On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dyna...On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer.Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.展开更多
As humans and robots work closer together than ever,anthropomorphic robotic arms with intuitive human-robot interaction interfaces have drawn massive attention to improving the quality of robot-assisted manipulation.I...As humans and robots work closer together than ever,anthropomorphic robotic arms with intuitive human-robot interaction interfaces have drawn massive attention to improving the quality of robot-assisted manipulation.In pursuit of this,we designed a dedicated 7-degrees-of-freedom(DoF)anthropomorphic robotic arm having three compact differential joints and a head-mounted gaze tracker enabling head-pose-tracked 3D gaze estimation.Moreover,two key challenges were addressed to achieve accurate robot-assisted manipulation of the object indicated by the direction of human gaze.First,a novel predictive pupil feature was proposed for 3D gaze estimation.Differing from most existing features subjected to the common paraxial approximation assumption,the proposed novel predictive pupil feature considered the light refraction at two corneal surfaces with a more realistic eye model,significantly improving the 3D gaze estimation accuracy when the eyeball rotates at large angles.Second,a novel optimization-based approach was developed to efficiently compensate for the posture errors of the designed 7-DoF anthropomorphic robotic arm for accurate manipulation.Compared with the existing Jacobian-based or optimization-based approaches with nominal joint values as iteration initial,the proposed approach computed the optimal iteration initial and realized faster convergence for real-time posture error compensation.With the posture error compensation in real time and 3D gaze estimated accurately,the human can command accurate robot-assisted manipulation using his eyes intuitively.The proposed system was successfully tested on five healthy subjects.展开更多
基金National Natural Science Foundation of China (52375412)Fundamental Research Funds for Central Universities (N2203011)China Scholarship Council Program (202306080057)。
文摘On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer.Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.
基金supported by the National Natural Science Foundation of China(Grant Nos.52027806,52435005,92248304,52075191).
文摘As humans and robots work closer together than ever,anthropomorphic robotic arms with intuitive human-robot interaction interfaces have drawn massive attention to improving the quality of robot-assisted manipulation.In pursuit of this,we designed a dedicated 7-degrees-of-freedom(DoF)anthropomorphic robotic arm having three compact differential joints and a head-mounted gaze tracker enabling head-pose-tracked 3D gaze estimation.Moreover,two key challenges were addressed to achieve accurate robot-assisted manipulation of the object indicated by the direction of human gaze.First,a novel predictive pupil feature was proposed for 3D gaze estimation.Differing from most existing features subjected to the common paraxial approximation assumption,the proposed novel predictive pupil feature considered the light refraction at two corneal surfaces with a more realistic eye model,significantly improving the 3D gaze estimation accuracy when the eyeball rotates at large angles.Second,a novel optimization-based approach was developed to efficiently compensate for the posture errors of the designed 7-DoF anthropomorphic robotic arm for accurate manipulation.Compared with the existing Jacobian-based or optimization-based approaches with nominal joint values as iteration initial,the proposed approach computed the optimal iteration initial and realized faster convergence for real-time posture error compensation.With the posture error compensation in real time and 3D gaze estimated accurately,the human can command accurate robot-assisted manipulation using his eyes intuitively.The proposed system was successfully tested on five healthy subjects.
