• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.289-290
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• 2009

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.10
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• pp.882-889
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• 2008

A humanoid is a robot with its overall appearance based on that of the human body. When the humanoid moves or walks, dynamic forces act on the body structure. Although the humanoid keeps the balance by using a precise control, the dynamic forces generate unexpected deformation or vibration and cause difficulties on the control. Generally, the structure of the humanoid is designed by the designer's experience and intuition. Then the structure can be excessively heavy or fragile. A humanoid design scenario for a systematic design is proposed to reduce the weight of the structure while sufficient strength is kept. Lower parts of the humanoid are selected to apply the proposed design scenario. Multi-body dynamics is employed to calculate the external dynamic forces on the parts and structural optimization is carried out to design the lower parts. Because structural optimization using dynamic forces directly is fairly difficult, linear dynamic response structural optimization using equivalent static loads is utilized. Topology and shape optimizations are adopted for two steps of initial and detailed designs, respectively. Various commercial software systems are used for analysis and optimization. Improved designs are obtained and the design results are discussed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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• pp.558-565
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• 2013

This paper describes a whole-body (human body's) motion generation scheme for an android robot that uses motion capture device and a nonlinear constrained optimization method. Because the captured motion data are based on global coordinates and the actors have different heights and different upper-lower body ratios, the captured motion data cannot be used directly for a humanoid robot. In this paper, we suggest a method for obtaining robot joint angles, which allow the resultant robot motion to be as close as possible to the captured human motion data, by applying a nonlinear constrained optimization method. In addition, the results are animated to demonstrate the similarity of the motions.