# Development of Automatic Berthing Support Program for Autonomous Ships
## 1 Introduction  
With the development of information and communication technology and shipbuilding technology, the automation and digital transformation of ships have been accelerated. The development of ship intelligence has become a key driving force in both navigation technology for autonomous ships and automatic berthing technology [1]. Ship control is still one of the important issues considering the automation and intelligence of ships. Moreover, automatic berthing has become an urgent issue within ship intelligence systems owing to its complexity and importance [2]. The ship berthing process involves close interaction between ships, waterways, and docks, as well as a series of complex operations and techniques [3, 4]. In this process, automated vessels use their own propulsion systems to achieve automatic berthing, whereas non-automated vessels rely on tugs for the necessary power and control [5, 6]. Intelligent vessel berthing technology has evolved from primarily focusing on automation to using a variety of vessel technologies and auxiliary equipmentto achieve automatic berthing. These include dynamic collision avoidance technology [7], which uses sensors such as light detection and ranging (LiDAR) and laser rangefinders to obtain the speed, course, and position of a ship and real-time monitoring of other ships and obstacles in the surrounding waters. Local route planning dynamically generates the optimal route to the target dock based on the real-time environment and the current location of the vessel [8]. Additionally, the coordination of detection technologies, path tracking, tug support, and dynamic positioning systems is highly important for automatic berthing in real-life scenarios, relying on the comprehensive application of these technologies [9]. Autonomous ships, utilizing artificial intelligence, the Internet of Things, big data, and heterogeneous sensor integration, can independently perceive and control their surroundings, thereby reducing maritime accidents and improving operational efficiency. This capability has attracted considerable interest for development both domestically and internationally. To enhance the autonomy of autonomous ships, it is essential to develop navigation systems capable of perceiving external conditions, making navigational decisions, and controlling the vessel [10]. Autonomous navigation systems may incorporate automatic berthing and unberthing technologies, allowing ships to dock or undock without assistance from tugs or pilots. In automatic berthing, a ship approaching a pier must recognize the pier and control its approach by accounting for the berthing speed and the influence of external forces. Research on automatic berthing includes creating models through neural network learning based on nonlinear ship maneuvering data during berthing and comparing these models with simulation results [11; 12; 13]. Supervised learning requires a diverse and extensive dataset to effectively train the model for optimal performance. In contrast, reinforcement learning does not require pre-prepared data; instead, the agent generates data by interacting with the environment. The agent observes the environmental state and selects appropriate actions accordingly. [PERSON] [14] proposed an automatic berthing system that builds an optimal control policy through trial-and-error interactions with a given environment, using the Proximal Policy Optimization (PPO) algorithm, a type of reinforcement learning, thus eliminating the need for training data. [PERSON] et al. [15] proposed a path-planning algorithm for automatic berthing based on deep reinforcement learning, utilizing maneuvering simulations. [PERSON] et al. [16] trained and evaluated control policy neural networks by combining supervised and reinforcement learning in a simulator designed for a specific harbor geometry under various wind and initial conditions. They found that the proposed method achieved higher success rates and reduced safety risks compared to naive supervised learning and reinforcement learning.