Automatic Collision Avoidance Research Articles

NEW TECHNOLOGY AND NEW APPLICATION OF AUTOMATIC COLLISION AVOIDANCE FOR SHIPS UNDER THE STRATEGY OF E-NAVIGATION

At the present stage, the world is vigorously developing the marine economy, and China has also attached greater importance to the implementation of the strategy of a strong maritime nation. In this context, e-navigation strategy has become an important direction of modern navigation. In order to enhance the combination of artificial intelligence, information technology and shipping, this paper explores the new technology and new application of automatic collision avoidance for ships under the e-navigation strategy, combines AIS technology, electronic charts and other technologies, and analyzes the application of new technology in collision avoidance for ships, in order to better guarantee the safety of ship navigation.

COLREGS-Complied Automatic Collision Avoidance for the Encounter Situations of Multiple Vessels

In crowded waters, the incidence of collision accidents of multiple vessels has increased significantly, most of which can be ascribed to human factors, particularly, human decision-making failures and inobservance with International Regulations for Preventing Collisions at Sea (COLREGS). On this basis, an automatic collision avoidance strategy for the encounter situations of multiple vessels is proposed herein. First of all, a COLREGS-complied evasive action decision-making mechanism is established, which can determine the evasive action from the give-way vessel and stand-on vessel separately. It is worth emphasizing that the situation of vessels against COLREGS is taken into consideration herein. Furthermore, a novel automatic collision avoidance controller of multiple vessels on account of model predictive control (MPC) is devised, which can carry out control action ahead of time and handle the problem of rudder saturation. Finally, the effectiveness of the proposed automatic collision avoidance strategy of multiple vessels is illustrated via extensive simulations.

COLREGs-Compliant Multi-Ship Collision Avoidance Based on Multi-Agent Reinforcement Learning Technique

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.

Toward Safer Navigation of Heterogeneous Mobile Robots in Distributed Scheme: A Novel Time-to-Collision-Based Method.

For safe and efficient navigation of heterogeneous multiple mobile robots (HMRs), it is essential to incorporate dynamics (mass and inertia) in motion control algorithms. Many methods rely only on kinematics or point-mass models, resulting in conservative results or occasionally failure. This is especially true for robots with different masses. In this article, we develop a novel navigation methodology for a distributed scheme by incorporating the robots' dynamics through calculating the time to collision (TTC) and designing a new controller accordingly that avoids collisions. We first propose a new predictive collision term by TTC that will be used to quantify imminent collisions among HMRs. Subsequently, using this term, we develop a novel nonlinear controller that explicitly incorporates TTC in the design and guarantees collision-free motion. Simulations and experiments were performed to demonstrate the effectiveness of the developed methods. We first compared the results of our proposed approach with controllers that only consider the robots' kinematics. It was shown that the proposed control strategy (a TTC-based controller) proves to be less conservative when determining safe motions. Specifically, for environments with limited space, it was demonstrated that using robots' kinematics may result in a collision, while our strategy results in safe motion. We also performed experiments that proved collision-free navigation of HMRs with this approach. The outcomes of this work provide more reliable motion control for HMRs, especially when the robots' masses or inertias are significantly different, for example, warehouses. The developments in this work are also applicable to vehicles and can therefore be beneficial in automated collision avoidance in autonomous driving and intelligent transportation.

Analysis of Automatic Collision Avoidance Strategy for Unmanned Surface Vessel

Automatic collision avoidance is a key technology in the research of unmanned surface vessel(USV). By establishing the collision avoidance decision coordinate system of USV, this paper estimates the motion parameters of obstacles relative to USV under two different conditions. Based on azimuth measurement information and based on azimuth and distance measurement information at the same time, analyzes the influence of speed and heading change on DCPA and TCPA, then, according to several typical sea encounters, the calculation methods of different collision avoidance decisions are given.

Decision-making method for multi-ship collision avoidance based on improved extensive game model

In the case of multi-ship encounter situations, most collision accidents are caused by not having timely and coordinated collision avoidance behaviors. Therefore, it is necessary to introduce an automatic collision avoidance method in the process of collision avoidance to quickly generate multi-ship collision avoidance decisions based on encounter situations. Multi-ship collision avoidance is a dynamic game used among officers under situations of multi-ship encounters. Considering the defects of the classic extensive game method in ship collision avoidance decision-making, the improved extensive game method is proposed, based on the velocity obstacle method. The feasible region of velocity is taken as the optional action range of the game to narrow down the action set. The simulation results show that the proposed method can effectively transform the multi-ship collision avoidance game problem into a 2-ship collision avoidance game problem, reduce the optional action range of a collision avoidance action set, and improve decision-making speed, while ensuring the safety of multi-ship navigation.

Intelligent Ship Collision Avoidance Algorithm Based on DDQN with Prioritized Experience Replay under COLREGs

Ship collisions often result in huge losses of life, cargo and ships, as well as serious pollution of the water environment. Meanwhile, it is estimated that between 75% and 86% of maritime accidents are related to human factors. Thus, it is necessary to enhance the intelligence of ships to partially or fully replace the traditional piloting mode and eventually achieve autonomous collision avoidance to reduce the influence of human factors. In this paper, we propose a multi-ship automatic collision avoidance method based on a double deep Q network (DDQN) with prioritized experience replay. Firstly, we vectorize the predicted hazardous areas as the observation states of the agent so that similar ship encounter scenarios can be clustered and the input dimension of the neural network can be fixed. The reward function is designed based on the International Regulations for Preventing Collision at Sea (COLREGs) and human experience. Different from the architecture of previous collision avoidance methods based on deep reinforcement learning (DRL), in this paper, the interaction between the agent and the environment occurs only in the collision avoidance decision-making phase, which greatly reduces the number of state transitions in the Markov decision process (MDP). The prioritized experience replay method is also used to make the model converge more quickly. Finally, 19 single-vessel collision avoidance scenarios were constructed based on the encounter situations classified by the COLREGs, which were arranged and combined as the training set for the agent. The effectiveness of the proposed method in close-quarters situation was verified using the Imazu problem. The simulation results show that the method can achieve multi-ship collision avoidance in crowded waters, and the decisions generated by this method conform to the COLREGs and are close to the level of human ship handling.

Investigation and Imitation of Human Captains' Maneuver Using Inverse Reinforcement Learning

Automatic collision avoidance is of significant importance to prevent maritime collisions. Although many studies have been conducted in recent years, autonomous system has not completely replaced human captains since it is still difficult to imitate their complicated decisions. Thus, the present paper tries to investigate and imitate experienced captains' maneuver using maximum entropy inverse reinforcement learning (MaxEnt IRL). We firstly verify that MaxEnt IRL can reproduce appropriate reward function from demonstrative trajectories. Afterwards, we conduct an experiment on a simulator where well-experienced captains maneuver in congested sea and estimate reward from the trajectories. Searching the route which maximizes the obtained reward, finally, we demonstrate the optimized route can avoid collision against multiple ships in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs).

Environment Potential Field Modeling for Ship Automatic Collision Avoidance in Restricted Waters

The accurate environment potential field (EPF) modeling method and highly efficient collision avoidance (CA) approach are key technologies for maritime autonomous surface ships (MASS). A novel and accurate environment potential field (EPF) model is proposed using electronic navigation chart (ENC) face objects to describe different types of navigable and non-navigable areas, and an improved artificial potential field (APF) method is presented to realize collaborative CA and obstacle avoidance (OA). Implicit equations of complex-shaped face objects were constructed based on R-function theory, and the discrete-convex hull method was introduced to realize automatic EPF modeling. Collaborative CA and OA experiments in restricted waters were conducted on a ship handling simulator. The results show that the improved APF method can obtain a robust and deterministic collision-free path under different weather conditions and in restricted waters, and the track zone width remains within 0.1 nm. The proposed face object EPF model is efficient and accurate, even with numerous vertices and complex shapes, and can drive the ship apart at a relatively safe distance in accordance with the recommended CA parameter. We present a practical CA approach and an effective EPF modeling method for APF-based ship path planning.

An Efficient Ship Automatic Collision Avoidance Method Based on Modified Artificial Potential Field

A novel collision avoidance (CA) algorithm was proposed based on the modified artificial potential field (APF) method, to construct a practical ship automatic CA system. Considering the constraints of both the International Regulations for Preventing Collisions at Sea (COLREGS) and the motion characteristics of the ship, the multi-ship CA algorithm was realized by modifying the repulsive force model in the APF method. Furthermore, the distance from the closest point of approach-time to the closest point of approach (DCPA-TCPA) criterion was selected as the unique adjustable parameter from the perspective of navigation practice. Collaborative CA experiments were designed and conducted to validate the proposed algorithm. The results of the experiments revealed that the actual DCPA and TCPA agree well with the parameter setup that keeps the ship at a safe distance from other ships in complex encountering situations. Consequently, the algorithm proposed in this study can achieve efficient automatic CA with minimal parameter settings. Moreover, the navigators can easily accept and comprehend the adjustable parameters, enabling the algorithm to satisfy the demand of the engineering applications.

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions.

The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel.

Path-following Control for Autonomous Navigation of Marine Vessels Considering Disturbances

Path-following control is considered as one of the most fundamental skills to realize autonomous navigation of marine vessels in the ocean. This study addresses with the path-following control for a ship in which there are environmental disturbances in the directions of the surge, sway, and yaw motions. The guiding principle and back-stepping method was utilized to solve the ship’s tracking problem on the reference path generated by a virtual ship. For path-following control, error dynamics is one of the most important skills, and it extends to the research fields of automatic collision avoidance and automatic berthing control. The algorithms for the guiding principles and error variables have been verified by numerical simulation. As a result, most error variables converged to zero values with the controller except for the yaw angle error. One of the most interesting results is that the tracking errors of path-following control between two ships are smaller than the existing safe passing distances considering interaction forces from near passing ships. Moreover, a trade-off between tracking performance and the ship’s safety should be considered for determining the proper control parameters to prevent the destructive failure of actuators such as propellers, fins, and rudders during the path-following of marine vessels.

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.

Failure behavior of multi-state system considering functional and physical isolation effects

The multi-state system (MSS) is a system that may exhibit multiple states or performance levels. Most existing studies assumed that the transition probabilities from states to states are known. However, in practical engineering, the complex stress conditions lead to great difficulty of collecting the statistical data of state transitions. In this paper, based on physics-of-failure theory, we consider different levels of damages caused by failure mechanisms (FMs) are the main reasons to components’ multiple states. Besides, the physical isolation (PI) effect on degradations of FMs is also studied, which is neglected in the existing studies about the MSS with functional dependence groups. Decision-diagram based methods are used for modeling the failure behavior of the MSS. An automatic collision avoidance system is analyzed for illustrating the proposed modeling and analyzing methods. The results show that comparing to the results without the consideration of PI effect, the probabilities of different states with PI effect of multi-state components and system may decrease or increase, which depends on the actual PI effects to the stress conditions.

AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres

AbstractEconomic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

Human Computer Interaction on the Modern Flight Deck

ABSTRACT There has been steady and significant growth and development of the technology available to pilots on the modern aviation flight deck. From improved information systems such as the Electronic Flight Bag (EFB), to increased control of the aircraft (e.g., automated collision avoidance maneuvering) and onwards to overall management of flights (via the Flight Management System, FMS), pilots now have a range of high-tech systems at their fingertips. This technology is designed to support pilots in maintaining situation awareness, making complex decisions, communicating effectively and assisting in flying the aircraft. However, each technological advancement comes with human computer interaction (HCI) considerations that warrant exploration. This special issue aims to bring together new knowledge and best practices, from the scientific community and industry, related to HCI issues on the modern flight deck. The goal of the special issue is to shed light on areas in need of improvement and potential new solutions for maintaining effective HCI on the flight deck. The articles within this special issue incorporate quantitative and qualitative research methods along with HCI design techniques to examine current and future use of technology on the flight deck and how it impacts pilot performance. Although this issue represents a small portion of the challenges pilots face when interacting with technology on the modern flight deck, it will hopefully serve as a lens to focus attention on the many HCI issues that pilots face, the progress that has been made to date, and the many areas that show potential for improvement.

Ship Path Optimization That Accounts for Geographical Traffic Characteristics to Increase Maritime Port Safety

Maritime ports face challenges associated with navigation safety, operational efficiency, and management. With the development of the Internet of Things, artificial intelligence simulation technologies, geographical information systems, and cloud computing technologies as well as navigation aids and decision support systems in maritime transportation, ports have the potential to better manage traffic, loading, and unloading. Recently, there has been growing attention in unmanned shipping to support the maritime industry and the military. This paper aims to extend the application of geographical theory and methodology in unmanned ship path optimization. Automatic collision avoidance concerning maneuvering capabilities of ships as well as complying with maritime traffic rules remains a challenge. This study attempts to tackle development needs associated with path optimization in maritime travel. By integrating ship movement behavior, geographical features, and the International Regulations for Avoiding Collisions at Sea, the proposed methods seek to reduce the human error associated with maritime accidents. This paper proposes economic efficiency and safety-driven unmanned ship path planning that will promote the future growth of intelligent port development.

Implications of mmWave Radiation on Human Health: State of the Art Threshold Levels

millimeter (mmWave) frequencies are covering from 30GHz to 300GHz in the electromagnetic spectrum and their uses in various applications like next-generation wireless communication systems (massive 5G telecommunications network), medical devices, airport security and automatic collision avoidance systems are growing vastly in the near future. Therefore, it is important to study the effects of mmWave radiation (non-ionization radiation) on biological systems and biophysical mechanisms. This paper focus on thorough review of nascent literature about current understandings of biological effects and epidemiological studies due to mmWave radiation in human beings. It presents latest guidelines with quantitative electromagnetic field thresholds by considering the realistic exposure scenarios of “general public” and “occupational” who undergo through wireless communication sources in their daily life. It also gives necessary safety measures to be taken while using the emerging mmWave technologies for future generation wireless communication networks.

An improved AHP based fuzzy evaluation model for ship collision risk

In the research of ship collision avoidance and automatic collision avoidance decision-making system, collision risk level is especially important. To evaluate the risk of ship collision, the nature of the risk level of ship collision is analyzed. Based on the fuzzy comprehensive evaluation model based on improved AHP, the concepts of space collision risk and time collision risk are proposed, and the models for space collision risk, time collision risk and collision risk level are established at the same time. The model developed and perfected the concept of ship collision risk, and are more logical, laid a solid foundation for subsequent comprehensive evaluation.

A ship collision avoidance system for human-machine cooperation during collision avoidance

Maritime Autonomous Surface Ships (MASS) attract increasing attention in recent years. Researchers aim at developing fully autonomous systems that replace the role of human operators. Studies either focus on supporting conflict/collision detection (for manned ships) or solving conflict automatically (for unmanned ships). The cooperation between human and machine has been less focused on in existing studies. However, this type of cooperation is essential both in practice and in the future: firstly, demands on navigational assistance are still strong for supporting navigators in manned ships; secondly, MASS with different autonomy levels require increasing cooperation between human operators and machines, e.g. monitoring automation, remotely controlling the ship, etc.; thirdly, the intelligence of human and the machines is highly complementary. Moreover, fully autonomous ships cannot replace all the manual ships overnight. Therefore, the future waterborne transport system will be a system in which both human-operated vessels and autonomous vessels exist.In this article, we firstly provide an overview of existing modes of human-machine interaction (HMI) during ship collision avoidance. Then, we propose a framework of HMI oriented Collision Avoidance System (HMI-CAS) whose decision-making process is interpretable and interactive for human operators. The HMI-CAS facilitates automatic collision avoidance and enables the human operators to take over the control of the MASS safely. Moreover, the proposed framework acknowledges the under-actuated feature of ships. Simulations are carried out to demonstrate the proposed HMI-CAS. The results show that the proposed HMI-CAS can not only control the under-actuated MASS to avoid collision automatically but also share the decision-making with human operators and support the operators to control the MASS.