This thesis presents a new human-like upper limb and hand motion generating method. The work is based on Functional Principal Component Analysis and Quadratic Programming. The human-like motion generating problem is formulated in a framework of minimizing the difference of the dynamic profile of the optimal trajectory and the known types of trajectory. Statistical analysis is applied to the pre-captured human motion records to work in a low dimensional space. A novel PCA FPCA hybrid motion recognition method is proposed. This method is implemented on human grasping data to demonstrate its advantage in human motion recognition. One human grasping hierarchy is also proposed during the study. The proposed method of generating human-like upper limb and hand motion explores the ability to learn the motion kernels from human demonstration. Issues in acquiring motion kernels are also discussed. The trajectory planning method applies different weight on the extracted motion kernels to approximate the kinematic constraints of the task. Multiple means of evaluation are implemented to illustrate the quality of the generated optimal human-like trajectory compared to the real human motion records.
|Commitee:||Qian, Xiaoning, Sarkar, Sudeep|
|School:||University of South Florida|
|Department:||Engineering Computer Science|
|School Location:||United States -- Florida|
|Source:||MAI 52/04M(E), Masters Abstracts International|
|Subjects:||Computer Engineering, Robotics, Computer science|
|Keywords:||Functional data analysis, Humanoid, Learning by demonstration, Motion analysis, Motion capture|
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