In this paper,we present a robot vision based system for coordinate measurement of feature points on large scale automobile parts.Our system consists of an industrial 6-DOF robot mounted with a CCD camera and a PC.The...In this paper,we present a robot vision based system for coordinate measurement of feature points on large scale automobile parts.Our system consists of an industrial 6-DOF robot mounted with a CCD camera and a PC.The system controls the robot into the area of feature points.The images of measuring feature points are acquired by the camera mounted on the robot.3D positions of the feature points are obtained from a model based pose estimation that applies to the images.The measured positions of all feature points are then transformed to the reference coordinate of feature points whose positions are obtained from the coordinate measuring machine（CMM）.Finally,the point-to-point distances between the measured feature points and the reference feature points are calculated and reported.The results show that the root mean square error（RMSE） of measure values obtained by our system is less than 0.5 mm.Our system is adequate for automobile assembly and can perform faster than conventional methods.展开更多
The kinematic error model of a 6-DOF space robot is deduced, and the cost function of kinematic parameter identification is built. With the aid of the genetic algorithm （GA） that has the powerful global adaptive pro...The kinematic error model of a 6-DOF space robot is deduced, and the cost function of kinematic parameter identification is built. With the aid of the genetic algorithm （GA） that has the powerful global adaptive probabilistic search ability, 24 parameters of the robot are identified through simulation, which makes the pose （position and orientation） accuracy of the robot a great improvement. In the process of the calibration, stochastic measurement noises are considered. Lastly, generalization of the identified kinematic parameters in the whole workspace of the robot is discussed. The simulation results show that calibrating the robot with GA is very stable and not sensitive to measurement noise. Moreover, even if the robot＇s kinematic parameters are relative, GA still has strong search ability to find the optimum solution.展开更多
This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for...This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.展开更多
With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technol...With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility.However,the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement.To address these challenges,this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net(Dual-line Registration Network)to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces.First,feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features.Subsequently,the features are sampled through unanimous voting and fed into the RANSAC(Random Sample Consensus)algorithm for pose estimation,enabling multi-view point cloud registration.Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy,thereby enhancing the overall performance of point cloud registration.展开更多
Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths....Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths.Consequently,it is difficult to guarantee the integrity and accuracy of three-dimensional(3D)measurements.In this study,a novel measurement trajectory planning method is developed to accurately obtain the 3D point clouds of large complex components by accounting for the field of view and overlapping area constraints.A hybrid identification algorithm based on the quasi-Newton and Levenberg Marquardt method is then proposed to realize the synchronous identification of kinematic parameter errors of the measurement system,allowing it to accurately reach the planning viewpoint.Finally,robotic calibration and measurement experiments of a high-speed rail headstock are conducted to evaluate the effectiveness and practicality of the proposed methods.展开更多
基金wsupported by the Thailand Research Fund and Solimac Automation Co.,Ltd.under the Research and Researchers for Industry Program(RRI)under Grant No.MSD56I0098Office of the Higher Education Commission under the National Research University Project of Thailand
文摘In this paper,we present a robot vision based system for coordinate measurement of feature points on large scale automobile parts.Our system consists of an industrial 6-DOF robot mounted with a CCD camera and a PC.The system controls the robot into the area of feature points.The images of measuring feature points are acquired by the camera mounted on the robot.3D positions of the feature points are obtained from a model based pose estimation that applies to the images.The measured positions of all feature points are then transformed to the reference coordinate of feature points whose positions are obtained from the coordinate measuring machine（CMM）.Finally,the point-to-point distances between the measured feature points and the reference feature points are calculated and reported.The results show that the root mean square error（RMSE） of measure values obtained by our system is less than 0.5 mm.Our system is adequate for automobile assembly and can perform faster than conventional methods.
基金supported by National Natural Science Foundation of China(No.60775049).
文摘The kinematic error model of a 6-DOF space robot is deduced, and the cost function of kinematic parameter identification is built. With the aid of the genetic algorithm （GA） that has the powerful global adaptive probabilistic search ability, 24 parameters of the robot are identified through simulation, which makes the pose （position and orientation） accuracy of the robot a great improvement. In the process of the calibration, stochastic measurement noises are considered. Lastly, generalization of the identified kinematic parameters in the whole workspace of the robot is discussed. The simulation results show that calibrating the robot with GA is very stable and not sensitive to measurement noise. Moreover, even if the robot＇s kinematic parameters are relative, GA still has strong search ability to find the optimum solution.
基金supported by the National Natural Science Foundation of China (No.11102018)
文摘This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.
基金co-supported by the National Natural Science Foundation of China(U22A20176)Guangdong Basic and Applied Basic Research Foundation,China(2022B1515120078)+2 种基金the Guangdong Basic and Applied Basic Research Foundation,China(2021A1515110898)GDAS’Project of Science and Technology Development,China(2022GDASZH-2022010108)the Key Areas R&D Program of Dongguan City,China(20201200300062).
文摘With the rapid advancement of mechanical automation and intelligent processing technology,ac-curately measuring the surfaces of complex parts has emerged as a significant research challenge.Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility.However,the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement.To address these challenges,this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net(Dual-line Registration Network)to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces.First,feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features.Subsequently,the features are sampled through unanimous voting and fed into the RANSAC(Random Sample Consensus)algorithm for pose estimation,enabling multi-view point cloud registration.Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy,thereby enhancing the overall performance of point cloud registration.
基金supported by the National Natural Science Foundation of China(Grant Nos.52105514,52075204)the Fundamental Research Funds for the Central Universities(Grant No.2042023kf0114)+1 种基金Wuhan Natural Science Foundation(Grant No.20240408010202220)Hubei Province Key R&D Program(Grant No.2022BAA067).
文摘Large complex components are characterized by their complexity and large size,making it challenging to precisely calibrate robots and measurement devices,compensate for their pose and error,and plan measurement paths.Consequently,it is difficult to guarantee the integrity and accuracy of three-dimensional(3D)measurements.In this study,a novel measurement trajectory planning method is developed to accurately obtain the 3D point clouds of large complex components by accounting for the field of view and overlapping area constraints.A hybrid identification algorithm based on the quasi-Newton and Levenberg Marquardt method is then proposed to realize the synchronous identification of kinematic parameter errors of the measurement system,allowing it to accurately reach the planning viewpoint.Finally,robotic calibration and measurement experiments of a high-speed rail headstock are conducted to evaluate the effectiveness and practicality of the proposed methods.
