The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that...The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that in the grasping process and,different control algorithm should be taken in the two process. A position-force hybrid control algorithm is proposed which is applied to the control system of the University of Science and Technology Beijing double-thumb robot hand successfully.展开更多
Gives an overview of the present status of researches on grasp stability of multi fingered dexterous robot hands,presents the imaginary displacement method for evaluating the grasp stability, which is easy to realize...Gives an overview of the present status of researches on grasp stability of multi fingered dexterous robot hands,presents the imaginary displacement method for evaluating the grasp stability, which is easy to realize on computer,and has no limit on contact points for each finger. Analyses for grasping stability with single contact point of typical objects with different curvature proved the effectiveness of the method proposed and optimal grasp examples are given as well.展开更多
This paper focuses on multi-modal Information Perception(IP)for Soft Robotic Hands(SRHs)using Machine Learning(ML)algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS)is fabricated,consisting of a Light-Emi...This paper focuses on multi-modal Information Perception(IP)for Soft Robotic Hands(SRHs)using Machine Learning(ML)algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS)is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical fiber.Bending the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft finger.Together with the curvature and pressure information,multi-modal IP is performed to improve the recognition accuracy.Recognitions of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs)of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA)of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI)are adopted to compare the recognition accuracies.The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective.The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations.Moreover,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.展开更多
文摘The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that in the grasping process and,different control algorithm should be taken in the two process. A position-force hybrid control algorithm is proposed which is applied to the control system of the University of Science and Technology Beijing double-thumb robot hand successfully.
文摘Gives an overview of the present status of researches on grasp stability of multi fingered dexterous robot hands,presents the imaginary displacement method for evaluating the grasp stability, which is easy to realize on computer,and has no limit on contact points for each finger. Analyses for grasping stability with single contact point of typical objects with different curvature proved the effectiveness of the method proposed and optimal grasp examples are given as well.
基金support provided by the National Natural Science Foundation of China (Nos. 61803267 and 61572328)the China Postdoctoral Science Foundation (No.2017M622757)+1 种基金the Beijing Science and Technology program (No.Z171100000817007)the National Science Foundation of China (NSFC) and the German Re-search Foundation (DFG) in the project Cross Modal Learning,NSFC 61621136008/DFG TRR-169
文摘This paper focuses on multi-modal Information Perception(IP)for Soft Robotic Hands(SRHs)using Machine Learning(ML)algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS)is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical fiber.Bending the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft finger.Together with the curvature and pressure information,multi-modal IP is performed to improve the recognition accuracy.Recognitions of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs)of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA)of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI)are adopted to compare the recognition accuracies.The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective.The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations.Moreover,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.
