An Improved VO Method for Collision Avoidance of Ships in Open Sea

A novel ship collision risk evaluation algorithm based on the maximum interval of two ship domains and the violation degree of two ship domains

In this paper, a novel ship domain model termed quaternion ship domain (QSD) is proposed. Unlike other ship domains, the proposed QSD is more dependable and more flexible for navigators to use to make decisions. The main characteristics are that: the domain size is determined by the quaternion including four radii, i.e. fore, aft, starboard and port, which sufficiently take factors affecting the domain (i.e. ship manoeuvring capability, speeds and courses, etc.) into account; and that the domain shape is modelled by another parameter which makes the QSD more flexible since the ship boundary could not only be linear or nonlinear, but also be thin or fat. In order to reasonably relate the proposed QSD to practical applications, i.e. collision risk assessment, collision avoidance and trajectory planning, etc., a fuzzy QSD (FQSD) has been developed by using fuzzy sets. As a result, fuzzy boundaries rather than crisp ones in the FQSD are more practical and more convenient for navigators to understand and judge since uncertainty and fuzzy information have been merged into the FQSD. Furthermore, concepts of longitudinal and lateral risk based on the FQSD have been introduced to estimate the spatial collision risk (SCR) for the ships encountered. Finally, several computer simulations have been conducted on various encounter situations and comparative studies with other ship domains have been comprehensively analyzed. Simulation results demonstrate that the proposed QSD is more effective and more flexible than other ship domains, and that the intelligent SCR based on the FQSD are reasonable and dependable.

The narrow waterway has always been a water area with a high incidence of ship collision accidents, and the research on the decision-making of collision avoidance system can improve the safety of ships sailing in the narrow waterway. The geometric scale of ship domain model is conservative, which is not very consistent with the actual collision avoidance of ships in narrow waterway. Therefore, via analyzing the existing research results in ship domain model in narrow waterway, a dynamic mathematical model of ship domain model in encounter situation is established with the "quaternary" ship field and the help of China's port design code, in narrow waterway. The MATLAB simulation platform is used to simulate the avoidance situation of two ships in the narrow waterway, and the feasibility of the model is verified.

The exploration of ship collision avoidance behavior characteristics can provide a theoretical basis for ship collision risk assessment and collision avoidance decision-making, which is significant for ensuring maritime navigation safety and the development of intelligent ships. In order to scientifically and effectively analyze the characteristics of ship collision-avoidance behavior and to seek the intrinsic connections among ship collision-avoidance behavior feature parameters(CABFPS), this study proposes a method that combines the Apriori algorithm and complex network theory to mine ship collision-avoidance behavior characteristics from massive AIS spatiotemporal data. Based on obtaining ship encounter samples and CABFPS from AIS data, the Apriori algorithm is used to mine the association rules of motion parameters, and the maximum mutual information coefficient is employed to represent the correlation between parameters. Complex networks of CABFPS for different encounter situations are constructed, and network topological indicators are analyzed. Mutual information theory is applied to identify key parameters affecting ship collision- avoidance behavior under different situations. The analysis using actual AIS data indicates that during navigation, the relationships among various parameters are closely linked and prone to mutual influence. The impact of CABFPS on ship collision-avoidance actions varies under different encounter scenarios, with relative distance and DCPA having the greatest influence on ship collision-avoidance actions. This method can comprehensively and accurately mine the correlations between CABFPS and the influence mechanism of parameters on collision-avoidance actions, providing a reference for intelligent ship navigation and the formulation of collision-avoidance decisions.

The combination of analytical and statistical method to define polygonal ship domain and reflect human experiences in estimating dangerous area

In this paper the author introduces a new method of collision avoidance for multi-target encounters. The method combines the qualities of a sequence of necessary manoeuvres with some optimisation elements and supports any given ship domain. First the problem of determining the optimal course alteration manoeuvre for given ship domain model and multi-target encounter situation is solved. The solution consists of analytical formulas and numerical algorithms combined to achieve low computational complexity. Then this solution is further used to construct a method of planning ship trajectories, which are safe, economic and intuitive for navigators. Example scenarios and the resulting trajectories are also provided. All of the algorithms, formulas and their derivations in the text are presented explicitly, so that they could be directly applied in any collision avoidance system.

With the development of intelligent waterborne transportation, mining collision avoidance patterns based on spatiotemporal and motion data of ships are crucial for the autonomous navigation of intelligent ships, which requires accurate collision avoidance information under various encounter scenarios. Addressing the existing issues of low precision and false detection in data mining algorithms, this paper proposes a collision avoidance behavior mining model considering encounter scenarios. The model is based on the Automatic Identification System (AIS) and the International Regulations for Preventing Collisions at Sea (COLREGs); it firstly identifies ship collision avoidance turning points by analyzing trajectory curvature with turning and recovering factors. Then, by combining AIS data and the specific navigational environment, it matches the ship encounter pairs and determines the encounter scenarios. Comparative experiments show that the model demonstrates superior accuracy in various scenarios compared to traditional algorithm. Finally, the model was applied to AIS data east of the Yangtze River Estuary, recognizing a total of 827 instances of ship collision avoidance behavior under different encounter scenarios. The case study shows that the model can precisely mine collision avoidance information, laying a solid foundation for future research on autonomous collision avoidance decision making for intelligent ships.

It is essential to investigate the path planning and tracking problem for maritime autonomous surface ships (MASS). This study proposed an automatic collision avoidance method based on the separation and recovery mechanism with formation of MASS (F-MASS). First, the marine environment model utilized S-57 electric navigation chart (ENC) data for construction. The Scan-searching method was proposed for multipath and path-optimized redistribution method was developed to address MASS congestion. The formation pattern transformation was settled by separation and recovery mechanism. Considered the ‘unmanned–manned’ encounter situation, formulated avoidance strategies for both the MASS formation and manned ships encounters. Finally, the simulation experiment was carried out in the sea near Zhoushan port to verify the effectiveness of the algorithm. The results indicated that the method can satisfy the integrated operation of collision avoidance in a complex environment and can offer effective collision avoidance strategies for F-MASSs navigating in complex marine environments.

In this paper, a new collision avoidance switching system is proposed to address the lack of adaptability of intelligent vehicles under different collision avoidance operating conditions. To ensure the rationality of the collision avoidance switching strategy for intelligent vehicles, the NGSIM road dataset is introduced to analyse the driver’s collision avoidance behaviour, and a two-layer fuzzy controller considering the overlap rate is established to design the collision avoidance switching strategy. In order to achieve real-time collision avoidance system activation, a lane change collision avoidance model based on MPC control is also developed. Finally, a simulation environment was created using Matlab/CarSim for simulation verification. The simulation results show that the collision avoidance switching system is more responsive and has a shorter start-up distance and is more adaptable to different driving conditions.

Autonomous navigation on the open sea involving automatic collision avoidance and route planning helps to ensure navigational safety. To judge whether all target ships (TSs) will pass safely and find the optimal route under multi-ship encounter situations, the relationship between the variations in the own ship (OS) velocity vector after nonlinear course altering motion and the collision avoidance result, which is defined as the collision avoidance mechanism, was analyzed. Methods producing the optimal route were also proposed. First, the static collision avoidance mechanism based on the ship domain and velocity obstacle (VO) was introduced. On that basis, the collision-free course alteration range of the OS, without consideration of the real manoeuvring process, was presented. Second, the ship motion equations and fuzzy adaptive proportion integral derivative (PID) control method were combined to develop a course control system. This system was then used to predict OS motions during the course-altering process. Based on this prediction, TS positions were calculated. Subsequently, the dynamic collision-free course altering ranges for the OS were obtained. Third, a model to compute the optimal route was introduced. Finally, simulations were performed under a situation including six TSs and two static objects, and the shortest collision-free route that satisfies both regulations for preventing collisions and good seamanship was found.

With vigorous development of the maritime trade, many intelligent algorithms have been proposed to avoid collisions due to resulting casualties and increased costs. According to the international regulations for preventing collisions at sea (COLREGs) and the self-evolution ability of the intelligent algorithm, the collision avoidance trajectory can be more consistent with the requirements of reality and maritime personnel. In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain under the condition of a wide sea area without any external disturbances. By balancing the safety and economy of ship collision avoidance, the avoidance angle and the time to the action point are used as the variables encoded by the algorithm, and the fuzzy ship domain is used to calculate the collision avoidance risk to achieve collision avoidance. The simulation results show that the proposed method can optimize the ship collision avoidance strategy and provide a reasonable scheme for ship navigation.

Although the navigation safety has been greatly improved nowadays, automation of ship driving are not achieved completely as the automation of collision avoidance has not achieved perfectly. This paper mainly research on ship domain modeling and application for ship collision avoidance. The novel achievements are that it proposed a 3-d model of ship domain and improved the danger judgments of collision to determine the more suitable timing and magnitude of action. This paper divided process of ship collision avoidance into three stages: ship domain build stage, danger judgment stage and action stage. Based on theories of each process of ship collision avoidance codified in 2-d coordinate system, this paper made simulations for crossing situation to express collision avoidance process.

COLREGs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs

In this article, a novel ship domain model termed intelligent quaternion ship domain (IQSD) has been proposed by effectively considering ship maneuverability and human factors. Unlike previous ship domains, the proposed IQSD is much more dependable and flexible for navigators to make decisions. The key characteristics are as followsthe domain size is determined by the quaternion including 4 radii, i.e., fore, aft, starboard, and port, which sufficiently take factors affecting the domain (i.e., ship maneuverability, speeds and courses, etc.) into account;the domain shape is modeled by another parameter k, which makes the IQSD more flexible because the ship boundary could be not only linear or nonlinear, but also thin or fat; 3) furthermore, the navigator states including skill ability and physical and mental states have been effectively modeled by a fuzzy system, which determines the shape parameter, k. To reasonably relate the IQSD to potential applications, i.e., collision risk assessment, collision avoidance and trajectory planning, etc., a generalized IQSD (GIQSD) with fuzzy boundaries has been developed by using fuzzy sets. As a consequence, the GIQSD would become more practical and convenient for applications because uncertainty and fuzzy information has been merged into the GIQSD. Furthermore, concepts of longitudinal and lateral risk based on the GIQSD have been defined to estimate the spatial collision risk (SCR) for ships encountered. Finally, comprehensive simulations have been conducted on various encounter situations and comparative studies with typical ship domains have been intensively analyzed. Simulation results demonstrate that the IQSD is more effective and flexible than previous ship domains, and the intelligent SCR based on the GIQSD is reasonable and dependable.

It is crucial to develop a COLREGs-compliant intelligent collision avoidance system for the safety of unmanned ships during navigation. This paper proposes a collision avoidance decision approach based on the deep reinforcement learning method. A modified collision avoidance framework is developed that takes into consideration the characteristics of different encounter scenarios. Hierarchical reward functions are established to assign reward values to constrain the behavior of the agent. The collision avoidance actions of the agent under different encounter situations are evaluated on the basis of the COLREGs to ensure ship safety and compliance during navigation. The deep Q network algorithm is introduced to train the proposed collision avoidance decision framework, while various simulation experiments are performed to validate the developed collision avoidance model. Results indicate that the proposed method can effectively perform tasks that help ships avoid collisions in different encounter scenarios. The proposed approach is a novel attempt for intelligent collision avoidance decisions of unmanned ships.