Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
In this paper, Neural Networks （NNs） are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverabil...In this paper, Neural Networks （NNs） are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.展开更多
Sloshing phenomenon in the liquid cargo carriers has caught the attention of researchers as the interaction between the sloshing waves and structure is one of the key point and difficulty in the study of sloshing.In t...Sloshing phenomenon in the liquid cargo carriers has caught the attention of researchers as the interaction between the sloshing waves and structure is one of the key point and difficulty in the study of sloshing.In this paper,we captured the free surface with a volume of fluid(VOF) method and then calculated the motions and responses of the structure by adopting the Reynolds-averaged Navier-Stokes(RANS) equations for the whole fluid domain.With the use of user defined functions(UDF) in Fluent,the interaction between fluid and structure was then simulated.As a reasonable simplification,the authors studied the response of a single cantilever in a tank under sloshing loads;Further study should pay more attention to the mechanisms of interaction between sloshing waves and elastic structures.展开更多
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
基金Partially Supported by the Special Item for the Fujian Provincial Department of Ocean and Fisheries(No.MHGX-16)the Special Item for Universities in Fujian Province by the Education Department(No.JK15003)
文摘In this paper, Neural Networks （NNs） are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.
基金Supported by the National Natural Science Foundation of China under the Grant No.10472032,50879030
文摘Sloshing phenomenon in the liquid cargo carriers has caught the attention of researchers as the interaction between the sloshing waves and structure is one of the key point and difficulty in the study of sloshing.In this paper,we captured the free surface with a volume of fluid(VOF) method and then calculated the motions and responses of the structure by adopting the Reynolds-averaged Navier-Stokes(RANS) equations for the whole fluid domain.With the use of user defined functions(UDF) in Fluent,the interaction between fluid and structure was then simulated.As a reasonable simplification,the authors studied the response of a single cantilever in a tank under sloshing loads;Further study should pay more attention to the mechanisms of interaction between sloshing waves and elastic structures.
