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.

Trajectory tracking control for underactuated unmanned surface vehicles with dynamic uncertainties

The trajectory tracking control problem for underactuated unmanned surface vehicles(USV) was addressed, and the control system took account of the uncertain influences induced by model perturbation, external disturbance, etc. By introducing the reference, trajectory was generated by a virtual USV, and the error equation of trajectory tracking for USV was obtained, which transformed the tracking problem of underactuated USV into the stabilization problem of the trajectory tracking error equation. A backstepping adaptive sliding mode controller was proposed based on backstepping technology and method of dynamic slide model control. By means of theoretical analysis, it is proved that the proposed controller ensures that the solutions of closed loop system have the ultimate boundedness property. Simulation results are presented to illustrate the effectiveness of the proposed controller.

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.

LSTM-DPPO based deep reinforcement learning controller for path following optimization of unmanned surface vehicle

To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal policy opti-mization(DPPO)algorithm,which is a modified actor-critic-based type of reinforcement learning algorithm,is adapted to improve the controller performance in repeated trials.The LSTM network structure is introduced to solve the strong temporal cor-relation USV control problem.In addition,a specially designed path dataset,including straight and curved paths,is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible.Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.

Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles

Unmanned combat system is one of the important means to capture information superiority,carry out precision strike and accomplish special combat tasks in information war.Unmanned attack strategy plays a crucial role in unmanned combat system,which has to ensure the attack by unmanned surface vehicles(USVs)from failure.To meet the challenge,we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization(DAGWO).The traditional grey wolf optimization(GWO)algorithm is improved with a distributed auction mechanism(DAM)to constrain the initialization of wolves,which improves the optimization process according to the actual situation.In addition,one unmanned aerial vehicle(UAV)is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack problem.The proposed DAGWO algorithm can not only ensure the diversity of wolves,but also avoid the local optimum problem.Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.

多USV协同系统研究现状与发展概述

无人水面艇(Unmanned Surface Vehicle,USV)具有较高的智能化程度,较好的隐身性能,较强的机动能力以及较低的造价,被认为是一种作战用途广泛的新概念武器。在无人驾驶运载工具中,USV发展相对较晚,但已逐渐成为一个研究热点课题。文中简述目前国内外USV的研究情况;重点介绍多USV协同系统的现状;阐述高性能艇型、控制体系、通信技术、避碰、任务规划与决策5项关键技术;提出未来发展趋势;展望多USV协同系统在海上补给、水上安全保护、完善无人作战系统、水文气象信息采集和水面搜救方面的应用前景。

USV-LIF遥测系统的控制系统设计与实现

无人船作为一种新型平台,在海洋环境监测中具有布放灵活、自动测量等优势。对于近岸港口等溢油事故发生频繁区域的环境监测,相比于传统的机载和船载溢油激光荧光遥感技术,采用无人船平台搭载激光荧光传感器进行探测更为方便有效。本文对基于无人船的激光荧光(USV-LIF)遥测系统进行总体方案设计,并详细设计了其控制系统,该系统包括岸基远程控制模块和USV-LIF现场机控制模块两部分。利用无线网络通信链路实现从岸基终端对USV-LIF现场机的远程控制与通信,并通过现场机的控制模块实现对USV-LIF现场机的仪器状态控制、数据采集以及时序控制等功能。在实验室条件下,对USV-LIF遥测系统的整体运行、远程控制数据传输以及系统长时间连续工作进行了模拟联调测试,结果表明,所研发的控制系统的功能基本达到了现场探测需求。

基于目标候选的USV海上船艇检测

海上船艇检测是无人水面艇(unmanned surface vehicle,USV)视觉系统最主要的任务之一.提出了一种基于目标候选(object proposal)的USV海上船艇检测算法.首先,基于改进的边缘框(edgebox)算法提取了图像的边缘信息,并建立“目标性”评分函数获取目标候选框;然后,对船艇目标进行方向梯度直方图(histogram of oriented gradient,HOG)特征进行建模,利用支持向量机(support vector machine,SVM),采用“自举法”训练分类器进行迭代;最后,将目标候选框的特征描述子输入到分类器中,对船艇进行检测.此外,基于USV在海天环境下的运行场景,结合海天线的特性进一步提升算法的检测性能.实验结果表明,该算法能快速、准确地检测船艇目标,且具有较高的检测率,对尺度以及光照条件的变化也有较强的鲁棒性.

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DAMPSO算法的USVs集群攻击任务规划研究

现代防御技术的迅速发展使得水面舰艇的攻击效果大大下降,水面无人舰艇自主编队集群攻击技术已经成为未来海战的关键技术之一,多水面无人舰艇之间的任务规划是保证无人舰艇顺利、高效完成任务的关键。将水面无人舰艇集群攻击任务规划问题看成是多约束的任务分配过程,建立任务规划模型,提出了基于分布式拍卖机制的粒子群优化算法,该算法结合分布式拍卖机制对粒子群优化算法的粒子初始化和寻优过程进行改进,使得粒子既符合任务的约束条件,又保持了多样性,避免粒子在寻优过程中陷入局部最优。仿真结果表明应用分布式拍卖机制粒子群优化算法得到的方案不仅完全满足水面无人舰艇集群攻击任务的要求,而且比传统粒子群优化算法和其他群体智能算法具有更好的收敛性。

面向海上搜救的UAV与USV集群协同路径跟踪控制

[目的]针对由无人机(UAV)与水面无人艇(USV)组成的异构无人集群系统,研究其在海上搜救过程中的三维协同路径跟踪控制问题。[方法]首先,在固定坐标系和机体坐标系下建立UAV和USV的运动学模型,同时建立流体坐标系,以设计适宜运动控制的三维路径跟踪控制器,并在Serret-Frenet坐标系下建立UAV和USV的路径跟踪误差模型;然后,在运动学层面设计三维视线(LOS)制导律,并基于此提出一种适用于异构海洋航行器集群的协同路径跟踪控制方法,从而使UAV与USV组成的集群系统可以有效跟踪设定的参数化路径;最后,基于李雅普诺夫稳定性理论分析该控制系统的稳定性。[结果]仿真结果验证了所提异构海洋航行器集群协同路径跟踪控制方法的有效性。[结论]研究成果可为面向海上搜救的无人航行器集群协同路径跟踪控制方法提供参考。

移动长基线定位USV的队形优化

在移动长基线定位系统中,无人水上舰船(USV)的队形以及与定位目标之间的位置关系对定位精度有着重要的影响。本文基于球面交会法,设计了一种精度评价函数。利用此精度评价函数分别研究了多USV结构对定位精度的影响;被定位目标与多USV阵型间的位置关系对定位精度的影响;量测精度对定位精度的影响。通过推理证明,得到被定位目标在4艘USV组成的正方形基阵中垂线上时定位精度最高的结论。仿真结果表明,USV队形对定位精度有显著影响。

不确定性下USV可变自主控制结构及算法

针对水面无人艇(unmanned surface vehicle,USV)在具有不确定性的实际任务形势下实现可变自主的问题,提出一种包含可变自主控制结构和自主等级决策算法的解决方案。可变自主控制结构中考虑了自主等级的划分、自主等级的评价和管理,并设计了基于多属性决策方法的自主等级决策算法。最后在USV导航任务为背景的仿真实验中,证明了可变自主系统控制结构和自主等级决策算法是合理的和有效的,同时显示了可变自主系统研究的实际意义及其对于系统规划性能的提高作用。

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。

基于非对称模型的欠驱动USV自适应路径跟踪控制

研究海流等外界扰动环境下一类非对称欠驱动无人艇的路径跟踪问题,提出了一种自适应路径跟踪控制算法。首先,基于坐标变换解除控制输入与欠驱动运动自由度的耦合;其次,利用级联系统理论,分别设计了自适应位置误差控制律和艏向角误差控制律。其中,提出的自适应位置误差和期望艏向角控制律中前视距离和积分增益都是以位置误差为函数的时变量,自适应估计并补偿外界扰动造成的侧滑角,从而获得对位置误差的镇定。艏向角误差则利用动态反馈线性化比例微分控制方法,根据滤波后的连续平滑信号去镇定。最后,基于级联系统理论和李雅普诺夫理论证明了当所有控制目标实现时,控制系统为一致半全局指数稳定和一致全局渐进稳定的,理论分析和仿真实验说明了算法的有效性。

Adaptive sliding-mode path following control system of the underactuated USV under the influence of ocean currents

The path-following control of the asymmetry underactuated unmanned surface vehicle(USV) under external disturbances such as unknown constant and irrational ocean currents is discussed, and an adaptive sliding-mode path-following control system is proposed, which comprises a path-variable updated law,a modified integral line-of-sight(ILOS) guidance law based on a time-varying lookahead distance and adaptive feedback linearizing controllers combined with sliding-mode technique. A more accurate USV model without the assumption of having diagonal inertia and damping matrices is first presented, aiming at improving the performance of the path-following control. Next, the coordinate transformation is adopted to decouple the sway dynamic from the rudder angle, and the path-following errors dynamics without non-singular problem are presented in the moving Frenet-Serret frame. Then, based on the cascaded theorem and the adaptive sliding-mode method, the adaptive control law of position errors and course error are designed, among which the lookahead distance and integral gain are all computed as different functions of cross-track error to estimate and compensate the sideslip angle caused by external disturbances adaptively. Finally, according to the Lyapunov and cascaded theorem, the control system proposed is proved to be uniform globally asymptotic stability(UGAS) and uniform semiglobal exponential stability(USGES) when the control objectives are all achieved. Simulation results illustrate the precision and high-quality performance of this new controller.

Case Analysis for USV Integrated Mission Planning System

Advanced countries around the world are spurring the development of Unmanned Surface Vehicles (USVs) that can operate autonomously at marine environment. The key enabling technology for such USVs is the mission planning system (MPS) that can autonomously navigate through the harsh waters. The MPS not only has the functions for the navigation, but also has the capabilities, such as obstacle avoidance, malfunction corrections, dealing with unexpected events, return home functions, and many other eventualities that cannot be programmed in advance. The autonomy levels are increasingly moving higher and it is foreseeable that the trend will continue in the future. The main purpose of this paper is the analysis of the MPS onboard the USVs, in terms of the categories, functions, and technological details. Also, we analyze the case study of autonomous mission planning control systems in various fields and introduce the features that constitute the critical functionalities of the mission planning systems.

面向港口环境精细感知的无人船多传感器融合SLAM系统

针对港口环境高精度感知需求,综合考虑影响同时定位与建图(SLAM)精度的港口环境因素,提出基于多传感器融合的激光SLAM环境感知方案。通过分析多种传感器对港口SLAM环境感知的影响,引入惯性测量传感器弥补激光SLAM输出频率低和剧烈运动位姿估计不准确等缺陷,采用卫星定位系统信息进行高程数据约束,处理船舶运动特性导致的垂荡累计漂移。从应用需求出发,对传感器进行选型和布置优化,搭建基于无人船的港口环境多传感器融合SLAM系统。结果表明,提出的港口环境高精度点云地图获取方案能在典型港口场景下准确实时建图,为水面精细SLAM提供技术支持。

自适应量化神经网络滑模无人船编队控制

针对复杂海洋环境下欠驱动水面无人船(unmanned surface vehicle,USV)编队控制存在的模型不确定性、参数摄动、控制输入量化等问题,提出一种自适应量化神经网络滑模控制算法。在USV运动学子系统中,设计基于内外环控制策略的制导律,解决USV欠驱动问题。由于所采用的动力学模型中含有未知项和外界环境干扰,故在USV动力学子系统中通过使用径向基函数神经网络实现对干扰的估计。采用一种线性解析模型来描述输入量化过程。所设计的控制系统不需要量化参数的先验信息。基于输入-状态稳定性理论证明了系统稳定性。通过仿真实验验证了所提算法的有效性。

无人艇在南极科考中的应用

南极科考对于理解全球气候变化、生态系统保护以及地球自身的历史具有极其重要的意义。随着科技进步,无人艇在南极科考中的应用也越来越广泛,无人艇能够在极端环境下开展环境探测和数据采集,为南极科考提供宝贵数据。文章首先介绍了国内外无人艇在南极科考中的应用情况,以及体现出的应用价值;然后,分析了南极科考无人艇的技术特点与未来应用推广;最后,从适应极端环境、长期海上作业、海洋环境认知等方面,对无人艇及其相关技术提出发展建议。