Graduation Year

2011

Document Type

Thesis

Degree

M.S.Cp.E.

Degree Granting Department

Computer Science and Engineering

Major Professor

Yu Sun, Ph.D.

Committee Member

Dmitry Goldgof, Ph.D.

Committee Member

Xiaoning Qian, Ph.D.

Committee Member

Sudeep Sarkar, Ph.D.

Keywords

Motion Analysis, Motion Mapping, Motion Capture, Principal Component Analysis, Functional Data Analysis

Abstract

With the rise autonomous and robotic systems in field applications, the need for dexterous, highly adaptable end effectors has become a major research topic. Control mechanisms of robotics hands with a high number independent actuators is recognized as a complex, high dimensional problem, with exponentially complex algorithms. However, recent studies have shown that human hand motion possesses very high joint correlation which translates into a set of predefined postures, or synergies. The hand produces a motion using a complementing contribution of multiple joints, called synergies. The similarities place variables onto a common dimensional space, effectively reducing the number of independent variables.

In this thesis, we analyze the motion of the hand during a set of objects grasps using mul- tivariate Principal Component Analysis (mPCA) to extract both the principal variables and their correlation during grasping. We introduce the use of Functional PCA (fPCA) primarily on princi- pal components to study the dynamic requirements of the motion. The goal is to defined a set of synergies common and specific to all motions. We expand the analysis by classifying the objects grasps, or tasks, using their functional components, or harmonics over the entire motion. A set of groups are described based on these classification that confirms empirical findings. Lastly, we evaluate the motions generated from the analysis by applying them onto robotic hands. The results from the mPCA and fPCA procedures are used to map the principal components from each motion onto underactuated robotic designs. We produce a viable routine that indicates how the mapping is performed, and finally, we implement the motion generated onto a real hand. The resultant robotic motion was evaluated on how it mimics the human motion.

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