Because of the low convergence efficiency of the typical Vicsek model,a Vicsek with static summoning points(VSSP)algorithm based on the Vicsek model considering static summoning points is proposed.Firstly,the mathemat...Because of the low convergence efficiency of the typical Vicsek model,a Vicsek with static summoning points(VSSP)algorithm based on the Vicsek model considering static summoning points is proposed.Firstly,the mathematical model of the individual movement total cost on each summoning point is established.Then the individual classification rule is designed according to the initial state of the cluster to obtain the subclusters guided by each summoning point.Finally,the summoning factor is introduced to modify the course angle updating formula of the Vicsek model.To verify the effectiveness of the proposed algorithm and study the effect of the cluster summoning factor on the convergence rate,three groups of simulation experiments under different summoning factors are designed in this paper.To verify the superiority of the VSSP algorithm,the performance of the VSSP algorithm is compared with the classic algorithm by designing the algorithm performance comparison verification experiment.The results show that the algorithm proposed in this paper has good convergence and course angle consistency.The summoning factor is the sensitive factor of cluster convergence.This algorithm can provide a reference for efficient cluster segmentation movement.展开更多
A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed.The pre-registration is achieved by transforming the mu...A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed.The pre-registration is achieved by transforming the multi-pose models to the standard frontal model＇s reference frame using the principal axis analysis algorithm.Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics.These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration.Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.展开更多
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
基金supported by the National Natural Science Foundation of China(51979193)the China Scholarship Council(201506290080)+1 种基金the China Postdoctoral Science Foundation(2019M653652)the Natural Science Basic Research Plan in Shaanxi Province of China(2019JQ-607).
文摘Because of the low convergence efficiency of the typical Vicsek model,a Vicsek with static summoning points(VSSP)algorithm based on the Vicsek model considering static summoning points is proposed.Firstly,the mathematical model of the individual movement total cost on each summoning point is established.Then the individual classification rule is designed according to the initial state of the cluster to obtain the subclusters guided by each summoning point.Finally,the summoning factor is introduced to modify the course angle updating formula of the Vicsek model.To verify the effectiveness of the proposed algorithm and study the effect of the cluster summoning factor on the convergence rate,three groups of simulation experiments under different summoning factors are designed in this paper.To verify the superiority of the VSSP algorithm,the performance of the VSSP algorithm is compared with the classic algorithm by designing the algorithm performance comparison verification experiment.The results show that the algorithm proposed in this paper has good convergence and course angle consistency.The summoning factor is the sensitive factor of cluster convergence.This algorithm can provide a reference for efficient cluster segmentation movement.
基金supported by the New Century Excellent Talents of China (NCET-05-0866)
文摘A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed.The pre-registration is achieved by transforming the multi-pose models to the standard frontal model＇s reference frame using the principal axis analysis algorithm.Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics.These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration.Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.
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.
