An optimal measurement pose number searching method was designed to improve the pose selection method.Several optimal robot measurement configurations were added to an initial pre-selected optimal configuration set to...An optimal measurement pose number searching method was designed to improve the pose selection method.Several optimal robot measurement configurations were added to an initial pre-selected optimal configuration set to establish a new configuration set for robot calibration one by one.The root mean squares (RMS) of the errors of each end-effector poses after being calibrated by these configuration sets were calculated.The optimal number of the configuration set corresponding to the least RMS of pose error was then obtained.Calibration based on those poses selected by this algorithm can get higher end-effector accuracy,meanwhile consumes less time.An optimal pose set including optimal 25 measurement configurations is found during the simulation.Tracking errors after calibration by using these poses are 1.54,1.61 and 0.86 mm,and better than those before calibration which are 7.79,7.62 and 8.29 mm,even better than those calibrated by the random method which are 2.22,2.35 and 1.69 mm in directions X,Y and Z,respectively.展开更多
This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters ...This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.展开更多
基金Project(2008AA04Z203) supported by the National High Technology Research and Development Program of China
文摘An optimal measurement pose number searching method was designed to improve the pose selection method.Several optimal robot measurement configurations were added to an initial pre-selected optimal configuration set to establish a new configuration set for robot calibration one by one.The root mean squares (RMS) of the errors of each end-effector poses after being calibrated by these configuration sets were calculated.The optimal number of the configuration set corresponding to the least RMS of pose error was then obtained.Calibration based on those poses selected by this algorithm can get higher end-effector accuracy,meanwhile consumes less time.An optimal pose set including optimal 25 measurement configurations is found during the simulation.Tracking errors after calibration by using these poses are 1.54,1.61 and 0.86 mm,and better than those before calibration which are 7.79,7.62 and 8.29 mm,even better than those calibrated by the random method which are 2.22,2.35 and 1.69 mm in directions X,Y and Z,respectively.
基金supported by the National Natural Science Foundation of China (61503392)。
文摘This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.
