## 4 Results
### Research Questions Results
#### 4.2.3 What Types of Algorithms Have Been the Subject of Research for Achieving USV Autonomy? In the process of labeling the papers according to the algorithm type, 22 subcategories were identified. However, some of them can be grouped considering the relationship between them in terms of the USV development stage, the type of task, or the component of the USV GNC system architecture. Table 14 presents the references related to GNC architecture systems, which are categorized by subsystem. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Application Area** & **Number of Papers** & **References** \\ \hline Environmental Monitoring & 30 & [12, 32, 35, 37, 41, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,  
The control subsystem emerged as the most prevalent research topic with 74 papers primarily focused on trajectory tracking. Guidance systems constituted the second category, encompassing 71 studies. Path-planning algorithms were the core focus of nearly half of these papers. While obstacle and collision avoidance was addressed in 32 papers, it is inherently linked to path planning. Trajectory planning, considering both spatial and temporal dimensions, was explored in a smaller subset of four papers. Navigation subsystem research comprised the third category with 69 studies predominantly focused on environment perception. The developed database also provides information about the methods used to address the corresponding tasks. This information was identified for 147 articles. Table 15 presents information on articles related to environment perception and obstacle detection. The remaining information is available in the database and is not included in this article due to its length and quantity. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Method/Algorithm** & **Reference** \\ \hline Waterline detection/Obstacle detection & Image segmentation & [49] \\ Wind speed and direction estimation & Neural networks–Perceptron & [124] \\ Estimation of meander parameters & Gaussian filters/Restricted interval & [125] \\ Kalman filter & & \\ Navigable waterway detection & & segmentation/Planar & [128] \\ Background segmentation and change & & \\ detection & Background subtraction & [130] \\ Coastline-water detection and & Line segment detection/coarse-to-fine & [133] \\ recognition & strategy & \\ \hline Obstacle detection & Sensor fusion/Weighted ELM binary & [134] \\ Multimodal perception for obstacle & classifier & \\ detection & CNN–YOLO V7 & [135] \\ LiDAR-based ambient detection & Sensor data fusion/Voxel filtering & [136] \\ Hallucinating hidden obstacles & Compositional model & [137] \\ Temporal context for obstacle detection & Temporal context extraction from image sequences for ambiguity reduction & [138] \\ Obstacle avoidance system & CNN–YOLO V4/Vector Field & [139] \\ Histogram (VFH) & & \\ Obstacle detection/Obstacle distance & Fuzzy Kohonen Network (FKN) & [140] \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \multicolumn{3}{c}{} \\ \end{tabular}
\end{table} 
Table 15: Methods implemented for environment perception and obstacle detection.