HyungJoo Kim

Multi-objective Optimization for Upper Body Posture Prediction

*† ‡ § ** The demand for realistic autonomous virtual humans is increasing, with potential application to prototype design and analysis for a reduction in design cycle time and cost. In addition, virtual humans that function independently, without input from a user or a database of animations, provide a convenient tool for biomechanical studies. However, development of such avatars is limited. In this paper, we capitalize on the advantages of optimization-based posture prediction for virtual humans. We extend this approach by incorporating multi-objective optimization (MOO) in two capacities. First, the objective sum and lexicographic approaches for MOO are used to develop new human performance measures that govern how an avatar moves. Each measure is based on a different concept with different potential applications. Secondly, the objective sum, the min-max, and the global criterion methods are used as different means to combine these performance measures. It is found that although using MOO to combine the performance measures generally provides reasonable results especially with a target point located behind the avatar, there is no significant difference between the results obtained with different MOO methods.

Modeling Deformation-Induced Fluid Flow in Cortical Bone's Canalicular-Lacunar System

To explore the potential role that load-induced fluid flow plays as a mechano–transduction mechanism in bone adaptation, a lacunar–canalicular scale bone poroelasticity model is developed and implemented. The model uses micromechanics to homogenize the pericanalicular bone matrix, a system of straight circular cylinders in the bone matrix through which bone fluids can flow, as a locally anisotropic poroelastic medium. In this work, a simplified two-dimensional model of a periodic array of lacunae and their surrounding systems of canaliculi is used to quantify local fluid flow characteristics in the vicinity of a single lacuna. When the cortical bone model is loaded, microscale stress, and strain concentrations occur in the vicinity of individual lacunae and give rise to microscale spatial variations in the pore fluid pressure field. Furthermore, loading of the bone matrix containing canaliculi generates fluid pressures in the contained fluids. Consequently, loading of cortical bone induces fluid flow in the canaliculi and exchange of fluid between canaliculi and lacunae. For realistic bone morphology parameters, and a range of loading frequencies, fluid pressures and fluid–solid drag forces in the canalicular bone are computed and the associated energy dissipation in the models compared to that measured in physical in vitro experiments on human cortical bone. The proposed model indicates that deformation-induced fluid pressures in the lacunar–canalicular system have relaxation times on the order of milliseconds as opposed to the much shorter times (hundredths of milliseconds) associated with deformation-induced pressures in the Haversian system.

Use of multi-objective optimization for digital human posture prediction

With sufficient fidelity, the use of virtual humans can save time, money, and lives through improved product design, process design, and understanding of behaviour. Optimization-based posture prediction is a unique tool, and this article presents a study that advances posture prediction with a multi-objective optimization (MOO) approach. MOO is used to both develop and combine the following human performance measures: joint displacement; musculoskeletal discomfort; and a variation on potential energy. The following MOO methods are studied in the context of human modelling: objective sum; min–max; and global criterion. Using MOO yields realistic results. Of the independent performance measures, discomfort generally provides the most accurate postures. Potential energy, however, is not a significant factor in governing human posture and should be combined with other performance measures. The three MOO methods for combining performance measures yield similar results, but the objective sum provides slightly more realistic postures.

Santos™: A New Generation of Virtual Humans

Abstract : Presented in this paper is an on-going project to develop a new generation of virtual human models that are highly realistic in terms of appearance, movement, and feedback (evaluation of the human body during task execution). Santos(Trademark) is an avatar that exhibits extensive modeling and simulation capabilities. It is an anatomically correct human model with more than 100 degrees of freedom. Santos(Trademark) resides in a virtual environment and can conduct human-factors analysis. This analysis entails, among other things, posture prediction, motion prediction, gain analysis, reach envelope analysis, and ergonomics studies. There are essentially three stages to developing virtual humans: (1) basic human modeling (representing how a human functions independently); (2) input functionality (awareness and analysis of the humans environment); and (3) intelligent reaction to input (memory, reasoning, etc.). This paper addresses the first stage. Specifically, we discuss a new human model in terms of mechanics and appearance. We present an optimization-based approach to kinematic and dynamic motion analysis. This approach allows the avatar to operate with complete autonomy rather than with dependence on stored animations and data, or restrictions associated with inverse kinematics. With dynamic analysis, it is not necessary to solve equations of motion. A novel approach for determining reach envelopes is also presented, and this approach provides a unique tool for ergonomic studies. Methods for evaluating the physiological status of the virtual human as tasks are completed are discussed. Finally, additional on-going research is summarized. The result is an exciting step towards a virtual human that is more extensive and more complete than any other.

VFI-based Robotic Arm Control for Natural Adaptive Motion

Since neural oscillator based control methods can generate rhythmic motion without information on system dynamics, they can be a promising alternative to traditional motion planning based control approaches. However, for field application, they still need to be robust against unexpected forces or changes in environments so as to be able to generate “natural motion” like most biological systems. In this study a biologically inspired control algorithm that combines neural oscillators and virtual force is proposed. This work gives the condition with respect to parameters tuning to stably activate the neural oscillators. This is helpful to achieve motion adaptability to environmental changes keeping the motion repeatability. He efficacy and efficiency of the proposed methods are tested in the control of a planar three-linkage robotic arm. It is shown that the proposed controller generates a given circular path stably and repeatedly, even with unexpected contact with a wall. The adaptivity of motion control is also tested in control of a robotic arm with redundant degrees of freedom. The proposed control algorithm works throughout the simulations and experiments.

Biologically Inspired Self-Stabilizing Control for Bipedal Robots

Despite recent major advances in computational power and control algorithms, the stable and robust control of a bipedal robot is still a challenging issue due to the complexity and high nonlinearity of robot dynamics. To address the issue an efficient and powerful alternative based on a biologically inspired control framework employing neural oscillators is proposed and tested. In a numerical test the virtual force controller combined with the neural oscillator of a humanoid robot generated rhythmic control signals and stable bipedal locomotion when coupled with proper impedance components. The entrainment nature inherent to neural oscillators also achieved stable and robust walking even in the presence of unexpected disturbances, in that the centre of mass (COM) was successfully kept in phase with the zero moment point (ZMP) input trajectory. The efficiency of the proposed control scheme is discussed alongside simulation results.

Theoretical analysis and design for a multilayered ionic polymer metal composite actuator

Ionic polymer metal composites with a flexible large deformation have been used as biomimetic actuators and sensors in various fields. This work mainly focuses on the validation of the proposed theoretical prediction for various ionic polymer metal composite applications, such as a field needing a large resultant force, large tip deflection, or high response frequency. Such properties can be controlled by the number of layers and the thickness ratio of a multilayered ionic polymer metal composite actuator. Thus, we considered major design factors such as the number of layers and the thickness ratio in analysis of the proposed theoretical model and performed experiments to verify the static and dynamic electromechanical responses of multilayered (multimorph) ionic polymer metal composite structures acting as actuators. The relation between the polymer (Nafion) and electrode or substrate is represented by β. From this theoretical analysis, three properties were analyzed and predicted based on the Euler–Bernoulli beam theory, considering the dynamics of the ionic polymer metal composite, electrode, and bonding layers (substrate layers). The predicted results of a symmetric ionic polymer metal composite multimorph were compared with results of finite element analysis and experiments using ionic polymer metal composite multimorphs with one to five layers. Finally, this work examined how the number of layers and thickness affect the dynamic properties. This can contribute to predicting and optimally designing a multilayered ionic polymer metal composite actuator for satisfying a specific requirement.

The enhanced performance of a robotic arm control based on neural oscillator networks

While the central pattern generator (CPG) controller based on biologically inspired neural oscillator networks is known to achieve repeatability and entrainment of rhythmic output motion, no systematic approach to implement the controller has been provided so far. In this study a series of schemes are introduced to tune the control parameters and to set up the network coupling of artificial neural oscillators combined with the virtual force control. Application of the schemes to the proposed controller for a three-link planar robot arm demonstrates that the optimized controller with proper neural oscillator networks can produce minimize the energy consumption with an enhanced-entrained motion with the change of a desired motion and interaction of unknown environments.

Multi-Scale Micro-Mechanical Poroelastic Modeling of Fluid Flow in Cortical Bone

To explore the potential role that load-induced fluid flow plays as a mechano-transduction mechanism in bone adaptation, a lacunar-canalicular scale bone poroelasticity model is developed and exercised. The model uses micromechanics to homogenize the pericanalicular bone matrix, a system of straight circular cylinders in the bone matrix through which bone fluids can flow, as a locally anisotropic poroelastic medium. In this work, a simplified two-dimensional model of a periodic array of lacunae and their surrounding systems of canaliculi is developed and exercised to quantify local fluid flow characteristics in the vicinity of a single lacuna. When the cortical bone model is loaded, microscale stress and strain concentrations occur in the vicinity of individual lacunae and give rise to microscale spatial variations in the pore fluid pressure field. Consequently, loading of cortical bone can induce fluid flow in the canaliculi and exchange of fluid between canaliculi and lacunae. For realistic bone morphology parameters, and a range of loading frequencies, fluid pressures and fluid-solid shear stresses in the canalicular bone are computed and the associated energy dissipation in the models compared to that measured in physical in vitro experiments on human cortical bone. For realistic volume fractions of canaliculi, deformation-induced fluid flow is found to have a much larger characteristic time constant than deformation-induced flow in the Haversian system.Copyright

Finite Deformation Micromechanical Analysis of Textile-Reinforced Plastics

Nonlinear elastic stiffness behaviors of plain-weave textile-reinforced composites are considered in this work by modeling finite deformation effects at two scales: (1) at the fiber diameter scale within yarns (~10 microns); (2) at the yarn diameter scale within woven textiles (~1000 microns). To capture the effect of heterogeneous microscale stress and strain fields, symmetric, conjugate, stress and strain measures are first established. A transversely isotropic hyperelasticity model is then presented for modeling finite deformation behaviors of yarns. After the free parameters of this model are estimated using unit cell analysis at the fiber-diameter scale, it is then incorporated into plain-weave textile unit cell model. The textile mode is then subjected to finite strain deformation controlled loading to quantify nonlinearity in stiffness behaviors.© 2003 ASME