Automation of coordinate planning voyage cycle of an autonomous vessel

Abstract

The purpose of this work is the development of algorithms and calculation schemes for automating the planning of the coordinates of the path of an autonomous vessel in the voyage cycle. One of the main goals is development of methods of increasing the accuracy of planning the coordinates of the path and improving the methods of effective maneuvering for the organization of safe navigation along them. Traditionally, coordinate planning has been performed manually, which is a time-consuming process and often leads to errors or shortcomings. With automation, the OOW makes faster and more accurate decisions, which significantly reduce the risk of navigation and cyber accidents. Automation can also help the vessel optimize its route taking into account external and internal factors that affect its movement. In addition, it is necessary to take into account the probability of the appearance of man-made risks and accordingly promptly adjust your route in order to avoid dangerous emergency situations. It can also speed up route planning and optimize the route to reduce fuel consumption, prepare cargo for safe transportation in difficult sailing conditions and other factors, resulting in cost savings and improved environmental sustainability of the voyage cycle. Another key benefit of automation in coordination planning is that it can help a vessel operate more efficiently in congested areas such as ports or high-traffic shipping lanes. Autonomous vessels, equipped with advanced sensors of maneuvering parameters and using modern technologies of automation of production processes, which allow to control the vessel quickly in difficult external conditions more reliably than manual control by an operator. By automating this critical aspect of the voyage cycle, shipping companies can reduce costs, improve sustainability, and ensure the safe and efficient operation of their vessels. Artificial intelligence (AI) and machine learning (ML) can be used to analyze data from sensors and make decisions on behalf of the vessel. These algorithms can also be used to optimize a vessel's route based on a variety of factors. The Rapid Random Tree Search (RRT*) control parameters of maneuvering is used to control traffic along the planned route, without collisions for the ship It creates a list of possible safe paths and chooses the best one based on the analysis of parameters of distance to navigational obstacles and safe separation from other vessels. In conclusion, automation of coordinate planning in the voyage cycle of an autonomous vessel is an important and rapidly developing area of shipping technology. By incorporating advanced sensors, AI/ML algorithms, and other technologies, autonomous vessels can optimize their route planning, improve navigation safety and reduce costs. The use of efficient algorithms, such as rapid exploration of the System State Parameter Random Tree (RRT*) and defined space of their change, helps to organize safe and efficient management of the ship's operation, and communication with other ships and ports can provide valuable real-time data for optimization voyage. As this technology continues to develop, we can expect to see increasingly autonomous vessels that require minimal human intervention in the voyage cycle. This will not only improve efficiency and safety, but also lead to lower costs. Therefore, the automation of coordinate planning in the voyage cycle of an autonomous vessel and the organization of maneuvering along them is a decisive step towards fully autonomous shipping. It has the potential to revolutionize the shipping industry, allowing ships to operate more efficiently and safely while reducing the need for human intervention.