Autonomous underwater vehicles (AUVs) navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions.The movement forms and flow field characteristics of a multi-movin...Autonomous underwater vehicles (AUVs) navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions.The movement forms and flow field characteristics of a multi-moving state AUV navigating in head sea at high speed were studied.The mathematical model on longitudinal motion of the high-speed AUV in head sea was established with considering the hydrodynamic lift based on strip theory,which was solved to get the heave and pitch of the AUV by Gaussian elimination method.Based on this,computational fluid dynamics (CFD) method was used to establish the mathematical model of the unsteady viscous flow around the AUV with considering free surface effort by using the Reynolds-averaged Navier-Stokes (RANS) equations,shear-stress transport (SST) k-w model and volume of fluid (VOF) model.The three-dimensional numerical wave in the computational field was realized through defining the unsteady inlet boundary condition.The motion forms of the AUV navigating in head sea at high speed were carried out by the program source code of user-defined function (UDF) based on dynamic mesh.The hydrodynamic parameters of the AUV such as drag,lift,pitch torque,velocity,pressure,and wave profile were got,which reflect well the real ambient flow field of the AUV navigating in head sea at high speed.The computational wave profile agrees well with the experimental phenomenon of a wave-piercing surface vehicle.The force law of the AUV under the impacts of waves was analyzed qualitatively and quantitatively,which provides an effective theoretical guidance and technical support for the dynamics research and shape design of the AUV in real complex environment.展开更多
This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater ve...This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle(AUV).The number of controllers is increased to realize AUV motion decoupling.At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller.Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified.展开更多
The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great ...The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles(AUVs)because of its hostile and dynamic nature.The major constraints for path planning are limited data transmission capability,power and sensing technology available for underwater operations.The sea environment is subjected to a large set of challenging factors classified as atmospheric,coastal and gravitational.Based on whether the impact of these factors can be approximated or not,the underwater environment can be characterized as predictable and unpredictable respectively.The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner.But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted.Path planning is necessary for many applications involving AUVs.These are based upon planning safety routes with minimum energy cost and computation overheads.This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable.The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.展开更多
基金Project(2006AA09Z235)supported by the National High Technology Research and Development Program of ChinaProject(CX2009B003)supported by Hunan Provincial Innovation Foundation For Postgraduate,China
文摘Autonomous underwater vehicles (AUVs) navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions.The movement forms and flow field characteristics of a multi-moving state AUV navigating in head sea at high speed were studied.The mathematical model on longitudinal motion of the high-speed AUV in head sea was established with considering the hydrodynamic lift based on strip theory,which was solved to get the heave and pitch of the AUV by Gaussian elimination method.Based on this,computational fluid dynamics (CFD) method was used to establish the mathematical model of the unsteady viscous flow around the AUV with considering free surface effort by using the Reynolds-averaged Navier-Stokes (RANS) equations,shear-stress transport (SST) k-w model and volume of fluid (VOF) model.The three-dimensional numerical wave in the computational field was realized through defining the unsteady inlet boundary condition.The motion forms of the AUV navigating in head sea at high speed were carried out by the program source code of user-defined function (UDF) based on dynamic mesh.The hydrodynamic parameters of the AUV such as drag,lift,pitch torque,velocity,pressure,and wave profile were got,which reflect well the real ambient flow field of the AUV navigating in head sea at high speed.The computational wave profile agrees well with the experimental phenomenon of a wave-piercing surface vehicle.The force law of the AUV under the impacts of waves was analyzed qualitatively and quantitatively,which provides an effective theoretical guidance and technical support for the dynamics research and shape design of the AUV in real complex environment.
基金supported by the National Natural Science Foundation of China (6197317561973172)Tianjin Natural Science Foundation (19JCZDJC32800)。
文摘This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle(AUV).The number of controllers is increased to realize AUV motion decoupling.At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller.Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified.
文摘The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles(AUVs)because of its hostile and dynamic nature.The major constraints for path planning are limited data transmission capability,power and sensing technology available for underwater operations.The sea environment is subjected to a large set of challenging factors classified as atmospheric,coastal and gravitational.Based on whether the impact of these factors can be approximated or not,the underwater environment can be characterized as predictable and unpredictable respectively.The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner.But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted.Path planning is necessary for many applications involving AUVs.These are based upon planning safety routes with minimum energy cost and computation overheads.This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable.The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.
