Browsing by Author "Hu, Chen-Hui"

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Deformation Simulation Based on Model Reduction with Rigidity Sampling
(2017-01-01)
Chien, Shuo-Ting
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Hu, Chen-Hui
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Huang, Cheng-Yang
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Tsai, Yu-Ting
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Lin, Wen-Chieh
Deformation simulation based on model reduction with rigidity-guided sampling
(2018-06-01)
Chien, Shuo-Ting
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Hu, Chen-Hui
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Huang, Cheng-Yang
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Tsai, Yu-Ting
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Lin, Wen-Chieh
The deformation results of previous model reduction methods with external forces applied show noticeable differences from full-scale finite element method (FEM) simulation. We found that data-driven approaches, specifically proper orthogonal decomposition, can be a solution to this nonlinear deformation simulation problem in the subspace. Nevertheless, off-line FEM simulation with an infinite number of possible input forces at different locations makes it infeasible if no prior information is given. We propose rigidity-guided sampling to efficiently select the points of application of forces (force sample points) to construct more effective and compact subspace bases, thereby improving the simulation accuracy of reduced deformable models with applied external forces and still retaining fast run-time performance. The key idea of our approach is that distinct deformations of an object at different force sample points can be estimated prior to FEM simulation. By selecting the force sample points with distinct deformations, the computational cost of off-line FEM simulation can be reduced significantly. Our run-time deformation results are much closer to the full-scale FEM simulation with external forces applied, compared to the results of using only the modal derivative bases while the speedup over full-scale simulation is still substantial.
Motion Sickness Simulation Based on Sensorimotor Control
(2018-05-01)
Hu, Chen-Hui
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Lin, Wen-Chieh
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資訊工程學系
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Department of Computer Science
Sensorimotor control is an essential mechanism for human motions, from involuntary reflex actions to intentional motor skill learning, such as walking, jumping, and swimming. Humans perform various motions according to different task goals and physiological sensory perception; however, most existing computational approaches for motion simulation and generation rarely consider the effects of human perception. The assumption of perfect perception (i.e., no sensory errors) of existing approaches restricts the generated motion types and makes dynamical reactions less realistic. We propose a general framework for sensorimotor control, integrating a balance controller and a vestibular model, to generate perception-aware motions. By exploiting simulated perception, more natural responses that are closer to human reactions can be generated. For example, motion sickness caused by the impairments in the function of the vestibular system induces postural instability and body sway. Our approach generates physically correct motions and reasonable reactions to external stimuli since the spatial orientation estimation by the vestibular system is essential to preserve balance. We evaluate our framework by demonstrating standing balance on a rotational platform with different angular speeds and duration. The generated motions show that either faster angular speeds or longer rotational duration cause more severe motion sickness. Our results demonstrate that sensorimotor control, integrating human perception and physically-based control, offers considerable potential for providing more human-like behaviors, especially for perceptual illusions of human beings, including visual, proprioceptive, and tactile sensations.
Motion Sickness Simulation Based on Sensorimotor Control
(2017-01-01)
Hu, Chen-Hui
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Chen, Ping
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Liu, Yan-Ting
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Lin, Wen-Chieh
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交大名義發表
Skiing Simulation Based on Skill-Guided Motion Planning
(2019-09-01)
Hu, Chen-Hui
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Lee, Chien-Ying
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Liou, Yen-Ting
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Sung, Feng-Yu
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Lin, Wen-Chieh
Skiing is a popular recreational sport, and competitive skiing has been events at the Winter Olympic Games. Due to its wide moving range in the outdoor environment, motion capture of skiing is hard and usually not a good solution for generating skiing animations. Physical simulation offers a more viable alternative. However, skiing simulation is challenging as skiing involves many complicated motor skills and physics, such as balance keeping, movement coordination, articulated body dynamics and ski-snow reaction. In particular, as no reference motions - usually from MOCAP data - are readily available for guiding the high-level motor control, we need to synthesize plausible reference motions additionally. To solve this problem, sports techniques are exploited for reference motion planning. We propose a physics-based framework that employs kinetic analyses of skiing techniques and the ski-snow contact model to generate realistic skiing motions. By simulating the inclination, angulation and weighting/unweighting techniques, stable and plausible carving turns and bump skiing animations can be generated. We evaluate our framework by demonstrating various skiing motions with different speeds, curvature radii and bump sizes. Our results show that employing the sports techniques used by athletes can provide considerable potential to generate agile sport motions without reference motions.