基于多智能体深度强化学习的船舶协同避碰策略

船舶避碰是智能航行中首要解决的问题,多船会遇局面下,只有相互协作,共同规划避碰策略,才能有效降低碰撞风险.为使船舶智能避碰策略具有协同性、安全性和实用性,提出一种基于多智能体深度强化学习的船舶协同避碰决策方法.首先,研究船舶会遇局面辨识方法,设计满足《国际海上避碰规则》的多船避碰策略.其次,研究多船舶智能体合作方式,构建多船舶智能体协同避碰决策模型:利用注意力推理方法提取有助于避碰决策的关键数据;设计记忆驱动的经验学习方法,有效积累交互经验;引入噪音网络和多头注意力机制,增强船舶智能体决策探索能力.最后,分别在实验地图与真实海图上,对多船会遇场景进行仿真实验.结果表明,在协同性和安全性方面,相较于多个对比方法,所提出的避碰策略均能获得具有竞争力的结果,且满足实用性要求,从而为提高船舶智能航行水平和保障航行安全提供一种新的解决方案.     

粒子群算法在多船避碰决策中的应用

多船避碰是船舶避碰中最复杂的问题,也是船舶自动避碰方法研究中的难点之一.对许多学者的多船避碰研究进行分析,将最近会遇距离DCPA、最近会遇时间TCPA、两船距离、相对距离、本船转向角等作为基本评判参数,利用雷达进行一系列的观测,获得避让要素,建立碰撞危险度的评价模型.应用粒子群算法找出最优的解决方案,得出最优转向避碰幅度解.该方法不仅有助于解决多船会遇情况下的本船最优转向角度值,而且也有助于多船避碰决策系统的智能化设计与开发.     

改进双延迟深度确定性策略梯度的多船协调避碰决策

目前，多数海上避碰模型都是将船舶作为单智能体进行避碰决策，未考虑船舶间的协调避让，在多船会遇场景下仅靠单船进行避碰操作会导致避让效果不佳。为此，提出了一种改进双延迟深度确定性策略梯度算法(TD3)的Softmax深层双确定性策略梯度(SD3)多船协调避碰模型。从考虑船舶航行安全的时空因素出发构建时间碰撞模型、空间碰撞模型，对船舶碰撞风险进行定量分析，在此基础上采用根据会遇态势和船速矢量动态变化的船域模型对船舶碰撞风险进行定性分析。综合船舶目标导向、航向角改变、航向保持、碰撞风险和《国际海上避碰规则》(COLREGS)的约束设计奖励函数，结合COLREGS中的典型相遇情况构造对遇、追越和交叉相遇多局面共存的会遇场景进行避碰模拟仿真。消融实验显示softmax运算符提升了SD3算法的性能，使其在船舶协调避碰中拥有更好的决策效果，并与其他强化学习算法进行学习效率和学习效果的比较。实验结果表明，SD3算法在多局面共存的复杂场景下能高效做出准确的避碰决策，并且性能优于其他强化学习算法。     

船舶避碰转向幅度角问题的研究

针对船舶避碰决策系统中的船舶运动趋势和避碰时机,根据各目标船相对于本船在下一时刻的运动参数,本文应用遗传算法、模拟退火算法和穷举法实时预测避碰最优角。仿真结果表明,遗传算法对于求解船舶转向避碰幅度具有较好的实时性和有效性,能有效提高船舶海上航行的灵活性和安全可靠性,达到提前预警和帮助驾驶员提前做好避碰准备与应急措施等目的。     

浅谈信息融合技术在典型船舶避碰系统中的应用

首先介绍了信息融合技术的定义、基本原理以及与经典信号处理方法的本质差别,然后列举了目前在船舶避碰应用的典型数据融合技术的3个实例,讨论了信息融合的4个层次:数据层融合、特征层状态信息融合、特征层目标特性融合和决策层融合,关键技术与难点:多种传感器的协调管理,优化合成、图像配准、多船避碰系统控制模型建立、船舶碰撞危险度确定和避碰效果检验.依据关键技术与难点指出了可能的几种研究思路,最后介绍了船舶避碰系统中信息融合的发展动态.     

船舶拟人智能避碰决策理论的集成机器学习策略

简述了研究船舶拟人智能避碰决策(PIDVCA)的意义、目标及其实现方法,提出了基于机器学习构建动态避碰知识库的关键技术及PIDVCA理论的机器学习机制,并结合避碰仿真实例,着重讨论了PIDVCA理论的集成机器学习策略以及获取动态避碰知识的机理.     

基于范例推理的船舶避碰决策支持系统

根据船舶避碰的特点，将范例推理（Case-based Reasoning，CBR）方法引入到船舶避碰决策支持系统的设计中．为提高决策系统的性能，优化范例表示方法；并在分析阶段先对碰撞局面进行分类；再通过搜索树进行范例匹配；最后，讨论该方法的可行性．     

船舶避碰智能决策支持系统研究

建立了一个基于RBR(Case-Based Reasoning,基于案例推理)和CBR(Rule-Based Reasoning,基于规则推理)结合的船舶避碰决策支持模型,将这个模型引入到船舶避碰智能决策支持系统IDSSVCA(Intelligent Decision Support System for Vessel...     

一种新型船舶避碰决策支持系统的设计与实现

该文介绍了一种新型的船舶避碰决策支持系统——基于Multi-Agent的协商避碰决策支持系统的设计与实现。该系统是一个开放的、智能化的系统,它基于系统论的思想,把协商决策引入船舶避碰,是一个包括避碰决策Agent、协商决策Agent、网络通信Agent和法律仲裁Agent等的Multi-Agent系统,该系统可以最大限度地减少避碰决策中的不确定性,具有良好的用户界面,能提供网络环境下智能化的决策支持,可以更好地解决船舶避碰问题。     

雷达导航避碰模糊识别

在多目标雷达导航避碰过程中,识别目标船相 对于本船可能发生碰撞的危险程度是驾驶员十分重要的工作,通常是由驾驶员根据经验作出 人为的识别或判断,本文运用模糊数学综合评判理论于船舶雷达导航避碰技术中,综合分析目 标相对于本船的方位,距离,最近碰撞点和最近碰撞时间参数,将目标碰撞危险程度这一模糊 概念加以数量化,最终提出一种新的目标碰撞危险度综合评判数学模型.     

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.     

Collision avoidance strategy optimization based on danger immune algorithm

Marine collision accidents cause a great loss of lives and property. As a possible solution, the danger immune algorithm is used to achieve ship collision avoidance strategy optimization, which is a multi-objective problem concerning safety and economy. Collision avoidance operations are encoded as the individuals of optimization algorithm. In the system, ship domain and ship arena, among others, are used for collision risk evaluation to assess the fitness of individuals. Through the optimization, the navigator will obtain the optimal collision avoidance strategy to achieve safe and efficient collision avoidance. The simulations indicate that the optimization algorithm is valid.     

Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance (MVCA) algorithm is proposed by extending the reciprocal ${ n}$-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently, without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore, MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay (${ < 100}$ ms) and low packet loss (${ < 5\%}$) can bring little influence to those trajectory planning algorithms that only depend on V2V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.      

Autonomous maritime collision avoidance: Field verification of autonomous surface vehicle behavior in challenging scenarios

We present results from sea trials for an autonomous surface vehicle (ASV) equipped with a collision avoidance system based on model predictive control (MPC). The sea trials were performed in the North Sea as part of an ASV Challenge posed by Deltares through a Dutch initiative involving different authorities, including the Ministry of Infrastructure and Water Management, the Netherlands Coastguard, and the Royal Netherlands Navy. To allow an ASV to operate in a maritime environment governed by the International Regulations for Preventing Collisions at Sea (COLREGs), the ASV must be capable of complying with COLREGs. Therefore, the sea trials focused on verifying COLREGs‐compliant behavior of the ASV in different challenging scenarios using automatic identification system (AIS) data from other vessels. The scenarios cover situations where some obstacle vessels obey COLREGs and emergency situations where some obstacles make decisions that increase the risk of collision. The MPC‐based collision avoidance method evaluates a combined predicted collision and COLREGs‐compliance risk associated with each obstacle and chooses the ‘best’ way out of dangerous situations. The results from the verification exercise in the North Sea show that the MPC approach is capable of finding safe solutions in challenging situations, and in most cases demonstrates behaviors that are close to the expectations of an experienced mariner. According to Deltares’ report, the sea trials have shown in practice that the technical maturity of autonomous vessels is already more than expected.     

An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system

Due to brisk industrial growth, the marine traffic has become an imperative subject in the open sea nowadays. The crew inside the vehicle traffic service (VTS) centre is facing challenging issues on account of continuous growth in vessel number. Currently, most of VTS centers’ are using the ARPA RADAR based conventional vehicle traffic management system and VTS staff has to carry out most of the things manually to guide the ship’s captain properly. Therefore, there is a strong impetus in the field of ocean engineering to develop a smart system which can take the data from RADAR and autonomously manipulate it, to calculate the degree of collision risk among all vessels from the VTS centre. Later on, the traffic management officer utilizes this information for intelligent decision making. In the past, several researchers have addressed this issue to facilities the VTS crew and captain of the ship but mostly, their research work was for academic purposes and could not get popularity because of extra manual workload. Our proposed vessel collision risk assessment system is an intelligent solution which is based on fuzzy inference system and has the ability to solve the said issues. We calculated the DCPA, TCPA, bearing and VCD among all vessels ships from the VTS centre by using conventional marine equipments and exploited the extracted information to calculate and display the degree of collision risk among all vessels. Furthermore, we developed the RADAR filtration algorithm which helps the VTS officer to gauge out the degree of collision risk around a particular ship. To authenticate the validity and to monitor the performance efficiency, we developed RADAR operated intelligent software which directly gets the required data from RADAR and displays the vessels list based on their degree of collision severity. The laboratory experiments confirm the validity of the proposed system.     

Evolution of the safe path for ship navigation

Genetic algorithm is applied to solving the problem of collision avoidance in ship navigation. When utilizing this method, the first step is to code the solution of problems as a finite-length string. For the studied subject, in the conventional coding, the position (longitude and latitude) of the ship is chosen just as genes of a chromosome. This was not especially good for checking the experimental values. In this paper, a new way of coding is proposed, where a single gene is constructed with the position and speed of the ship, as well as the factors of tide, wind, and wave. Then it is verified with both an automatic collision avoidance simulation system developed in the VC++ language and a real ship. The experimental results show that this new coding is a more effective way to search for the optimum and safe path.     

基于FPGA和ARM的船舶防撞系统设计

研究设计了一种基于FPGA和ARM的船舶防碰撞告警设备。该设备主要应用在中小型船只上,与船上原有经济型航海雷达配合使用构成一个新系统。防碰撞告警设备从原航海雷达上采集雷达原始数据,并将数据进行变换和二次信息处理,使系统具有自动目标跟踪、危险目标自动识别以及各种危险目标告警的功能。在不影响原航海雷达功能的前提下,为其增加了防撞避碰的功能。     

基于压力反馈控制的车间机械手智能防碰撞监测系统设计

针对目前提出的车间机械手智能防碰撞监测系统监测精准度低,防碰撞效果较差的问题,提出基于压力反馈控制的车间机械手智能防碰撞监测系统。硬件系统引入自动调心装置,在机械手内部加入两个避碰开关,将避碰开关与报警器相连接,如果出现碰撞,则发出警报,引用JCQ1工作监测器与机械手串联,确定机械手的工作状态和动作次数。软件部分依据机械臂运动的特点,设置机械手双动式液压缸和臂架单活塞杆双动式液压缸,通过压力反馈计算活塞直径得到液压缸直径,判断最大流量,将判断结果与阈值进行对比,完成防碰撞软件监测。实验结果表明,基于压力反馈控制的车间机械手智能防碰撞监测系统能够更加精准地控制机械手智能防碰撞监测数据,防碰撞控制精度较高,说明所设计系统能够有效实现间机械手智能防碰撞。