X.; Yang, Y.; Yu, J.; Zhao, Z.; Jin, X. Autonomous Decision-Making of Unmanned Surface Vehicle Operation with Multi-Source Sensing. In Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China, 6–8 November 2020; pp. 2570–2575. [Google Scholar]
  12. Wang, N.; Gao, Y.; Zhang, X. Data-Driven Performance-Prescribed Reinforcement Learning Control of an Unmanned Surface Vehicle. IEEE Trans. Neural Netw. Learn. Syst. 2021, 32, 5456–5467. [Google Scholar] [CrossRef]
  13. Liu, G.; Wu, J.; Wen, N.; Zhang, R. A Review on Collaborative Planning of Multiple Unmanned Surface Vehicles. In Proceedings of the 2018 Chinese Automation Congress (CAC), Xi’an, China, 30 November–2 December 2018; pp. 2158–2163. [Google Scholar]
  14. Mostafa, M.Z.; Khater, H.A.; Rizk, M.R.; Bahasan, A.M. A novel GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance autonomous navigation, guidance and control system of AUSVs based on ADSF Combined Filter. Measurement 2019, 146, 590–605. [Google Scholar] [CrossRef]
  15. Yang, P.; Xue, J.; Hu, H. A Bibliometric Analysis and Overall Review of the New Technology and Development of Unmanned Surface Vessels. J. Mar. Sci. Eng. 2024, 12, 146. [Google Scholar] [CrossRef]
  16. Jovanović, I.; Perčić, M.; BahooToroody, A.; Fan, A.; Vladimir, N. Review of research progress of autonomous and unmanned shipping and identification of future research directions. J. Mar. Eng. Technol. 2024, 23, 82–97. [Google Scholar] [CrossRef]
  17. Munim, Z.H. Autonomous ships: A review, innovative applications and future maritime business models. Supply Chain. Forum Int. J. 2019, 20, 266–279. [Google Scholar] [CrossRef]
  18. Wróbel, K.; Gil, M.; Krata, P.; Olszewski, K.; Montewka, J. On the use of leading safety indicators in maritime and their feasibility for Maritime Autonomous Surface Ships. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2021, 237, 314–331. [Google Scholar] [CrossRef]
  19. Bibuli, M.; Singh, Y.; Sharma, S.; Sutton, R.; Hatton, D.; Khan, A. A Two Layered Optimal Approach towards Cooperative Motion Planning of Unmanned Surface Vehicles in a Constrained Maritime Environment. IFAC-PapersOnLine 2018, 51, 378–383. [Google Scholar] [CrossRef]
  20. Yao, P.; Lou, Y.; Zhang, K. Multi-USV cooperative path planning by window update based self-organizing map and spectral clustering. Ocean Eng. 2023, 275, 114140. [Google Scholar] [CrossRef]
  21. Gao, S.; Peng, Z.; Liu, L.; Wang, H.; Wang, D. Coordinated target tracking by multiple unmanned surface vehicles with communication delays based on a distributed event-triggered extended state observer. Ocean Eng. 2021, 227, 108283. [Google Scholar] [CrossRef]
  22. Brecher, J.N.; Zereik, E.; Bibuli, M.; Loth, M.; Glotzbach, T. Cooperative Distributed Navigation, Guidance, and Control Systems for Autonomous Surface Vehicles with Different Physical Connections. IFAC-PapersOnLine 2021, 54, 208–215. [Google Scholar] [CrossRef]
  23. Liu, H.; Chen, G.; Tian, X. Cooperative formation control for multiple surface vessels based on barrier Lyapunov function and self-structuring neural networks. Ocean Eng. 2020, 216, 108163. [Google Scholar] [CrossRef]
  24. Huang, Y.; Wu, D.; Li, L.; Feng, N. Event-triggered cooperative path following control of multiple underactuated unmanned surface vehicles with complex unknowns and actuator saturation. Ocean Eng. 2022, 249, 110740. [Google Scholar] [CrossRef]
  25. Tan, G.; Zhuang, J.; Zou, J.; Wan, L. Multi-type task allocation for multiple heterogeneous unmanned surface vehicles (USVs) based on the self-organizing map. Appl. Ocean Res. 2022, 126, 103262. [Google Scholar] [CrossRef]
  26. Tan, G.; Sun, H.; Du, L.; Zhuang, J.; Zou, J.; Wan, L. Coordinated control of the heterogeneous unmanned surface vehicle swarm based on the distributed null-space-based behavioral approach. Ocean Eng. 2022, 266, 112928. [Google Scholar] [CrossRef]
  27. Li, X.; Gao, M.; Kang, Z.; Sun, H.; Liu, Y.; Yao, C.; Zhang, A. Collaborative search and rescue based on swarm of H-MASSs using consensus theory. Ocean Eng. 2023, 278, 114426. [Google Scholar] [CrossRef]
  28. Zhu, Y.; Bai, J.; Guo, G. Cooperative target substitution tracking control of multiple unmanned surface vehicles with substitute USVs. Ocean Eng. 2023, 273, 113903. [Google Scholar] [CrossRef]
  29. Liang, X.; Qu, X.; Hou, Y.; Li, Y.; Zhang, R. Distributed coordinated tracking control of multiple unmanned surface vehicles under complex marine environments. Ocean Eng. 2020, 205, 107328. [Google Scholar] [CrossRef]
  30. Fan, J.; Liao, Y.; Li, Y.; Jiang, Q.; Wang, L.; Jiang, W. Formation Control of Multiple Unmanned Surface Vehicles Using the Adaptive Null-Space-Based Behavioral Method. IEEE Access 2019, 7, 87647–87657. [Google Scholar] [CrossRef]
  31. van Eck, N.J.; Waltman, L. VOS: A New Method for Visualizing Similarities Between Objects. In Proceedings of the 30th Annual Conference of the German-Classification-Society, Berlin, Germany, 8–10 March 2006; pp. 299–306. [Google Scholar]
  32. Li, J.; Goerlandt, F.; Reniers, G. An overview of scientometric mapping for the safety science community: Methods, tools, and framework. Saf. Sci. 2021, 134, 105093. [Google Scholar] [CrossRef]
  33. Veiga-del-Baño, J.M.; Cámara, M.Á.; Oliva, J.; Hernández-Cegarra, A.T.; Andreo-Martínez, P.; Motas, M. Mapping of emerging contaminants in coastal waters research: A bibliometric analysis of research output during 1986–2022. Mar. Pollut. Bull. 2023, 194, 115366. [Google Scholar] [CrossRef]
  34. Demirci, S.M.E.; Elçiçek, H. Scientific awareness of marine accidents in Europe: A bibliometric and correspondence analysis. Accid. Anal. Prev. 2023, 190, 107166. [Google Scholar] [CrossRef]
  35. Di Ciaccio, F.; Troisi, S. Monitoring marine environments with Autonomous Underwater Vehicles: A bibliometric analysis. Results Eng. 2021, 9, 100205. [Google Scholar] [CrossRef]
  36. Vasconcelos, R.N.; Lima, A.T.C.; Lentini, C.A.D.; Miranda, G.V.; Mendonça, L.F.; Silva, M.A.; Cambuí, E.C.B.; Lopes, J.M.; Porsani, M.J. Oil Spill Detection and Mapping: A 50-Year Bibliometric Analysis. Remote Sens. 2020, 12, 3647. [Google Scholar] [CrossRef]
  37. Peng, Z.; Wang, D.; Chen, Z.; Hu, X.; Lan, W. Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles with Uncertain Dynamics. IEEE Trans. Control Syst. Technol. 2013, 21, 513–520. [Google Scholar] [CrossRef]
  38. Kuwata, Y.; Wolf, M.T.; Zarzhitsky, D.; Huntsberger, T.L. Safe Maritime Autonomous Navigation With COLREGS, Using Velocity Obstacles. IEEE J. Ocean. Eng. 2014, 39, 110–119. [Google Scholar] [CrossRef]
  39. Shojaei, K. Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque. Ocean Eng. 2015, 105, 196–205. [Google Scholar] [CrossRef]
  40. Peng, Z.; Liu, L.; Wang, J. Output-Feedback Flocking Control of Multiple Autonomous Surface Vehicles Based on Data-Driven Adaptive Extended State Observers. IEEE Trans. Cybern. 2021, 51, 4611–4622. [Google Scholar] [CrossRef]
  41. Gu, N.; Wang, D.; Peng, Z.; Liu, L. Observer-Based Finite-Time Control for Distributed Path Maneuvering of Underactuated Unmanned Surface Vehicles with Collision Avoidance and Connectivity Preservation. IEEE Trans. Syst. Man Cybern. Syst. 2021, 51, 5105–5115. [Google Scholar] [CrossRef]
  42. Almeida, J.; Silvestre, C.; Pascoal, A. Cooperative control of multiple surface vessels in the presence of ocean currents and parametric model uncertainty. Int. J. Robust Nonlinear Control 2010, 20, 1549–1565. [Google Scholar] [CrossRef]
  43. Lu, Y.; Zhang, G.; Sun, Z.; Zhang, W. Adaptive cooperative formation control of autonomous surface vessels with uncertain dynamics and external disturbances. Ocean Eng. 2018, 167, 36–44. [Google Scholar] [CrossRef]
  44. Peng, Z.; Gu, N.; Zhang, Y.; Liu, Y.; Wang, D.; Liu, L. Path-guided time-varying formation control with collision avoidance and connectivity preservation of under-actuated autonomous surface vehicles subject to unknown input gains. Ocean Eng. 2019, 191, 106501. [Google Scholar] [CrossRef]
  45. Chen, L.; Hopman, H.; Negenborn, R.R. Distributed model predictive control for vessel train formations of cooperative multi-vessel systems. Transp. Res. Part C Emerg. Technol. 2018, 92, 101–118. [Google Scholar] [CrossRef]
  46. Shojaei, K. Observer-based neural adaptive formation control of autonomous surface vessels with limited torque. Robot. Auton. Syst. 2016, 78, 83–96. [Google Scholar] [CrossRef]
  47. Wang, N.; Jin, X.; Er, M.J. A multilayer path planner for a USV under complex marine environments. Ocean Eng. 2019, 184, 1–10. [Google Scholar] [CrossRef]
  48. Zhang, B.; Liu, W.; Mao, Z.; Liu, J.; Shen, L. Cooperative and Geometric Learning Algorithm (CGLA) for path planning of UAVs with limited information. Automatica 2014, 50, 809–820. [Google Scholar] [CrossRef]
  49. Xing, B.; Yu, M.; Liu, Z.; Tan, Y.; Sun, Y.; Li, B. A Review of Path Planning for Unmanned Surface Vehicles. J. Mar. Sci. Eng. 2023, 11, 1556. [Google Scholar] [CrossRef]
  50. Yao, P.; Zhao, R.; Zhu, Q. A Hierarchical Architecture Using Biased Min-Consensus for USV Path Planning. IEEE Trans. Veh. Technol. 2020, 69, 9518–9527. [Google Scholar] [CrossRef]
  51. Savla, K.; Frazzoli, E.; Bullo, F. Traveling Salesperson Problems for the Dubins Vehicle. IEEE Trans. Autom. Control 2008, 53, 1378–1391. [Google Scholar] [CrossRef]
  52. Faigl, J. An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective. Comput. Intell. Neurosci. 2016, 2016, 2720630. [Google Scholar] [CrossRef]
  53. Liu, Y.; Bucknall, R. Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations. Neurocomputing 2018, 275, 1550–1566. [Google Scholar] [CrossRef]
  54. Guan, W.; Luo, W.; Cui, Z. Intelligent decision-making system for multiple marine autonomous surface ships based on deep reinforcement learning. Robot. Auton. Syst. 2024, 172, 104587. [Google Scholar] [CrossRef]
  55. Jin, X.; Zhang, G.; Yang, M.; Peng, X.; Shi, B. Cooperative multi-task traversing with complex marine environment for multiple unmanned surface vehicles inspired by membrane computing. Ocean Eng. 2022, 266, 112586. [Google Scholar] [CrossRef]
  56. Liu, Y.; Song, R.; Bucknall, R.; Zhang, X. Intelligent multi-task allocation and planning for multiple unmanned surface vehicles (USVs) using self-organising maps and fast marching method. Inf. Sci. 2019, 496, 180–197. [Google Scholar] [CrossRef]
  57. Afonso, R.J.M.; Maximo, M.R.O.A.; Galvão, R.K.H. Task allocation and trajectory planning for multiple agents in the presence of obstacle and connectivity constraints with mixed-integer linear programming. Int. J. Robust Nonlinear Control 2020, 30, 5464–5491. [Google Scholar] [CrossRef]
  58. Wang, Z.; Yan, X. Multi-UAV Task Assignment Based on Quantum Genetic Algorithm. J. Phys. Conf. Ser. 2021, 1824, 012010. [Google Scholar] [CrossRef]
  59. Patel, R.; Rudnick-Cohen, E.; Azarm, S.; Otte, M.; Xu, H.; Herrmann, J.W. Decentralized Task Allocation in Multi-Agent Systems Using a Decentralized Genetic Algorithm. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August 2020; pp. 3770–3776. [Google Scholar]
  60. Qizilbash, A.A.H.; Henkel, C.; Mostaghim, S. Ant Colony Optimization based Multi-Robot Planner for Combined Task Allocation and Path Finding. In Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR), Kyoto, Japan, 22–26 June 2020; pp. 487–493. [Google Scholar]
  61. Han, Q. An Application of Improved PSO Algorithm in Cooperative Search Task Allocation. In Proceedings of the 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China, 22–24 January 2021; pp. 580–583. [Google Scholar]
  62. Zhang, J.; Ren, J.; Cui, Y.; Fu, D.; Cong, J. Multi-USV Task Planning Method Based on Improved Deep Reinforcement Learning. IEEE Internet Things J. 2024, 11, 18549–18567. [Google Scholar] [CrossRef]
  63. Yi, X.; Zhu, A.; Yang, S.X.; Luo, C. A Bio-Inspired Approach to Task Assignment of Swarm Robots in 3-D Dynamic Environments. IEEE Trans. Cybern. 2017, 47, 974–983. [Google Scholar] [CrossRef]
  64. Sun, W.; Zhang, F.; Xue, M.; Hu, W.; Li, L. An SOM-based algorithm with locking mechanism for task assignment. In Proceedings of the 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Ningbo, China, 19–21 November 2017; pp. 36–41. [Google Scholar]
  65. Tang, H.; Lin, A.; Sun, W.; Shi, S. An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments. Energies 2020, 13, 3296. [Google Scholar] [CrossRef]
  66. Luo, Q.; Wang, H.; Li, N.; Zheng, W. Multi-unmanned surface vehicle model-free sliding mode predictive adaptive formation control and obstacle avoidance in complex marine environment via model-free extended state observer. Ocean Eng. 2024, 293, 116773. [Google Scholar] [CrossRef]
  67. Loria, A.; Dasdemir, J.; Jarquin, N.A. Leader–Follower Formation and Tracking Control of Mobile Robots Along Straight Paths. IEEE Trans. Control Syst. Technol. 2016, 24, 727–732. [Google Scholar] [CrossRef]
  68. Wang, W.; Tong, S.; Wang, D. Adaptive Fuzzy Containment Control of Nonlinear Systems With Unmeasurable States. IEEE Trans. Cybern. 2019, 49, 961–973. [Google Scholar] [CrossRef]
  69. Dong, X.; Hu, G. Time-Varying Formation Tracking for Linear Multiagent Systems with Multiple Leaders. IEEE Trans. Autom. Control 2017, 62, 3658–3664. [Google Scholar] [CrossRef]
  70. Peng, Z.; Wang, D.; Wang, W.; Liu, L. Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design. ISA Trans. 2015, 59, 160–171. [Google Scholar] [CrossRef]
  71. Liu, L.; Wang, D.; Peng, Z. Coordinated path following of multiple underacutated marine surface vehicles along one curve. ISA Trans. 2016, 64, 258–268. [Google Scholar] [CrossRef] [PubMed]
  72. Asadi, M.M.; Ajorlou, A.; Aghdam, A.G. Distributed control of a network of single integrators with limited angular fields of view. Automatica 2016, 63, 187–197. [Google Scholar] [CrossRef]
  73. Liang, X.; Qu, X.; Wang, N.; Li, Y.; Zhang, R. A Novel Distributed and Self-Organized Swarm Control Framework for Underactuated Unmanned Marine Vehicles. IEEE Access 2019, 7, 112703–112712. [Google Scholar] [CrossRef]
  74. Palacios, L.; Ceriotti, M.; Radice, G. Close proximity formation flying via linear quadratic tracking controller and artificial potential function. Adv. Space Res. 2015, 56, 2167–2176. [Google Scholar] [CrossRef]
  75. Liu, Y.-C.; Chopra, N. Control of semi-autonomous teleoperation system with time delays. Automatica 2013, 49, 1553–1565. [Google Scholar] [CrossRef]
  76. Oland, E.; Kristiansen, R. Collision and terrain avoidance for UAVs using the potential field method. In Proceedings of the 2013 IEEE Aerospace Conference, Big Sky, MT, USA, 2–9 March 2013; pp. 1–7. [Google Scholar]
  77. He, W.; Yin, Z.; Sun, C. Adaptive Neural Network Control of a Marine Vessel with Constraints Using the Asymmetric Barrier Lyapunov Function. IEEE Trans. Cybern. 2017, 47, 1641–1651. [Google Scholar] [CrossRef]
  78. Liang, X.; Qu, X.; Wan, L.; Ma, Q. Three-Dimensional Path Following of an Underactuated AUV Based on Fuzzy Backstepping Sliding Mode Control. Int. J. Fuzzy Syst. 2018, 20, 640–649. [Google Scholar] [CrossRef]
  79. Mu, D.; Wang, G.; Fan, Y.; Bai, Y.; Zhao, Y. Fuzzy-Based Optimal Adaptive Line-of-Sight Path Following for Underactuated Unmanned Surface Vehicle with Uncertainties and Time-Varying Disturbances. Math. Probl. Eng. 2018, 2018, 7512606. [Google Scholar] [CrossRef]
  80. Wang, N.; Er, M.J. Self-Constructing Adaptive Robust Fuzzy Neural Tracking Control of Surface Vehicles with Uncertainties and Unknown Disturbances. IEEE Trans. Control Syst. Technol. 2015, 23, 991–1002. [Google Scholar] [CrossRef]
  81. Peng, Z.; Wang, J.; Wang, D. Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation. IEEE Trans. Control Syst. Technol. 2018, 26, 1083–1090. [Google Scholar] [CrossRef]
  82. Cheng, Y.; Zhang, W. Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels. Neurocomputing 2018, 272, 63–73. [Google Scholar] [CrossRef]
  83. Sui, Z.; Pu, Z.; Yi, J.; Wu, S. Formation Control with Collision Avoidance Through Deep Reinforcement Learning Using Model-Guided Demonstration. IEEE Trans. Neural Netw. Learn. Syst. 2021, 32, 2358–2372. [Google Scholar] [CrossRef] [PubMed]
  84. Wang, S.; Ma, F.; Yan, X.; Wu, P.; Liu, Y. Adaptive and extendable control of unmanned surface vehicle formations using distributed deep reinforcement learning. Appl. Ocean Res. 2021, 110, 102590. [Google Scholar] [CrossRef]
  85. Xie, J.; Zhou, R.; Liu, Y.; Luo, J.; Xie, S.; Peng, Y.; Pu, H. Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles. Appl. Sci. 2021, 11, 546. [Google Scholar] [CrossRef]
  86. Chen, H.; Yan, H.; Wang, Y.; Xie, S.; Zhang, D. Reinforcement learning-based close formation control for underactuated surface vehicle with prescribed performance and time-varying state constraints. Ocean Eng. 2022, 256, 111361. [Google Scholar] [CrossRef]
  87. Wu, C.; Zhao, M.; Min, C.; Wang, Y.; Luo, J. Adaptive neural formation control of autonomous underactuated surface vessels based on disturbance observer with leader–follower strategy. Trans. Inst. Meas. Control 2021, 43, 2972–2982. [Google Scholar] [CrossRef]
  88. Ghommam, J.; Saad, M.; Mnif, F.; Zhu, Q.M. Guaranteed Performance Design for Formation Tracking and Collision Avoidance of Multiple USVs With Disturbances and Unmodeled Dynamics. IEEE Syst. J. 2021, 15, 4346–4357. [Google Scholar] [CrossRef]
  89. He, S.; Wang, M.; Dai, S.L.; Luo, F. Leader–Follower Formation Control of USVs With Prescribed Performance and Collision Avoidance. IEEE Trans. Ind. Inform. 2019, 15, 572–581. [Google Scholar] [CrossRef]
  90. Chen, G.; Yao, D.; Zhou, Q.; Li, H.; Lu, R. Distributed Event-Triggered Formation Control of USVs with Prescribed Performance. J. Syst. Sci. Complex. 2022, 35, 820–838. [Google Scholar] [CrossRef]
  91. Dai, S.L.; He, S.; Lin, H.; Wang, C. Platoon Formation Control with Prescribed Performance Guarantees for USVs. IEEE Trans. Ind. Electron. 2018, 65, 4237–4246. [Google Scholar] [CrossRef]
  92. Huang, C.; Zhang, X.; Zhang, G. Improved decentralized finite-time formation control of underactuated USVs via a novel disturbance observer. Ocean Eng. 2019, 174, 117–124. [Google Scholar] [CrossRef]
  93. Zhang, G.; Liu, S.; Huang, J.; Zhang, W. Dynamic Event-Triggered Path-Following Control of Underactuated Surface Vehicle with the Experiment Verification. IEEE Trans. Veh. Technol. 2022, 71, 10415–10425. [Google Scholar] [CrossRef]
  94. Xiang, X.; Yu, C.; Lapierre, L.; Zhang, J.; Zhang, Q. Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles. Int. J. Fuzzy Syst. 2018, 20, 572–586. [Google Scholar] [CrossRef]
  95. Zhou, W.; Wang, Y.; Ahn, C.K.; Cheng, J.; Chen, C. Adaptive Fuzzy Backstepping-Based Formation Control of Unmanned Surface Vehicles with Unknown Model Nonlinearity and Actuator Saturation. IEEE Trans. Veh. Technol. 2020, 69, 14749–14764. [Google Scholar] [CrossRef]
  96. Wu, T.; Xue, K.; Wang, P. Leader-follower formation control of USVs using APF-based adaptive fuzzy logic nonsingular terminal sliding mode control method. J. Mech. Sci. Technol. 2022, 36, 2007–2018. [Google Scholar] [CrossRef]
  97. Liu, L.; Wang, D.; Peng, Z.; Chen, C.L.P.; Li, T. Bounded Neural Network Control for Target Tracking of Underactuated Autonomous Surface Vehicles in the Presence of Uncertain Target Dynamics. IEEE Trans. Neural Netw. Learn. Syst. 2019, 30, 1241–1249. [Google Scholar] [CrossRef]
  98. Zhang, S.; Xiang, X.; Yang, S. Cooperative control system of multi-ASV: Design and implementation. In Proceedings of the 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS), Wuhan, China, 1–3 December 2018; pp. 1–6. [Google Scholar]
  99. Liu, K.; Fridman, E.; Hetel, L. Networked Control Systems in the Presence of Scheduling Protocols and Communication Delays. SIAM J. Control Optim. 2015, 53, 1768–1788. [Google Scholar] [CrossRef]
  100. Gao, J.; Zhang, G.; Wu, P.; Zhao, X.; Wang, T.; Yan, W. Model Predictive Visual Servoing of Fully-Actuated Underwater Vehicles with a Sliding Mode Disturbance Observer. IEEE Access 2019, 7, 25516–25526. [Google Scholar] [CrossRef]
  101. Wang, N.; Sun, Z.; Yin, J.; Zou, Z.; Su, S.-F. Fuzzy unknown observer-based robust adaptive path following control of underactuated surface vehicles subject to multiple unknowns. Ocean Eng. 2019, 176, 57–64. [Google Scholar] [CrossRef]
  102. Zereik, E.; Bibuli, M.; Mišković, N.; Ridao, P.; Pascoal, A. Challenges and future trends in marine robotics. Annu. Rev. Control 2018, 46, 350–368. [Google Scholar] [CrossRef]
  103. Jiang, Y.; Peng, Z.; Wang, D.; Chen, C.L.P. Line-of-Sight Target Enclosing of an Underactuated Autonomous Surface Vehicle with Experiment Results. IEEE Trans. Ind. Inform. 2020, 16, 832–841. [Google Scholar] [CrossRef]
  104. Fan, J.; Li, Y.; Liao, Y.; Ma, T.; Ge, Y.; Wang, Z. A Formation Reconfiguration Method for Multiple Unmanned Surface Vehicles Executing Target Interception Missions. Appl. Ocean Res. 2020, 104, 102359. [Google Scholar] [CrossRef]
  105. Miao, R.; Wang, L.; Pang, S. Coordination of distributed unmanned surface vehicles via model-based reinforcement learning methods. Appl. Ocean Res. 2022, 122, 103106. [Google Scholar] [CrossRef]
  106. Wang, C.; Zhang, H.; Yu, Y. USV trajectory tracking control system based on ADRC. In Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China, 20–22 October 2017; pp. 7534–7538. [Google Scholar]
  107. Liu, L.; Wang, D.; Peng, Z. Path following of marine surface vehicles with dynamical uncertainty and time-varying ocean disturbances. Neurocomputing 2016, 173, 799–808. [Google Scholar] [CrossRef]
  108. Wang, N.; Gao, Y.; Sun, Z.; Zheng, Z. Nussbaum-Based Adaptive Fuzzy Tracking Control of Unmanned Surface Vehicles with Fully Unknown Dynamics and Complex Input Nonlinearities. Int. J. Fuzzy Syst. 2018, 20, 259–268. [Google Scholar] [CrossRef]
  109. Peng, Z.; Wang, D.; Li, T.; Wu, Z. Leaderless and leader-follower cooperative control of multiple marine surface vehicles with unknown dynamics. Nonlinear Dyn. 2013, 74, 95–106. [Google Scholar] [CrossRef]
  110. Qu, Y.; Xiao, B.; Fu, Z.; Yuan, D. Trajectory exponential tracking control of unmanned surface ships with external disturbance and system uncertainties. ISA Trans. 2018, 78, 47–55. [Google Scholar] [CrossRef]
  111. Li, R.; Huang, J.; Pan, X.; Hu, Q.; Huang, Z. Path following of underactuated surface ships based on model predictive control with neural network. Int. J. Adv. Robot. Syst. 2020, 17, 1729881420945956. [Google Scholar] [CrossRef]
  112. Weng, Y.; Wang, N.; Qin, H.; Karimi, H.R.; Qi, W. Data-Driven Adaptive Tracking Control of Unknown Autonomous Marine Vehicles. IEEE Access 2018, 6, 55723–55730. [Google Scholar] [CrossRef]
  113. Zheng, Z.; Sun, L. Path following control for marine surface vessel with uncertainties and input saturation. Neurocomputing 2016, 177, 158–167. [Google Scholar] [CrossRef]
  114. Liao, Y.; Jiang, Q.; Du, T.; Jiang, W. Redefined Output Model-Free Adaptive Control Method and Unmanned Surface Vehicle Heading Control. IEEE J. Ocean. Eng. 2020, 45, 714–723. [Google Scholar] [CrossRef]
  115. Xu, H.; Hassani, V.; Guedes Soares, C. Uncertainty analysis of the hydrodynamic coefficients estimation of a nonlinear manoeuvring model based on planar motion mechanism tests. Ocean Eng. 2019, 173, 450–459. [Google Scholar] [CrossRef]
  116. Shen, H.; Wen, G.; Lv, Y.; Zhou, J.; Wang, L. USV Parameter Estimation: Adaptive Unscented Kalman Filter-Based Approach. IEEE Trans. Ind. Inform. 2023, 19, 7751–7761. [Google Scholar] [CrossRef]
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Figure 1. Research framework of the current study.
Figure 1. Research framework of the current study.
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Figure 2. Publications and citations in the field of multi-USVs during 2004–2024.
Figure 2. Publications and citations in the field of multi-USVs during 2004–2024.
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Figure 3. Author collaboration network using analysis of co-authorship.
Figure 3. Author collaboration network using analysis of co-authorship.
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Figure 4. Organization collaboration network using analysis of co-authorship.
Figure 4. Organization collaboration network using analysis of co-authorship.
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Figure 5. Country collaboration network using analysis of co-authorship.
Figure 5. Country collaboration network using analysis of co-authorship.
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Figure 6. Citation map for papers cited more than 5 times.
Figure 6. Citation map for papers cited more than 5 times.
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Figure 7. Co-citation map for journals cited more than 20 times.
Figure 7. Co-citation map for journals cited more than 20 times.
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Figure 8. Keyword heatmap.
Figure 8. Keyword heatmap.
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Figure 9. Term heatmap.
Figure 9. Term heatmap.
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Table 1. Top 10 most influential authors in the field of multi-USVs.
Table 1. Top 10 most influential authors in the field of multi-USVs.
AuthorTPsTCsCPsOrganization
Zhouhua Peng1897654.22Dalian Maritime University
Dan Wang1584356.20Dalian Maritime University
Lu Liu1045245.20Dalian Maritime University
Jin Zou813917.38Harbin Engineering University
Nan Gu732145.86Dalian Maritime University
Yuanchang Liu729241.71University College London
Guoge Tan611519.17Harbin Engineering University
Yu Lu415939.75Shanghai Jiao Tong University
Linying Chen411729.25Delft University of Technology
Richard Bucknall314749.00University College London
Notes: TPs = total publications; TCs = total citations; CPs = citations per publication.
Table 2. Top 15 most influential organizations in the field of multi-USVs.
Table 2. Top 15 most influential organizations in the field of multi-USVs.
OrganizationTPsTCsCPsAPYCountry
Dalian Maritime University55160629.202020.69China
Harbin Engineering University2227312.412021.50China
Wuhan University of Technology1317913.772021.62China
Shanghai University12574.752020.67China
Shanghai Jiao Tong University917619.562021.44China
University College London729241.712019.86England
Ocean University of China7415.862022.43China
Delft University of Technology613923.172019.83Netherlands
University of Seville6244.002022.33Spain
Shanghai Maritime University6152.502022.83China
University of Lisbon48020.002021.25Portugal
Norwegian University of Science and Technology4133.252022.25Norway
Swinburne University of Technology311739.002022.67Australia
University of Zagreb3227.332019.33Croatia
Korea Institute of Ocean Science and Technology320.672023.67Korea
Notes: TPs = total publications; TCs = total citations; CPs = citations per publication; APY = average publication year.
Table 3. Top 10 most influential countries in the field of multi-USVs.
Table 3. Top 10 most influential countries in the field of multi-USVs.
CountryTPsTCsCPsAPY
China151244216.172021.40
USA1751030.002018.06
England1639624.752019.75
Spain11585.272020.55
South Korea10999.902020.40
Australia757281.712021.14
Netherlands714620.862020.14
Canada710114.432020.29
Portugal624440.672017.83
Italy55811.602017.80
Notes: TPs = total publications; TCs = total citations; CPs = citations per publication; APY = average publication year.
Table 4. Top 10 most cited papers in the field of multi-USVs.
Table 4. Top 10 most cited papers in the field of multi-USVs.
AuthorTitleYearTCsCYs
Peng et al. [37]Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics201340937.18
Kuwata et al. [38]Safe maritime autonomous navigation with COLREGS, using velocity obstacles201427427.40
Shojaei [39]Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque201513715.22
Peng et al. [40]Output-feedback flocking control of multiple autonomous surface vehicles based on data-driven adaptive extended state observers202111739.00
Gu et al. [41]Observer-based finite-time control for distributed path maneuvering of underactuated unmanned surface vehicles with collision avoidance and connectivity preservation202110635.33
Almeida et al. [42]Cooperative control of multiple surface vessels in the presence of ocean currents and parametric model uncertainty20101027.29
Lu et al. [43]Adaptive cooperative formation control of autonomous surface vessels with uncertain dynamics and external disturbances20189415.67
Peng et al. [44]Path-guided time-varying formation control with collision avoidance and connectivity preservation of under-actuated autonomous surface vehicles subject to unknown input gains20198917.80
Chen et al. [45]Distributed model predictive control for vessel train formations of cooperative multi-vessel systems20188614.33
Shojaei [46]Observer-based neural adaptive formation control of autonomous surface vessels with limited torque2016759.38
Notes: TCs = total citations; CYs = citations per year.
Table 5. Top 10 most cited journals in the field of multi-USVs.
Table 5. Top 10 most cited journals in the field of multi-USVs.
SourceTPsTCsCPs2023 IF
Ocean Engineering4291621.814.6
Journal of Marine Science and Engineering151026.802.7
IEEE Access916117.893.4
Applied Ocean Research612020.004.3
IEEE Transactions on Intelligent Transportation Systems57715.407.9
Electronics5224.402.6
IEEE Transactions on Systems Man Cybernetics-Systems415739.258.6
IEEE Internet of Things Journal45112.758.2
Applied Sciences-Basel451.252.5
Neurocomputing312943.005.5
Notes: TPs = total publications; TCs = total citations; CPs = citations per publication; IF = impact factor.
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Xue, J.; Song, Y.; Hu, H. Formation Control of a Multi-Unmanned Surface Vessel System: A Bibliometric Analysis. J. Mar. Sci. Eng. 2024, 12, 1484. https://doi.org/10.3390/jmse12091484

AMA Style

Xue J, Song Y, Hu H. Formation Control of a Multi-Unmanned Surface Vessel System: A Bibliometric Analysis. Journal of Marine Science and Engineering. 2024; 12(9):1484. https://doi.org/10.3390/jmse12091484

Chicago/Turabian Style

Xue, Jie, Yuanming Song, and Hao Hu. 2024. "Formation Control of a Multi-Unmanned Surface Vessel System: A Bibliometric Analysis" Journal of Marine Science and Engineering 12, no. 9: 1484. https://doi.org/10.3390/jmse12091484

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