# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies
### Perception Technology  
When berthing a large ship using multiple tugboats, visibility and communication restrictions may occur. For example, when a tugboat is close to a large vessel, it may obstruct the crew's view, and if the position of the tugboat is in a blind spot due to a shadow zone created by the large vessel, it may be difficult to see potential hazards or obstacles. Additionally, factors such as background noise and equipment failure can limit communication, making it difficult to convey important information such as speed or direction changes. These visibility and communication challenges highlight the need for autonomous tugboats equipped with cognitive technologies. First, it is possible to obtain accurate location information between a pier, a ship, and each tugboat due to sensor information such as a camera, LiDAR, and radar, as well as the development of complex navigation technology. In addition, as sensor fusion technology and artificial intelligence technology develop, even if one of the sensors fails or a shadow area occurs, accurate information can be recognized by integrating and converging the information with the remaining sensor information. In [36], an algorithm was proposed that combines GPS information and measured distance information between nodes to determine relative position. Similarly, ref. [37] used INS (Inertial Navigation System), GPS (Global Positioning System), and NHC (Nonholonomic Constraint) to obtain accurate location information. While these studies were not specifically designed for the maritime domain, their approach can be applied to the berthing environment to identify accurate position through relative and absolute position information by designing a complex navigation system. In [38], the distance between the ship and the pier was accurately calculated using a measured phase difference of a low-frequency signal and the predefined distance between the reader and transponder of each antenna. In [39], when the ship docked, the motion state of the ship itself was estimated based on 3D LiDAR, and then the ship's speed and attitude were estimated. In addition, the GPS coordinates of the port shoreline were converted into the ship reference coordinate system to calculate the distance between the ship and the target berth, the transversal velocity, the longitudinal velocity and yaw rate, and [40]; to solve the problem of real-time pose estimation when berthing the ship, the motion process of the ship was monitored in real time according to the SLAM (Simulation Localization and Mapping) of a monocular camera. Both of these studies propose a recognition method based on a feature extraction and matching algorithm based on the SLAM algorithm. In addition, in terms of perception technology, research using AI technology has been actively conducted recently. When AI technology is applied, it can have advantages in terms of cost and performance compared with existing sensors, and it has the advantage of further enhancing situational awareness in terms of sensor fusion. The information below introduces perception technologies applied with artificial intelligence in studies within the last three years. Ref. [41] proposed a new artificial intelligence vision-based monitoring system for berthing ships (AVMS), as shown in Figure 10. Accurately estimating the relative distance between the wharf wall and the ship is necessary to anchor a ship, but the widely used berthing assistance system (BAS) is expensive and has limitations depending on the size of the ship. An AVMS sensor module is composed of a low-cost camera, DGPS (Differential Global Positioning System) receiver, and inertial measurement unit (IMU), and its performance was compared with the existing BAS. AVMS showed strong performance in poor weather conditions compared with the existing laser distance sensor-based BAS, and it can be applied directly in the actual port by providing real-time image information of ships approaching the quay wall.