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Real-Time Control Allocation for autonomous surface vehicle Using Constrained Quadratic Programming

Guidance, Navigation and Control 2021年第4期1卷 116-133页

作者： Xiaocheng Liu Zhihuan Hu Ziheng Yang Weidong Zhang Department of Automation Shanghai JiaoTong University Shanghai 200240 China Key laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 China

Control system is very important for each autonomous surface vehicle(ASV),which involves the problem of maintaining the vehicle's position and heading using feedback controller and achieving the desired forces through thrust *** this paper,we present a practical thrust allocator for under-actuated and fully-actuated vehicles,which can be represented as a quadratic programming(QP)problem with linear *** an optimization method allows us to consider common propulsion system,including tunnel thruster,azimuth thruster,and-xed propeller with *** linear constraints enable us to explicitly account for the rate,amplitude and azimuth constraints of each propeller on the *** proposed methods have been illustrated by simulated and experimental maneuvers for di®erent thruster layout of a vehicle.

关键词： Control allocation autonomous surface vehicle convex optimization

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

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Journal of Automation and Intelligence 2022年第1期1卷 23-33页

作者： Liang Hu Huosheng Hu Wasif Naeem Zidong Wang School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK School of Electronics Electrical Engineering and Computer ScienceQueen’s University BelfastBelfast BT71NNUK Department of Computer Science Brunel University LondonUxbridge UB83PHUK

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 *** paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based *** features a holistic viewpoint of ASV safe navigation,namely from collision detection to decision making and then to path *** existing methods in all these three stages are classified according to various *** 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 ***,more general challenges and future directions of ASV navigation are highlighted.

关键词： autonomous surface vehicle Collision avoidance Path re-planning Deep reinforcement learning

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