The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships

The International Regulations for Preventing Collisions at Sea (COLREGS) specify certain navigation rules for ships at risk for collision. Theoretically, the safety of unmanned surface vehicles and traffic boats would be guaranteed when they comply with the COLREGS. However, if traffic boats do not comply with the demands of the convention, thereby increasing the danger level, then adhering to the COLREGS may be dangerous for the unmanned surface vehicle. In this article, a dynamic obstacle avoidance algorithm for unmanned surface vehicles based on eccentric expansion was developed. This algorithm is used to solve the possible failure of collision avoidance when the unmanned surface vehicle invariably obeys the COLREGS during the avoidance process. An obstacle avoidance model based on the velocity obstacle method was established. Thereafter, an eccentric expansion operation on traffic boats was proposed to ensure a reasonable balance between safety and the rules of COLREGS. The expansion parameters were set according to the rules of COLREGS and the risk level of collision. Then, the collision avoidance parameters were calculated based on the aforementioned motion model. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under different situations. Results show the validity, reliability and intellectuality of the algorithm. This research can be used for intelligent collision avoidance of unmanned surface vehicle and other automatic driving ships.

With the increasingly fierce competition in the global shipbuilding industry, shipbuilding enterprises need to maintain competitiveness and cope with rapid changes. In this case, shipbuilding enterprises need to establish effective supply chain management. Among them, choosing the right supplier is one of the most critical activities. The supplier selection of shipbuilding enterprises is considered a complex multicriteria decision-making (MCDM) problem that attracts much attention due to intuitionistic fuzzy sets to deal with possible imprecision and fuzziness in real life. Based on this, this paper proposes a new method based on the intuitionistic fuzzy SWARA (stepwise weight assessment ratio analysis) and COPRAS (complex proportional assessment) method to select shipbuilding enterprise suppliers which is a new research area. First of all, different weights are given to each expert evaluation result according to their position, educational background, and working years. The supplier index’s weight is determined based on the intuitionistic fuzzy SWARA method, and it is easy to understand and operate. The ranking of suppliers is determined by the intuitionistic fuzzy COPRAS method. This method considers all kinds of uncertainties and evaluates the utility index and the cost index of alternative suppliers. Finally, taking a shipbuilding enterprise as an example, applying the intuitionistic fuzzy SWARA-COPRAS method is illustrated. Compared with other methods and sensitivity analysis, it shows that the intuitionistic fuzzy multicriteria decision-making method is effective and stable in shipbuilding enterprises.

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.

The unmanned surface vehicle has the characteristics of high maneuverability and flexibility. Object detection and tracking skills are required to improve the ability of unmanned surface vehicle to avoid collisions and detect targets on the surface of the water. Mean-shift algorithm is a classic target tracking algorithm, but it may fail when pixel interference and occlusion occur. This article proposes a tracking algorithm for unmanned surface vehicle based on an improved mean-shift optimization algorithm. The method uses the self-organizing feature map spatial topology to reduce the interference of the background pixels on the target object and predicts the center position of the object when the target is heavily occluded according to the extended Kalman filter. First, a self-organizing feature map model is built to classify pixels in a rectangular frame and the background pixels are extracted. Then, the method optimizes the extended Kalman filter solution process to complete the prediction and correction of the target center position and introduces a similarity function to determine the target occlusion. Finally, numerical analyses based on a ship model sailing experiment are performed with the help of OpenCV library. The experimental results validated that the proposed method significantly reduces the cumulative error in the tracking process and effectively predicts the position of the target between continuous frames when temporary occlusion occurs. The research can be used for target detection and autonomous navigation of unmanned surface vehicle.