This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles in...This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.展开更多
This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received ...This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'dynamics.Accordingly,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control vectors.Moreover,a disturbance observer is presented to estimate unknown time-varying environmental disturbance.Furthermore,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target.It enables each ASV to adjust its forces and moments according to the received information from its neighbors.The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.展开更多
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 waterways.Th...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 waterways.This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches.It features a holistic viewpoint of ASV safe navigation,namely from collision detection to decision making and then to path replanning.The existing methods in all these three stages are classified according to various criteria.An 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 needed.Finally,more general challenges and future directions of ASV navigation are highlighted.展开更多
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 thr...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 allocation.In 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 constraints.Such an optimization method allows us to consider common propulsion system,including tunnel thruster,azimuth thruster,and-xed propeller with rudder.These linear constraints enable us to explicitly account for the rate,amplitude and azimuth constraints of each propeller on the vessel.The proposed methods have been illustrated by simulated and experimental maneuvers for di®erent thruster layout of a vehicle.展开更多
基金the National Natural Science Foundation of China(51939001,52171292,51979020,61976033)Dalian Outstanding Young Talents Program(2022RJ05)+1 种基金the Topnotch Young Talents Program of China(36261402)the Liaoning Revitalization Talents Program(XLYC20-07188)。
文摘This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.
基金supported in part by the National Science Foundation of China(61873335,61833011)the Project of Scie nce and Technology Commission of Shanghai Municipality,China(20ZR1420200,21SQBS01600,19510750300,21190780300)。
文摘This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'dynamics.Accordingly,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control vectors.Moreover,a disturbance observer is presented to estimate unknown time-varying environmental disturbance.Furthermore,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target.It enables each ASV to adjust its forces and moments according to the received information from its neighbors.The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.
基金This work was supported in part by the Engineering and Physical Sciences Research Council(EPSRC)of the U.K.,the Royal Society of the U.K.
文摘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 waterways.This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches.It features a holistic viewpoint of ASV safe navigation,namely from collision detection to decision making and then to path replanning.The existing methods in all these three stages are classified according to various criteria.An 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 needed.Finally,more general challenges and future directions of ASV navigation are highlighted.
基金supported by the Key R&D Program of Guangdong(2020B1111010002)the Key R&D Program of Hainan(ZDYF2021GXJS041)and the National Natural Science Foundation of China(U2141234).
文摘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 allocation.In 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 constraints.Such an optimization method allows us to consider common propulsion system,including tunnel thruster,azimuth thruster,and-xed propeller with rudder.These linear constraints enable us to explicitly account for the rate,amplitude and azimuth constraints of each propeller on the vessel.The proposed methods have been illustrated by simulated and experimental maneuvers for di®erent thruster layout of a vehicle.