## 3 The Results of the Bibliometric Analysis
### Keywords and Term Analysis
#### 3.4.1 Keywords Analysis  
Keywords usually contain valuable information regarding the intended focus of the author. The co-occurrence analysis of keywords is widely used to reveal the interconnection of keywords and to help readers gain insight into research hotspots and future trends [59]. In Figure 13, a visualization of the most significant keywords is presented, which included both the author's keywords and keywords plus.  
This visualization was based on the frequency of their shared occurrences in the documents under analysis. A total of 1734 keywords were identified by VOSviewer from the 402 retrieved articles. Among these, over 1500 terms appeared only once or twice, and 46 terms appeared more than five times, which is set as the criteria. A method of normalization was employed using association strength. The weights assigned to the keywords were calculated based on their frequencies, and the different clusters were distinguished by color-coding.  
The analysis focused on new technology in USV development, leading to the identification of keywords predominantly associated with this domain. Notably, emerging trends were discernible within the dataset. High-frequency keywords highlighted the keen interest of researchers in new concepts such as path-planning algorithms, marine navigation, the design of systems and collision avoidance. Additionally, related keywords, though less frequent, included e-Navigation, marine robots, machine learning and sensor fusion. This heightened focus on collision-avoidance could be attributed to the advancements in e-Navigation and the issue of COLREGs.  
Furthermore, the terms, i.e., control system, multi-agent systems, nonlinear-systems and tracking control indicated researchers' utilization of real-time or historical traffic data for developing novel systems grounded in AI. This shift could be linked to the progressive integration of AI in the shipping industry, as evidenced by relevant studies [60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70]. Additionally, the inclusion of environments, identification, remote sensing and object detection in the ranking suggested a growing inclination towards environmental perception in maritime decision-making process.  
Figure 13: Co-occurrence network of USV keywords.  
The research within the field of USVs has evolved significantly over the years. In 2017, the foundational aspects of simulation, autonomous navigation, and sensor technology formed the key areas of scholarly attention. Afterwards, the focus transitioned towards enhancing capabilities in monitoring, path planning and identification, laying the groundwork for more intricate system functionalities. By 2018, the emphasis shifted to the practical application of these technologies, with a heightened focus on collision avoidance, control systems, safety measures and environmental perception. The year 2019 marked a pivot towards the integration of sensor fusion and system identification, signaling a trend towards multifunctionality and the amalgamation of diverse technologies. The period from 2020 to 2022 witnessed a surge in AI-centered research, characterized by the incorporation of advanced AI algorithms, such as deep reinforcement learning, machine learning, and neural networks, and the utilization of satellite data and computer vision. These developments underscore a broader trend of enhancing communication and intelligent decision-making capabilities in USVs.