在航运港口中，进出港、港内货物运输或仅执行监视任务等重复性机动作业，可由本土驾驶员凭借经验高效快速完成。这类实操经验可被无人水面艇（Unmanned Surface Vehicles, USV）习得，以自主复现相同任务。然而，船舶轨迹固有的非线性特性，以及风、海流等环境扰动，使得模型及其对应控制律的学习难度较大。为此，我们提出一种面向USV的数据驱动学习与控制方法，该方法基于高斯混合模型（Gaussian Mixture Model）、高斯混合回归（Gaussian Mixture Regression）以及Sontag通用公式构建。我们的方法既能学习非线性动力学特性，又能通过鲁棒控制器保证向目标的收敛性。我们通过在休达港（port of Ceuta）开展船舶实验采集了真实数据，已成功学习专家操控的复杂轨迹及对应的鲁棒控制器。文中展示了该控制器针对噪声与扰动的作用效果，采用误差指标对比估计轨迹与真实数据轨迹，并最终开展了计算复杂度分析。
英文摘要：
At shipping ports, some repetitive maneuvering tasks such as entering/leaving port, transporting goods inside it or just making surveillance activities, can be efficiently and quickly carried out by a domestic pilot according to his experience. This know-how can be seized by Unmanned Surface Vehicles (USV) in order to autonomously replicate the same tasks. However, the inherent nonlinearity of ship trajectories and environmental perturbations as wind or marine currents make it difficult to learn a model and its respective control. We therefore present a data-driven learning and control methodology for USV, which is based on Gaussian Mixture Model, Gaussian Mixture Regression and the Sontag's universal formula. Our approach is capable to learn the nonlinear dynamics as well as guarantee the convergence toward the target with a robust controller. Real data have been collected through experiments with a vessel at the port of Ceuta. The complex trajectories followed by an expert have been learned including the robust controller. The effect of the controller over noise/perturbations are presented, a measure of error is used to compare estimates and real data trajectories, and finally, an analysis of computational complexity is performed.
Batch Estimation of a Steady, Uniform, Flow-Field from Ground Velocity and Heading Measurements
Department of Mechanical Engineering（机械工程系）；Engineering Science, University of North Carolina at Charlotte（工程科学，北卡罗来纳大学夏洛特分校）；Physical Acoustics Branch, Code 7135, Naval Research Laboratory（声学分支，海军研究实验室）
Guidance and Control of Unmanned Surface Vehicles via HEOL
ENSTA；UMR 6285；IPP Lab-STICC；ENI Brest；UMR CNRS 6027；IRDL；CRAN (CNRS, UMR 7039)；Université de Lorraine；LIX (CNRS, UMR 7161)；École polytechnique；LJLL (CNRS, UMR 7238)；Sorbonne Unversité；AL.I.E.N.
System Architecture and Communication Infrastructure for the RoboVaaS project
Fraunhofer Center for Maritime Logistics and Service（弗劳恩霍夫海事物流与服务中心）；University of Padova（帕多瓦大学）；Wireless and More srl（Wireless and More有限公司）；Hamburg University of Technology（汉堡工业大学）
Cooperative Integrated Estimation-Guidance for Simultaneous Interception of Moving Targets
Intelligent Systems & Control (ISaC) Lab, Department of Aerospace Engineering, Indian Institute of Technology Bombay（智能系统与控制实验室，航空航天工程系，印度理工学院班加罗尔）；Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati（智能系统引导、自主、学习与控制实验室，航空航天工程与工程力学系，辛辛那提大学）
