# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies
### Decision-Making Technology  
Each tugboat performs an individual mission, and when it arrives near a port, it requires a mission planning process, such as adjusting the thrust of each tugboat and selecting a formation suitable for the condition of the mothership berthing process. For example, if the mothership needs to change its heading angle or speed, a series of mission tasks must be properly planned. For instance, some of the tugboats need to adjust the heading angle direction of the mothership, and the other tugboats should generate longitudinal force by applying thrust to the vessel. Therefore, there is a need for a technology capable of automatically allocating these tasks to each tugboat when operating a swarm of fully autonomous tugboats. Task assignment is made in consideration of the characteristics of the platform itself, and reassignment is made according to environmental conditions and mission status. Currently, the Hungarian algorithm [31] is widely used for allocating tasks to multiple unmanned ships. Its computational complexity is large, and the amount of computation increases when the size of the swarm increases. Therefore, developing an algorithm that can efficiently calculate and cope with uncertainty is necessary. In addition, tugboats and motherships must form a swarm to create an optimal route avoiding static and dynamic obstacles, and there must be no collisions within the swarm. In [32], a fast marching technique was proposed for swarm navigation of multiple unmanned surface vehicles, as shown in Figure 12, and a simulation study was performed in which swarm USVs navigated to and from the swarm to the target point without any collision. In this study, a grid map was created based on the surrounding environment of clustered ships for the fast marching (FM) technique. The potential values of the grid map were defined using information such as the speed and attitude of each ship. When obstacle information for the clustered ships was identified, collision avoidance was performed by generating an avoidance route through the FM technique and cruising [32]. In this study, an arbitrary path was followed using six USVs, but this technology can be applied to a scenario where a group of unmanned survey vessels and motherships move toward the port in towing mode. Lastly, in studies related to ship berthing using autonomous tugboat swarms, control problems have been addressed by controlling the swarm of tugboats to follow the desired (reference) trajectory of the mothership. However, in complex and difficult berthing scenarios, path planning problems must be addressed, taking into account the spatial constraints of the swarm with respect to the shape of port. The A star search algorithm, which is a commonly applied method, is an optimal path generation algorithm that uses a road map called a \"visibility graph\" to create an optimal path even in a complex environment. When applied to a swarm of tugboats, it finds the optimal route, but the route is not smooth, causing sudden movements of the tugboats, which can make them difficult to control [33]. Figure 13 illustrates the concept of the visibility graph. In research [33], the autonomous swarm control of unmanned ships was verified through the development and study of path  
Figure 11: Noise reduction results for radar image proposed by [PERSON] et al. [43]: left: before reduction, right: after reduction. planning algorithms. The A star search algorithm was adjusted for unmanned ships, and an algorithm was developed to explore the path of unmanned ships using information about their dimensions, position, and direction. In addition, the shortest path of all points was calculated offline, improving the speed of the A star search algorithm by 12.3 times. In this study, the path planning system was verified using the Robot Operating System framework. In [34], a reactive path planning approach called Dubins APF (Dubins Artificial Potential Field) was proposed to solve the problem of autonomous robot path planning in environments with obstacles.