## 2 Previous Works of Bibliometric Analysis and Literature Review
### Literature Reviews on Specific Topics Related to USV  
Several review papers focus on specific aspects associated with different tasks or components of the vessel's control architecture, such as path planning [6; 7; 8], path-following control systems [9], adaptive control [10], autonomous docking [11], deep learning in Maritime Autonomous Surface Ships (MASSs) [12], decision making in MASS operations [13], and the regulation of remotely controlled and autonomous commercial vessels [14]. Path planning is fundamental in the context of USVs and has been widely studied. Various review papers on path planning can be found in the literature. In [6], the authors provide a chronological overview of eight previous literature review papers related to path planning, which were published from 2006 to 2023. They also present a taxonomy of path planning, categorizing algorithms into global path planning and local path planning. This article also presents a state-of-the-art review of path-planning algorithms in chronological order, which were divided into four time intervals starting from the year 2000, specifying whether the implementation was in simulated conditions or validated in real-world scenarios. The work presented in [7] conducts a state-of-the-art review of global and local planning algorithms using the same taxonomy found in [6]. Additionally, it includes the state of the art of algorithms for proximity risk avoidance and cluster path planning. The authors state that their contribution lies mainly in including aspects related to complex maritime environmental conditions in their analysis. In [8], the authors present various aspects related to autonomous surface vehicles (ASVs). This article provides a context for ASVs, the conceptual elements associated with path planning, and a timeline of literature reviews on path planning and collision avoidance. Furthermore, sections are dedicated to the regulatory framework, the architecture of the guidance, navigation, and control (GNC) system, and the path planning algorithms classified into classical, advanced, and hybrid approaches. Path following is related to the component that various authors define as the central element for the autonomy of USVs and ASVs, which is known as the guidance, navigation, and control system [8; 9]. In the literature review presented in [9], the authors explain the trajectory-tracking problem, focusing on the vessel's guidance and control subsystems. They also present the state of the art of guidance laws and motion control systems. An important contribution of this article is the comparison of algorithms found in the literature based on the architecture structure, simulation or experimental results, guidance law used, consideration of environmental disturbances, type of controller, consideration of vessel dynamics, type of stability analysis, and consideration of degrees of freedom for the control problem. Among the many challenges associated with the development and operation of USVs, selecting an appropriate control strategy to respond to the complexities of the environment in real-world applications is crucial. Among the approaches found in the literature, ref. [10] presents a review of Model-Free Adaptive Control (MFAC). The article provides a context for MFAC, highlighting its characteristics and advantages over conventional control schemes. The authors describe and present the state of the art of three data-driven approaches, which use neural networks, reinforcement learning, and fuzzy logic. Autonomous docking is essential for the development of fully autonomous USVs. The developments and challenges of this specific aspect of USV operation are studied in [11]. The authors provide a description of the process and components of the architecture for autonomous docking. Additionally, the article presents a state-of-the-art review of existing methods for autonomous docking, considering aspects such as sensors, decision making, path planning and collision avoidance, and control.