The Smart AtoN(Aids to Navigation) project aims to upgrade the facilities of AtoN to provide new additional information to nearby ships and unmanned ships. This paper deals with methods for collecting various sensor data through standardized interfaces; the NMEA-0183(serial line), the NMEA-2000(CAN), and the NMEA OneNet(IPv6). The AIS(Automatic Identification System) and the AIS-ASM(Application Specific Message) are considered as communication means for providing sensor information to nearby ships. In this paper, we summarize existing NMEA sentences for collecting sensor data and AIS-ASM messages that can be used to deliver sensor data to ships. Information provided from the smart AtoN through AIS, ASM, etc. may be presented on the shipborne displays that complies with the IEC62288:2021 standard.
In order to solve the problem of improper thrust distribution of each thruster of underwater vehicle, the PSO optimization algorithm is used to solve the problem of thrust distribution. According to the spatial layout of the thruster, the algorithm model of the underwater vehicle propulsion system is established. The thrust input is carried out under the broken line search trajectory, and the simulation verifies the thrust allocation results of the PSO algorithm and the traditional pseudo-inverse method. The simulation results show that compared with the traditional algorithm. First of all, the PSO algorithm can set the physical threshold for each thruster to prevent the thruster from having too much thrust. Secondly, it can ensure that the thruster can turn with a reasonable torque to prevent the robot from drifting due to the large thrust gap. This paper provides a theoretical reference for thrust distribution of underwater salvage robot, and has practical engineering significance.
With the continuous development of science and technology, unmanned ship has gradually become a hot spot in the field of marine research. In practical applications, unmanned ships need to have long-range navigation and high efficiency, so that they can accurately perform tasks in the marine environment. As one of the key technologies of unmanned ship, path planning is of great significance to improve the endurance of unmanned ship. In order to meet the requirements, this paper proposes a path planning method for long distance unmanned ships based on reinforcement learning angle precedence ant colony improvement algorithm. Firstly, canny operator is used to automatically extract navigation environment information, and then MAKLINK graph theory is applied for environment modelling. Finally, the basic ant colony algorithm is improved and applied to the path planning of unmanned ship to generate an optimal path. The experimental results show that, compared with the traditional ant colony algorithm, the path planning method based on the improved ant colony algorithm can achieve a voyage duration of nearly 7 km for unmanned ships under the same sailing environment, which has certain practicability and popularization value.
Ship collision accidents not only endanger the safety of ships and personnel, but also may cause serious marine environmental pollution. To solve this problem, advanced technologies have been developed and applied in the field of intelligent ships in recent years. In this paper, a novel path planning algorithm is proposed based on particle swarm optimization (PSO) to construct a decision-making system for ship's autonomous collision avoidance using the process analysis which combines with the ship encounter situation and the decision-making method based on ship collision avoidance responsibility. This algorithm is designed to avoid both static and dynamic obstacles by judging the collision risk considering bad weather conditions by using BP neural network. When the two ships enter a certain distance, the optimal collision avoidance course and speed of the ship are obtained through the improved collision avoidance decision-making method. Finally, through MATLAB and Visual C++ platform simulations, the results show that the ship collision avoidance decision-making scheme can obtain reasonable optimal collision avoidance speed and course, which can ensure the safety of ship path planning and reduce energy consumption.
At present, the assessment for the crew training using the ship handling simulator is completed by the assessor, which is subjective and difficult to unify the assessment criteria. Under this assessment mode, the assessor will have a great work intensity. So it is necessary to design and develop the automatic assessment system for the ship handling simulator. This paper introduces the automatic assessment system developed by Dalian Maritime University (shorted for DMU), which includes the assessment method, system architecture and implementation. A selected example of applications is described.