LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Radar-Based Collision Avoidance for Unmanned Surface Vehicles'

Unmanned surface vehicles （USVs） have become a focus of research because of their extensive applications. To ensure safety and reliability and to perform complex tasks autonomously, USVs are required to possess accurate perception of the environment and effective collision avoidance capabilities. To achieve these, investigation into real- time marine radar target detection and autonomous collision avoidance technologies is required, aiming at solving the problems of noise jamming, uneven brightness, target loss, and blind areas in marine radar images. These technologies should also satisfy the requirements of real-time and reliability related to high navigation speeds of USVs. Therefore, this study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle （V-obstacle） which adjusts the USV＇s heading and speed in real-time. Sea trials results in multi-obstacle avoidance firstly demonstrate the effectiveness and efficiency of the proposed avoidance system, and then verify its great adaptability and relative stability when a USV sailing in a real and complex marine environment. The obtained results will improve the intelligent level of USV and guarantee the safety of USV independent sailing.

Analysis of Design Directions for Unmanned Surface Vehicles (USVs)

In recent years, because of the development of marine military science technology, there is a growing interest in the unmanned systems throughout the world. Also, the demand of Unmanned Surface Vehicles (USVs) which can be autonomously operated without the operator intervention is increasing dramatically. The growing interests lie in the facts that those USVs can be manufactured at much lower costs, and can be operated without the human fatigue, while can be sent to the hostile or quite dangerous areas that are inherently unhealthy for human operators. The utilization and the deployment of such vessels will continue to grow in the future. In this paper, along with the technological development of unmanned surface vehicles, we investigate and analyze the cases of already developed platforms and identify the trends of the technological advances. Additionally, we suggest the future directions of development.

A review on COLREGs-compliant navigation of autonomous surface vehicles:From traditional to learning-based approaches

A growing interest in developing autonomous surface vehicles(ASVs)has been witnessed during the past two decades,including COLREGs-compliant navigation to ensure safe autonomy of ASVs operating in complex waterways.This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches.It features a holistic viewpoint of ASV safe navigation,namely from collision detection to decision making and then to path replanning.The existing methods in all these three stages are classified according to various criteria.An in-time overview of the recently-developed learning-based methods in motion prediction and path replanning is provided,with a discussion on ASV navigation scenarios and tasks where learning-based methods may be needed.Finally,more general challenges and future directions of ASV navigation are highlighted.

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

基于改进人工势场法的多无人艇避障策略

针对多无人艇编队避障问题,对静态避障的路径消耗问题进行建模分析,在动态避障时提出一种偏置人工势场法使策略符合艇群国际海上避碰规则(swarm International Regulations for Preventing Collisions at Sea,sCOLREGS)。本方法首先对传统人工势场法进行改进,定义符合艇群会遇态势判断需求的sCOLREGS,通过速度障碍法实时判断碰撞风险,然后利用偏置斥力区域的改进人工势场法实现对规则的遵守。仿真实验表明,本文方法在障碍物与编队大小相当时可显著减少避障路程,在确保避障实时性的同时,较好地遵守了国际海上避碰规则相关条例。研究结论可为海面无人艇集群安全航行提供参考。

基于USV与AUV异构平台协同海洋探测系统研究综述

对基于无人水面艇(USV)和多自主水下机器人(AUVs)组成的异构协同海洋探测系统进行综述。首先介绍不同的USV与多AUVs组成的异构海洋探测系统的最新进展和应用成果,从系统设计概念和技术特点对这些异构系统进行分析和比较;其次对多水下机器人异构系统研究中的协同关键技术进行综述,重点分析协同问题中异构协同、系统任务分配、路径规划和编队控制等问题;而后从环境和硬件的外界约束以及海洋探测系统辅助技术两个方面分析海洋探测系统所面临的技术难点与问题;最后通过分析当前取得的进展和实际需求对多水下机器人异构海洋探测系统的发展前景进行展望。

基于细菌避障策略的无人艇集群自主巡航方法

针对无人艇集群执行巡航任务中的避障问题,提出了一种动态避障算法。首先,获取无人艇周边水域的方形网格轨迹单元态势矩阵;然后,借鉴细菌游走觅食原理,在方形网格轨迹单元集合中,动态匹配最佳避障路径;最后,在动态避障基础上,将无人艇自主巡航过程划分为向目标行进、动态避障、返航补给等阶段,实现无人艇集群对划定水域的常态化巡航。仿真实验结果表明,所提方法对静态点、面障碍以及移动障碍均具有高效的规避能力,在算法基础上,能够实现无人艇集群的常态化无人值守巡航。该算法不仅适合无人艇的避障问题,在其他异构无人装备中也具有广阔的应用前景。

面向USV的AUV自主回收装置设计及其水动力分析

随着海洋无人系统的发展,自主式水下机器人(autonomous underwater vehicle,AUV)的自主布放和回收技术成为亟待突破的瓶颈技术。利用无人水面艇(unmanned surface vehicle,USV)回收AUV成为海洋无人系统协同作业的发展方向之一。目前国外一些研究机构提出的利用USV回收AUV的方案,成熟度都较低。为了实现USV自主回收AUV,设计了一种用于USV动态回收AUV的回收装置。该装置通过绳索连接固定在USV上的绞车,下方连接一个V形翼,用于维持其稳定性,以实现USV在航行中自主收放AUV。首先,介绍了国内外自主回收AUV的研究现状;然后,对回收装置的设计背景、结构形式以及稳定性进行了分析;最后,利用水动力分析软件CFX对AUV在水下对接的动态过程进行了水动力仿真,获得了回收装置在不同工况下对接时的阻力变化情况。通过数值仿真发现:对接过程中,AUV的摩擦阻力逐渐增大;当AUV接近回收装置时,压差阻力迅速减小,但AUV在对接过程中的总阻力是先增加后减小。通过分析可知,所设计的回收装置具有较好的稳定性。研究结果对动态回收AUV具有一定的指导意义。

基于粒子群优化算法的USV集群协同避碰方法

针对无人水面艇(unmanned surface vessel,USV)集群在路径规划中的协同避碰问题,提出了基于滚动优化策略结合粒子群优化算法的USV集群协同避碰方法。首先,通过已有雷达、光电等传感器参数指标建立综合视域模型;其次,采取基于正切函数的惯性权重调整结合线性调整学习因子的方法来提高粒子群优化算法的全局搜索能力,同时,在适应度函数中加入转艏角控制来提高路径的平滑性;最后,利用改进后的粒子群优化算法规划出每个综合视域内的路径。仿真实验结果表明,该优化算法能实现USV集群的实时避碰,并快速为USV集群规划出平滑、安全的全局最优无避碰路径。

面向无人艇的T-DQN智能避障算法研究

无人艇(Unmanned surface vehicle, USV)作为一种具有广泛应用前景的无人系统,其自主决策能力尤为关键.由于水面运动环境较为开阔,传统避障决策算法难以在量化规则下自主规划最优路线,而一般强化学习方法在大范围复杂环境下难以快速收敛.针对这些问题,提出一种基于阈值的深度Q网络避障算法(Threshold deep Q network, T-DQN),在深度Q网络(Deep Q network, DQN)基础上增加长短期记忆网络(Long short-term memory, LSTM)来保存训练信息,并设定经验回放池阈值加速算法的收敛.通过在不同尺度的栅格环境中进行实验仿真,实验结果表明, T-DQN算法能快速地收敛到最优路径,其整体收敛步数相比Q-learning算法和DQN算法,分别减少69.1%和24.8%,引入的阈值筛选机制使整体收敛步数降低41.1%.在Unity 3D强化学习仿真平台,验证了复杂地图场景下的避障任务完成情况,实验结果表明,该算法能实现无人艇的精细化避障和智能安全行驶.

融合改进动态窗口法与速度障碍法的无人船局部路径规划

为解决传统动态窗口法未考虑无人船的运动特性,规划路径绕行于障碍物稠密区外围,路程偏长,且与动态障碍物距离过近,动态避障效果不佳等缺点,提出融合改进动态窗口法与速度障碍法的无人船局部路径规划算法。首先,对于无人船的运动特性,在速度选择中考虑艏摇角速度及漂角的影响;其次,提出障碍物搜索角这一概念,更好地处理障碍物对无人船航行造成的影响,使得无人船可穿行于障碍物稠密区而不发生碰撞,且可大大缩短航行的路程与时间;最后,融合速度障碍法,并根据无人船与动态障碍物的相对速度提出新的动态窗口法评价函数,使得无人船在避碰安全的情况下以较快的速度避开动态障碍物。数值仿真结果表明,改进方法规划的路径曲折少、更平滑,有利于无人船的航行,在路径长度、航行时间与平均速度等方面均优于传统算法,并且具有更强的动态避障调节能力。

基于非线性干扰观测器的无人船与自主水下航行器协同运动控制策略

面向水域探测场景的立体化、多样化、复杂化,基于领航-跟随编队控制的思想,提出了水面无人船(Unmanned Surface Vehicle,USV)和自主水下无人航行器(Autonomous Underwater Vehicle,AUV)跨介质异构体协同运动控制策略.首先,该控制策略考虑了未知环境干扰等因素带来的动力学建模误差,设计了非线性干扰观测器用以估计误差影响;其次,利用动态自适应前视距离改进视线法(LOS),在制导层面解决AUV欠驱动控制问题,结合级联系统分析USV-AUV编队控制问题,通过反馈线性化设计AUV跟随控制器;最后,采用李雅普诺夫理论证明了基于改进制导律的USV-AUV编队控制系统的一致全局渐进稳定.湖试结果表明,所提出的USV-AUV编队控制策略是有效的.

基于蚁群算法的无人船平滑路径规划

为解决无人驾驶船舶在复杂环境中规划路径时存在的转向角度大、路径拐点多、航行能耗高等问题,文中提出一种基于改进蚁群算法的平滑路径规划方法。该方法采用栅格法进行环境建模,通过在启发函数中引入路径平滑度、距离启发因子以及在路径转移概率中引入障碍物启发因素,提高路径寻优和静态避障能力。结合启发因素改进信息素更新标准,设置可调节信息素挥发因子增加算法的自适应性。提取输出的最优路径关键节点并对其进行平滑处理,进一步保证路径平滑度和安全性。根据不同栅格环境下的避障仿真结果可知,与传统算法相比,文中改进蚁群算法的路径寻优速度提高了45%~62%,转向次数减少了25%~44%,平滑处理后的路径安全性和可行性得到了提升,较好地实现了不同环境下无人船自主路径规划。

基于漂角估计的无人船局部动态避障方法

为了提高无人船局部动态避障的规范性、安全性和稳定性,提出基于漂角估计的无人船局部动态避障方法。首先,根据国际海上避碰规则(COLREGS)选取动态窗口法(DWA)的速度空间;其次,根据预测的障碍物运动状态选择动态窗口法中无人船的无碰预测轨迹;最后,考虑海洋扰动,利用横向位置误差估计漂角,并在无人船的航向上进行补偿。仿真实验结果验证了所提方法的有效性。

虚实结合的无人水面艇自主航行测试系统设计

无人水面艇自主航行系统的海上实装测试成本高、周期长,纯虚拟仿真测试在无人水面艇运动仿真环节真实性差,为了提高无人水面艇自主航行系统测试的效费比,综合虚拟仿真测试与实装测试的优势,设计了虚实结合的无人水面艇自主航行测试系统,在任务场景构建与环境感知传感器环节使用虚拟仿真技术,在无人艇传动系统与船舶海洋动力学环节采用实装在环路技术,通过虚实空间配准技术将无人水面艇在虚拟空间的数字孪生体与真实空间的物理实体进行实时空间位置、姿态同步,最后通过仿真实验表明了该测试系统的正确性与合理性。

改进速度障碍法的无人艇局部路径规划

针对传统速度障碍(velocity obstacles,VO)法进行局部路径规划时避碰路径长、避碰时间长以及忽略紧迫局面会遇中避让责任的约束等问题,提出一种适用于水面无人艇(unmanned surface vehicle,USV)的改进VO法。将四元船舶领域和碰撞威胁距离引入VO避碰时机的计算中,确定了避碰阈值;将最晚施舵距离作为碰撞危险局面过渡到紧迫局面的临界值,构建了紧迫局面模型;设计了USV避碰模型,并通过国际海上避碰规则(International Regulations for Collision Avoidance at Sea,GOLREGs)进行约束。比较分析了对遇、右交叉、左交叉和追越4种会遇的避碰仿真。仿真结果表明:改进的VO法平均路径缩短39.88%、平均时间减少44.26%,改进后的局部规划能够在保证安全避碰的条件下满足GOLREGs第8条和第13~17条的避让要求。

一种水面无人艇拖线阵自主收放系统设计

任务载荷的自主收放是无人船搭载任务载荷完成海洋探测任务的关键,本文设计一种无人船拖线阵自主收放系统,从总体设计、机械结构、液压驱动、自主控制等方面做全面阐述。海上试验证明,该收放系统能够在无人船海上机动时,安全可靠地实现大缆长变直径拖线阵的自主收放、存储、拖曳等功能,在无人干预下可自主实现拖线阵的可靠释放和回收,为我国无人船开展声学探测任务奠定了技术基础。

基于椭圆碰撞锥的无人艇动态避障方法

为了提高无人艇在海洋动态避障中的效率,提出一种基于椭圆聚类-碰撞锥推演的可预测型无人艇海洋动态避障控制方法。在现有的路径规划算法,大多数对障碍物进行圆形建模,进一步采取规避措施。依据无人艇在海洋环境下实际的碰撞问题,提出对动态船只进行椭圆聚类,进一步提高无人艇的避障效率。提出的避障算法结合了《国际海上避碰规则》(COLREGS),并在避障结束后回归到初始规划轨迹上,最后,对提出的动态避障算法进行了仿真分析和实验测试。

基于速度障碍法和动态窗口法的无人水面艇动态避障

速度障碍法是一种普遍应用在无人水面艇(unmanned surface vehicle,USV)上的动态避障方法,但传统的速度障碍法未考虑无人水面艇运动学性能与障碍物运动信息误差的影响,也未明确何时开始避障与结束避障.在速度障碍法的基础上,用椭圆表示无人水面艇与障碍物,给出一种求解椭圆切线的方法;将动态窗口法与速度障碍物法相结合,考虑无人水面艇的运动学性能,只使用无人水面艇在给定时间内能到达的速度和方向进行避障计算;通过比较碰撞时间与无人水面艇避开障碍物所需的时间来确定何时开始避障,并提出一种结束避障的判断方法;为了减小障碍物运动信息误差的影响,提出了一种虚拟障碍物方法.最后,通过仿真实验验证了该避障方法的可行性与有效性.