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In multi-vessel collision avoidance decision-making, the collision between merchant and fishing vessels is a significant challenge. This paper proposes a fuzzy fusion method for making avoidance decisions under the influence of the navigation environment. First, C-means clustering was used to collect and analyze Automatic Identification System (AIS) data from fishing vessels. On this basis, the environment collision risk was determined using fuzzy reasoning. Second, the basic collision risk is obtained by calculating the DCPA and TCPA, and the integrated Collision Risk Index (CRI) is concluded by fuzzy logic through basic collision risk and the environment collision risk. The similar cases are extracted from the fuzzy case database, and collision avoidance decisions for merchant vessels are formulated following fuzzy adjustments. Finally, to validate the method, data from Chengshantou coastal waters is employed for verification. The results show that it can provide theoretical guidance and practical value for merchant vessels in making collision avoidance decisions. [ABSTRACT FROM AUTHOR]

In this research, we focus on developing an autonomous system for multiship collision avoidance. The proposed approach combines global path planning based on deep reinforcement learning (DRL) and local motion control to improve computational efficiency and alleviate the sensitivity to heading angle changes. To achieve this, firstly, DRL is used to learn a policy that maps observable states of target ships to a sequence of predicted waypoints. This learning task aims to generate a specific trajectory while avoiding collision with target ships complying with the international regulations for preventing collisions at sea (COLREGs). The learned policy is used as a global path planner during navigation. Secondly, the line-of-sight (LOS) guidance system is applied to calculate the desired course command based on the collision-free trajectory generated according to the policy. Lastly, a model-based control strategy is implemented to control the ship to the specific goal in collision-free space while satisfying the desired commands. We demonstrate the performance of the approach using an example of an autonomous surface vehicle. In comparison to other methods, our proposed control can provide a more stable and smoother maneuvering effect.

In multi-vessel collision avoidance decision-making, the collision between merchant and fishing vessels is a significant challenge. This paper proposes a fuzzy fusion method for making avoidance decisions under the influence of the navigation environment. First, C-means clustering was used to collect and analyze Automatic Identification System (AIS) data from fishing vessels. On this basis, the environment collision risk was determined using fuzzy reasoning. Second, the basic collision risk is obtained by calculating the DCPA and TCPA, and the integrated Collision Risk Index (CRI) is concluded by fuzzy logic through basic collision risk and the environment collision risk. The similar cases are extracted from the fuzzy case database, and collision avoidance decisions for merchant vessels are formulated following fuzzy adjustments. Finally, to validate the method, data from Chengshantou coastal waters is employed for verification. The results show that it can provide theoretical guidance and practical value for merchant vessels in making collision avoidance decisions.

The increasing traffic and complexity of navigation at sea require advanced decision support systems to ensure greater safety. In this study, we propose a novel decision support system that employs fuzzy logic to improve situational awareness and to assist navigators in collision avoidance during multi-vessel encounters. The system is based on the integration of the rules of the Convention on International Regulations for Preventing Collisions at Sea (COLREGs) and artificial intelligence techniques. The proposed decision model consists of two main modules to calculate the initial encounter conditions for the target vessels, evaluate the collision risk and navigation situation based on COLREG rules, sort the target vessels, and determine the most dangerous vessel. Fuzzy logic is used to calculate the collision avoidance maneuver for the selected ship, considering the closest point of approach, relative bearing, and the ship's own speed. Simulation tests demonstrate the effectiveness of the fuzzy-based decision model in scenarios with two ships. However, in complex situations with multiple ships, the performance of the model is affected by possible conflicts between evasive maneuvers. This highlights the need for a cooperative collision avoidance algorithm for all vessels in high traffic areas. [ABSTRACT FROM AUTHOR]

The increasing traffic and complexity of navigation at sea require advanced decision support systems to ensure greater safety. In this study, we propose a novel decision support system that employs fuzzy logic to improve situational awareness and to assist navigators in collision avoidance during multi-vessel encounters. The system is based on the integration of the rules of the Convention on International Regulations for Preventing Collisions at Sea (COLREGs) and artificial intelligence techniques. The proposed decision model consists of two main modules to calculate the initial encounter conditions for the target vessels, evaluate the collision risk and navigation situation based on COLREG rules, sort the target vessels, and determine the most dangerous vessel. Fuzzy logic is used to calculate the collision avoidance maneuver for the selected ship, considering the closest point of approach, relative bearing, and the ship’s own speed. Simulation tests demonstrate the effectiveness of the fuzzy-based decision model in scenarios with two ships. However, in complex situations with multiple ships, the performance of the model is affected by possible conflicts between evasive maneuvers. This highlights the need for a cooperative collision avoidance algorithm for all vessels in high traffic areas.

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships. [ABSTRACT FROM AUTHOR]

The congestion of waterways can easily lead to traffic hazards. Moreover, according to the data, the majority of sea collisions are caused by human error and the failure to comply with the Convention on the International Regulation for the preventing Collision at Sea (COLREGs). To avoid this situation, ship automatic collision avoidance has become one of the most important research issues in the field of marine engineering. In this study, an efficient method is proposed to solve multi-ship collision avoidance problems based on the multi-agent reinforcement learning (MARL) algorithm. Firstly, the COLREGs and ship maneuverability are considered for achieving multi-ship collision avoidance. Subsequently, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is utilized to detect and reduce the risk of collision. Ships can operate at the safe velocity computed by the ORCA algorithm to avoid collisions. Finally, the Nomoto three-degrees-of-freedom (3-DOF) model is used to simulate the maneuvers of ships. According to the above information and algorithms, this study designs and improves the state space, action space and reward function. For validating the effectiveness of the method, this study designs various simulation scenarios with thorough performance evaluations. The simulation results indicate that the proposed method is flexible and scalable in solving multi-ship collision avoidance, complying with COLREGs in various scenarios. [ABSTRACT FROM AUTHOR]

To ensure navigation safety, unmanned surface vehicles (USVs) need to have autonomous collision avoidance capability. A large number of studies on ship collision avoidance are available, and most of these papers assume that the target ships keep straight or follows the International Regulations for Preventing Collisions at Sea (COLREGS). However, in the actual navigation process, the target ship may temporarily turn. Based on the above reasons, this paper proposes a multi-ship collision avoidance decision method for USVs based on the improved velocity obstacle algorithm. In the basic dynamic ship domain model, a collision risk model is constructed to improve the accuracy of the risk assessment between the USV and target ships. The velocity obstacle algorithm is combined with the dynamic ship domain, and the collision avoidance timing and method are judged according to the collision risk. The simulation results show that the decision method can handle the situation that the target ship temporarily turns and has an emergency collision avoidance capability. Compared with the traditional VO algorithm, the collision avoidance time of the method is shorter, and the number of course changes is less. [ABSTRACT FROM AUTHOR]

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships.

The congestion of waterways can easily lead to traffic hazards. Moreover, according to the data, the majority of sea collisions are caused by human error and the failure to comply with the Convention on the International Regulation for the preventing Collision at Sea (COLREGs). To avoid this situation, ship automatic collision avoidance has become one of the most important research issues in the field of marine engineering. In this study, an efficient method is proposed to solve multi-ship collision avoidance problems based on the multi-agent reinforcement learning (MARL) algorithm. Firstly, the COLREGs and ship maneuverability are considered for achieving multi-ship collision avoidance. Subsequently, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is utilized to detect and reduce the risk of collision. Ships can operate at the safe velocity computed by the ORCA algorithm to avoid collisions. Finally, the Nomoto three-degrees-of-freedom (3-DOF) model is used to simulate the maneuvers of ships. According to the above information and algorithms, this study designs and improves the state space, action space and reward function. For validating the effectiveness of the method, this study designs various simulation scenarios with thorough performance evaluations. The simulation results indicate that the proposed method is flexible and scalable in solving multi-ship collision avoidance, complying with COLREGs in various scenarios.

Unmanned surface vessel (USV) has a wide range of applications in oceanographic research, resource development, environment detection, and security rescue due to its advantages of maneuverability, flexibility, fast response, and intelligence. The ability of USVs to autonomously and effectively avoid obstacles in highly dynamic and uncertain marine environments is a prerequisite for the successful completion of their tasks. Therefore, in this article, a USV collision avoidance based on International Regulations for Preventing Collisions at Sea and the Collision Risk Model with the Improved Differential Evolution Algorithm (CRI-DE) has been considered. Based on the International Regulations for Preventing Collisions at Sea (COLREGs) and common practices of seafarers, an improved ship collision risk model is proposed. Specifically, the model is innovatively combined with the differential evolution algorithm (DE) as a constraint condition to further realize path planning in complex situations. Moreover, chaotic multi-population parallel optimization, parameter adaptive adjustment strategy, and the construction of fitness function based on individual path points are added to the DE. In this way, the ability to escape from local optima and enrich population diversity can be guaranteed. Finally, experiments based on the proposed CRI-DE are conducted and the results indicate the efficiency and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To ensure navigation safety, unmanned surface vehicles (USVs) need to have autonomous collision avoidance capability. A large number of studies on ship collision avoidance are available, and most of these papers assume that the target ships keep straight or follows the International Regulations for Preventing Collisions at Sea (COLREGS). However, in the actual navigation process, the target ship may temporarily turn. Based on the above reasons, this paper proposes a multi-ship collision avoidance decision method for USVs based on the improved velocity obstacle algorithm. In the basic dynamic ship domain model, a collision risk model is constructed to improve the accuracy of the risk assessment between the USV and target ships. The velocity obstacle algorithm is combined with the dynamic ship domain, and the collision avoidance timing and method are judged according to the collision risk. The simulation results show that the decision method can handle the situation that the target ship temporarily turns and has an emergency collision avoidance capability. Compared with the traditional VO algorithm, the collision avoidance time of the method is shorter, and the number of course changes is less.

Maritime collision risk prediction is crucial for the safety management of ocean transportation. Previous studies have primarily focused on near-miss collision risk of ship pairs, yet the risk due to congestion caused by multiple ships is significant. This paper proposes a novel space-time geographical approach for addressing multi-ship near-miss collision risk based on vessel motion behavior. The direction-constrained space-time prism is used to characterize the interaction possibility of ships, enabling potential collision risk to be evaluated. The advantage of direction-constrained space-time prism in analyzing ship movements is that it accounts for direction limitation thereby eliminating unreasonable estimation associated with the assumption of arbitrary changes in sailing direction through the classical space-time prism. This paper uses the trajectories of ships traveling the southeast coast of China to investigate the viability of the proposed approach. In comparison to the fuzzy quaternion ship domain model and closest point of approach-based methods, the proposed approach is capable of identifying hierarchical near-miss collision risks for different ships to improve risk evaluation. This is essential for ship path optimization that accounts for changes in the speed and course of ships. This work supports maritime collision risk forecasting, but also provides detailed insights for actionable steps to reduce risks. [ABSTRACT FROM AUTHOR]

• Compared to other deep reinforcement learning algorithms, this research proposes intelligent decision-making system based on an improved Proximal Policy Optimization (PPO) algorithm that takes into full consideration the constraints of COLREGs, the ship domain and maneuvering characteristics. By designing a reward function, the method encourages ships to navigate and avoid collisions by adopting smaller rudder angles, while ensuring compliance with COLREGs. • With the introduction of Bi-Directional Gated Recurrent Unit (BiGRU) network and layer normalization techniques for processing algorithm input data, the convergence speed of the model trained by the proposed PPO algorithm is significantly improved. This study addresses the issue of multi-ship collision avoidance and the requirements for autonomous navigation of Marine Autonomous Surface Ships. To address this challenge, we propose a collision avoidance decision-making system for multi-ship encounters that integrates the Proximal Policy Optimization (PPO) algorithm with the ship domain while considering ship manoeuvring characteristics. Moreover, the reward function designed for the PPO algorithm can effectively ensure the trained collision avoidance decision-making model adheres to COLREGs and to prioritizes the utilization of small angle steering whenever possible. Additionally, a neural network based on Bidirectional Gated Circulation Unit (BiGRU) is established, incorporating Layer Normalization to handle high-dimensional observation data and enhancing the algorithm's efficiency. Through simulation experiments, the results demonstrate the flexibility and safety of the proposed decision-making method in multi-ship collision avoidance and surface obstacle avoidance scenarios. [ABSTRACT FROM AUTHOR]

Automatic collision avoidance from obstacles is important, but the conventional methods for ship collision avoidance emphasize how to design the feasible routes for collision avoidance basing on a ship's real time position, not to consider the dynamics or control for ship. . In this paper, an applicable multi-ship collision avoidance control method on the basis of the directions of relative velocity is presented, which applies to the routing plan of collision avoidance and ship motion control simultaneously. This method consists of three main steps. The first step is extracting/calculating basic motion and position parameters of the own ship and target ships. The second step is calculating suitable real time directions of velocity of the own ship basing on the method of velocity obstacle. The third step is controlling the variable transformation and algorithm design. This method is simple and flexible. It can be applied to the real time multi-ship collision avoidance, and is very feasible. Simulation results show that the algorithm has good effects. [ABSTRACT FROM AUTHOR]

A novel scheme is proposed for the distributed multi-ship collision avoidance (CA) problem with consideration of the autonomous, dynamic nature of the real circumstance. All the ships in the envisioned scenario cannot share their decisions or motivation, and they make decisions based on the limited observable information. Each ship is assumed to have a high-layer intention to guide the CA decision, which is called the collision avoidance logic (CAL). Each ship has its own CAL that governs the CA decisions and actions; meanwhile, each ship tries to understand the CALs of other ships by continuous inference and observation according to their extrinsic behaviours, especially the difference between the observed information and the predicted behaviour. This iterative scheme features a four-phase, programmed decision-making procedure, namely the observation-inference-prediction-decision (OIPD) model. Simulation results indicate that the OIPD method shows good flexibility and adaptability. When all ships comply COLREGs, the proposed scheme can produce normal solutions. In case some of the ships fail to make correct decisions, the other ships that adopt the proposed scheme can response in time and revise their CA strategies in a proactive way. Data Envelopment Analysis method is applied to further quantitatively evaluate the efficiency of the proposed scheme. • A human-like collision avoidance (CA) decision-making process is proposed based on a two-layer CA logical framework. • The CA logical framework contains a hidden CA logical layer and an observable and predictable maneuver layer. • The decision-making scheme contains observation , inference , prediction and decision phases in an iterative decision cycle. • A practical solution for multi-ship collision avoidance without assuming a coordination hub. • The proposed CA scheme does not make any assumptions about the COLREGs compliance and CA algorithms of other ships. • Data Envelopment Analysis method is applied to quantitatively evaluate the efficiency of the performance of the scheme. [ABSTRACT FROM AUTHOR]