Interpolation Technique for the Underwater DEM Generated by an Unmanned Surface Vessel

High-resolution underwater digital elevation models(DEMs)are important for water and soil conservation,hydrological analysis,and river channel dredging.In this work,the underwater topography of the Panjing River in Shanghai,China,was measured by an unmanned surface vessel.Five different interpolation methods were used to generate the underwater DEM and their precision and applicability for different underwater landforms were analyzed through cross-validation.The results showed that there was a positive correlation between the interpolation error and the terrain surface roughness.The five interpolation methods were all appropriate for the survey area,but their accuracy varied with different surface roughness.Based on the analysis results,an integrated approach was proposed to automatically select the appropriate interpolation method according to the different surface roughness in the surveying area.This approach improved the overall interpolation precision.The suggested technique provides a reference for the selection of interpolationmethods for underwater DEMdata.

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.

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 planning and tracking control for underactuated unmanned surface vessels

The trajectory planning and tracking control for an underactuated unmanned surface vessel(USV) were addressed.The reference trajectory was generated by a virtual USV,and the error equation of trajectory tracking for underactuated USV was obtained,which transformed the tracking and stabilization problem of underactuated USV into the stabilization problem of the trajectory tracking error equation.A nonlinear state feedback controller was proposed based on backstepping technique and Lyapunov's direct method.By means of Lyapunov analysis,it is proved that the proposed controller ensures that the solutions of closed loop system have the ultimate boundedness property.Numerical simulation results are presented to validate the effectiveness and robustness 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.

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.

Comprehensive Marine Observing Experiment Based on High-Altitude Large Unmanned Aerial Vehicle(South China Sea Experiment2020 of the “Petrel Project”)

In collaboration with 12 other institutions, the Meteorological Observation Center of the China Meteorological Administration undertook a comprehensive marine observation experiment in the South China Sea using the Yilong-10 high-altitude large unmanned aerial vehicle(UAV). The Yilong-10 UAV carried a self-developed dropsonde system and a millimeter-wave cloud radar system. In addition, a solar-powered unmanned surface vessel and two drifting buoys were used. The experiment was further supported by an intelligent, reciprocating horizontal drifting radiosonde system that was deployed from the Sansha Meteorological Observing Station, with the intent of producing a stereoscopic observation over the South China Sea. Comprehensive three-dimensional observations were collected using the system from 31 July to2 August, 2020. This information was used to investigate the formation and development processes of Typhoon Sinlaku(2020). The data contain measurements of 21 oceanic and meteorological parameters acquired by the five devices, along with video footage from the UAV. The data proved very helpful in determining the actual location and intensity of Typhoon Sinlaku(2020). The experiment demonstrates the feasibility of using a high-altitude, large UAV to fill in the gaps between operational meteorological observations of marine areas and typhoons near China, and marks a milestone for the use of such data for analyzing the structure and impact of a typhoon in the South China Sea. It also demonstrates the potential for establishing operational UAV meteorological observing systems in the future, and the assimilation of such data into numerical weather prediction models.

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。

带有状态/输入量化的无人艇航向跟踪控制

[目的]针对水面无人艇(USV)的海上通信受限的问题,提出一种带有状态/输入量化的USV航向跟踪控制方法。[方法]基于反步法设计系统控制律,结合动态面技术降低虚拟控制律的计算量膨胀问题。对于控制系统中存在的不确定项及外界干扰,利用扩张状态观测器(ESO)进行估计。采用均匀量化器分别对控制系统中的状态变量和控制输入进行量化,且量化后的状态反馈信息仅用于跟踪控制。利用量化状态递归设计基于ESO的USV航向控制器,证明闭环控制系统中量化变量和非量化变量间误差的有界性。[结果]基于李雅普诺夫稳定性理论,提出了量化误差考量及闭环系统稳定性判定方法,严格证明了所设计的带有状态量化和输入量化的USV航向跟踪控制系统的稳定性,仿真实验验证了该控制策略的有效性。[结论]结果表明,所提方法可为USV航向跟踪控制提供借鉴。

改进RRT^(*)算法的无人艇局部路径规划方法

文中提出一种改进RRT^(*)算法,该算法依据国际海上避碰规则计算本船与他船间DCPA与TCPA并判断会遇场景,限制采样空间.通过偏置采样在采样空间中选取采样点,增强目的性,加快搜索速度.依据线段公理和锚点采样的方式重新选择父节点,去除中间冗余的节点,减少路径转向次数和路径长度,使得最终生成的路径相对平滑.结果表明:改进RRT^(*)算法减少了81%以上的转向点和5%以上的路径长度.

无人水面艇路径规划与控制实验平台研究

针对无人艇路径规划与避障控制等功能在真实环境下系统验证和评估不足的问题,提出一种具备多种传感器和控制系统的无人滑行艇实验平台。文中所开发的实验平台集成激光雷达、深度相机、全球定位系统(global positioning system,GPS)、超宽带(ultra wide band,UWB)定位系统、陀螺仪等多种传感器,并采用基于机器人操作系统(robot operating system,ROS)和Free_RTOS操作系统的主控制器和底层控制器。该实验平台不仅包括滑行艇本身、感知系统、底层控制器和远程监控系统等,还包括上层定位算法、路径规划算法和执行器控制算法。文中以1艘装载了多种传感器的无人滑行艇为实验对象,搭建了包括建图、定位与远程监控的综合实验平台,并且进行了滑行艇导航实验,验证了路径规划和避障控制算法的有效性,可为无人艇的技术发展和应用提供一定的参考。

土耳其武装无人艇发展

近年来,土耳其武装无人艇发展迅速,已成为世界无人艇装备领域重要的力量,从作战概念设计、装备发展、技术实现和试验验证等方面梳理了土耳其武装无人艇的发展。首先总结了土耳其武装无人艇作战概念和主要武装无人艇发展现状,归纳了各主要武装无人艇的作战用途、平台特点、作战能力和任务载荷搭载情况;其次介绍了各武装无人艇搭载的模块化导弹武器载荷、反潜任务载荷等无人任务载荷,讨论了各任务载荷的主要技术和战术指标;然后分析了土耳其武装无人艇开展的自主航行试验、导弹实弹射击试验、遥控武器站实弹射击试验、反潜作战试验等,重点指出了各项试验的显著特点;最后进行了土耳其武装无人艇与世界典型无人艇的对比,进一步概括了土耳其武装无人艇的主要发展特点。系统性地对土耳其武装无人艇发展进行分析总结,可为无人艇论证和研制提供一定的借鉴。

基于改进粒子群算法的 USV 航向分数阶控制

针对欠驱动水面船舶(underactuated surface vessel,USV)航向保持稳定性问题,对船舶自动舵控制系统设计了分数阶PIλDμ控制器。积分阶次λ和微分阶次μ的引入使得分数阶比例-积分-微分(proportion integration differentiation,PID)PIλDμ控制器具有更好的鲁棒性和抗扰动能力,但同时也加大了算法设计的难度。使用改进粒子群算法对分数阶PIλDμ控制器参数进行整定,即解决了粒子群算法容易使粒子陷入局部最优问题,又解决了分数阶PIλDμ控制器整定参数多、设计复杂问题。通过仿真对比实验,结果表明,该控制器能很好地根据船舶动态特性变化,自动进行适应性参数优化,具有跟踪速度快、航向控制超调小以及抗扰性强等优点。

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。

虚假数据注入式攻击下无人水面船舶自适应神经输出反馈轨迹跟踪控制

本文主要研究网络环境下无人水面船舶(Unmanned surface vessels,USVs)遭受虚假数据注入式(False-data-injection,FDI)攻击的跟踪控制问题.其中,内部和外部不确定以及输入饱和约束等实际因素均考虑在设计中.在控制设计过程中,为避免将船舶速度的攻击信号引入闭环系统,采用分类重构思想,构造一种新的神经网络(Neural network,NN)状态观测器,同时重构船舶速度和攻击信号.进一步,在backstepping设计框架下,利用重构的攻击信号补偿USVs运动学通道因虚假数据注入式攻击引起的非匹配不确定项.在动力学设计通道中,利用自适应神经技术和单参数学习法,重构由内部和外部不确定组成的复合不确定部分,进而提出自适应神经输出反馈控制方案.理论分析表明,即便在FDI攻击、内外不确定以及执行器饱和约束的情况下,所提控制方案仍能迫使USVs跟踪给定的参考轨迹.同时,仿真和比较结果证实了所提控制方案的有效性和优越性.

异构多智能体输出调节量化自适应跟踪控制

考虑到无人机-无人船动力学模型中部分状态信息丢失、输出降维和延迟量化输入等复杂情形,提出了一种适用于解决异构多智能体系统一致性跟踪控制问题的输出调节量化自适应方案。利用降维的输出信息设计量化反馈控制输入及自适应律,实现异构多智能体编队系统内全维状态信息向参考模型的一致性跟踪,基于Lyapunov方法的稳定性分析证明了各变量的一致有界性,将该一致性跟踪控制方法应用于无人机-无人船编队系统中,通过数值仿真验证了该异构多智能体编队系统在平面内实现各变量一致性跟踪的能力。

水面无人艇单目视觉伺服自主控制研究综述

单目视觉伺服采用相机模仿人眼视觉功能,感知周围环境和测量运动状态,是提高无人艇航行感知与控制自主性的重要手段。首先,从基本原理出发,简述视觉伺服技术分类、相机透视投影原理和无人艇运动数学模型,为文献综述分析提供框架基础。然后,根据任务复杂度,依次综述单目视觉伺服在无人艇航向控制、镇定控制、轨迹跟踪控制和集群控制等典型应用场景下的研究进展和挑战。最后,系统性地总结无人艇单目视觉伺服自主控制的潜在研究方向和发展趋势。

基于战术意图欺骗的无人舰艇航路规划算法

随着海战场的智能化,无人舰艇的航路意图欺骗性对作战至关重要。针对传统航路规划算法对航路意图欺骗性的考虑缺失,提出一种作战空间欺骗性能的离散表征方法,以及一种基于PID代价的DTI-A*欺骗航路规划算法。经仿真实验结果表明,该算法可以显著提高所规划航路的欺骗性,极大程度地阻碍敌方对我方战术意图的识别,大幅提升作战任务的成功率。

基于智能预测控制的鱼雷状小型无人艇轨迹跟踪研究

[目的]针对无人艇(USV)在狭窄湖泊、涵洞作业时存在精度保持难和航迹控制难的问题,以自主研制的一款鱼雷状小型USV为对象,提出一种轨迹跟踪智能预测控制方法。[方法]首先,构建自主研制的欠驱动USV非线性状态空间模型;然后,设计智能预测控制器,该控制器基于模型预测控制的设计思想并结合改进的粒子群算法,在线决策、优化每一时刻的性能指标并纠正预测状态;最后,开展仿真和湖试试验测试系统对参考轨迹的跟踪性能,并与线性模型预测控制器的跟踪性能进行比较。[结果]结果表明,所设计的智能预测控制器超调小、抗干扰性好。[结论]所提方法不仅能运用于鱼雷状小型USV跟踪系统,也能对其他USV跟踪系统起到很好的借鉴作用。

基于自抗扰控制器的无人艇航向自适应控制

为了提高水面无人艇在外界干扰和自身运动状态变化等非线性及不确定因素下的航向控制精度,针对常规自抗扰控制(ADRC)在船舶航向控制中存在的参数整定难这一问题,提出将粒子群优化算法(PSO)与径向基函数神经网络(RBF)相结合的ADRC参数自适应整定和无人艇航向精确控制策略。设计一个RBF网络用于准实时无人艇运动模型建模。在此基础上,运用粒子群算法对无人艇航向ADRC控制器参数整定进行优化。仿真试验结果表明:设计的RBF-PSO-ADRC控制器在无人艇的航向控制中相比常规ADRC控制器和比例-积分-微分控制器(PID)控制器表现出更好的动态响应特性和稳定性,对未知扰动也具有较强的鲁棒性和抗干扰能力。RBF-PSO-ADRC控制器实现了复杂环境下ADRC控制器参数的自适应优化整定,这对于高精度的无人艇航向控制和航线跟踪具有一定实际意义,可为水面无人艇操纵设计提供有益参考。