Design of human-like posture prediction for inverse kinematic posture control of a humanoid robot
Name
62860123-MIT.pdf
Description
Full printable version
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2.11 MB
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Checksum (MD5)
1d7723933a64935a82098d7e62026b4e
Author(s)
Thomann, Derik (Derik S.)
Advisor(s)
Cynthia Breazeal.
Date Issued
2005
Publisher
Massachusetts Institute of Technology
Abstract
A method and system has been developed to solve the kinematic redundancy for a humanoid redundant manipulator based on forward kinematic equation and the optimization of human-like constraints. The Multiple Objective Optimization (MOO) is preformed using a Genetic Algorithms (GA) and implemented using the Genetic and Evolutionary Algorithm Matlab Toolbox. The designed system is illustrated on a simple redundant 3 degree of freedom (dof) manipulator and is set up for a more complicated redundant 7 dof manipulator. The 7 dof manipulator is modeled from the Stan Winston studio's Leonardo, an 61 dof expressive humanoid robot. It has been found that the inverse kinematic solution to a 3dof model arm converged within 1% error of the solution within .05 mins processor time using the discomfort human-like constraint in 2d space. Similarly, the inverse kinematic solution to a 7 dof model arm consisting of Leonardo's right arm geometry was found to converge within 1% error within .20 mins processor time using the discomfort human-like constraint in 3D space. The full kinematic model of Leonardo is developed and future efficiency optimizations are posed to move towards the real-time motion control of a redundant humanoid robot by way of human-like posture prediction.
Description
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.
Includes bibliographical references (p. 53-54).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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