Unmanned surface vessels (USVs) are widely used in ocean exploration and environmental protection fields. To ensure that USV can successfully perform its mission, trajectory planning and motion tracking are the two most critical technologies. In this paper, we propose a novel trajectory generation and tracking method for USV based on optimization theory. Specifically, the USV dynamic model is described with differential flatness, so that the trajectory can be generated by dynamic RRT* in a linear invariant system expression form under the objective of optimal boundary value. To reduce the sample number and improve efficiency, we adjust the trajectory through local optimization. The dynamic constraints are considered in the optimization process so that the generated trajectory conforms to the kinematic characteristics of the under-actuated hull, and makes it easier to be tracked. Finally, motion tracking is added with model predictive control under a sequential quadratic programming problem. Experimental results show the planned trajectory is more in line with the kinematic characteristics of USV, and the tracking accuracy remains a higher level.
Katholieke Universiteit Leuven（鲁汶天主教大学）；University of Melbourne（墨尔本大学）
We propose a safety-guaranteed planning and control framework for unmanned surface vessels (USVs), using Gaussian processes (GPs) to learn uncertainties. The uncertainties encountered by USVs, including external disturbances and model mismatches, are potentially state-dependent, time-varying, and hard to capture with constant models. GP is a powerful learning-based tool that can be integrated with a model-based planning and control framework, which employs a Hamilton-Jacobi differential game formulation. Such a combination yields less conservative trajectories and safety-guaranteeing control s…
MMUSV-Sim: A Perception-Oriented Simulation and Data-Generation Platform for Multi-USV Cooperative Perception
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院）；School of Intelligent Imagery Engineering, Beijing Film Academy（北京电影学院智能影像工程学院）
Distributed Control Barrier Functions for Safe Multi-Vehicle Navigation in Heterogeneous USV Fleets
Massachusetts Institute of Technology（麻省理工学院）；MIT Lincoln Laboratory（MIT林肯实验室）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Charles Stark Draper Laboratory（查尔斯·斯塔克·德拉珀实验室）；Brigham Young University
Efficient Time-Domain Simulation of USV Motions in Short-Crested Irregular Waves Using an IRF-Based Framework
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University（上海交通大学海洋工程国家重点实验室）；Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde（斯特拉斯克莱德大学海军建筑、海洋与海洋工程系）；Institute of Artificial Intelligence, Shanghai University（上海大学人工智能研究院）；Rules & Technology Center of China Classification Society, Shanghai（中国船舶分类学会规则与技术中心）
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.
Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection
Institute of Informatics & Telecommunications, National Center for Scientific Research “Demokritos”（德谟克利特国家科学研究所信息与通信研究所）
