• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.188-196
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• 2006

During the communication and interaction with a human using motions or gestures, a humanoid robot needs not only to look like a human but also to behave like a human to make sure the meanings of the motions or gestures. Among various human-like behaviors, arm motions of the humanoid robot are essential for the communication with people through motions. In this work, a mathematical representation for characterizing human arm motions is first proposed. The human arm motions are characterized by the elbow elevation angle which is determined using the position and orientation of human hands. That representation is mathematically obtained using an approximation tool, Response Surface Method (RSM). Then a method to generate human-like arm motions in real time using the proposed representation is presented. The proposed method was evaluated to generate human-like arm motions when the humanoid robot was asked to move its arms from a point to another point including the rotation of its hand. The example motion was performed using the KIST humanoid robot, MAHRU.

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.282-287
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• 2007

Humanoid and android robots are emerging as a trend shifts from industrial robot to personal robot. So human-robot interaction will increase. Ultimate objective of humanoid and android would be a robot like a human. In this aspect, implementation of robot's facial expression is necessary in making a human-like robot. This paper proposes a dynamic emotion model for a mascot-type robot to display similar facial and more recognizable expressions.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2154-2159
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• 2005

This paper presents the kinematic analysis of two robots, an exoskeleton type master robot and a human like slave robot with two arms. Two robots are designed and built to be equivalent as motion following robots. The operator wears the exoskeleton robot to generate motions, then the slave robot is required to follow after the motion of the master robot. However, different kinematic configuration yields position mismatches of the end-effectors. To synchronize motions of two robots, kinematic analysis of mapping is analyzed. The forward and inverse kinematics have been simulated and the corresponding experiments are also conducted to confirm the proposed mapping analysis.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.287-299
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• 2008

People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1210-1216
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• 2004

This paper deals with a human-like gait generation of a biped robot with a balancing weight of an inverted pendulum type by using genetic algorithm. The ZMP (Zero Moment Point) is the most important index in a biped robot's dynamic walking stability. To perform a stable walking of a biped robot, a balancing motion is required according to legs' trajectories and a desired ZMP trajectory. A dynamic equation of the balancing motion is nonlinear due to an inverted pendulum type's balancing weight. To solve the nonlinear equation by the FDM (Finite Difference Method), a linearized model of equation is proposed. And GA (Genetic Algorithm) is applied to optimize a human-like balancing motion of a biped robot. By genetic algorithm, the index of the balancing motion is efficiently optimized, and a dynamic walking stability is verified by the ZMP verification equation. These balancing motion are simulated and experimented with a real biped robot IWR-IV. This human-like gait generation will be applied to a humanoid robot, at future work.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.107.4-107
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• 2002

One of the most important functions of a biped robot is to walk naturally like human. For the human being, toe is very important joint in order to walk naturally. Thus, for a biped robot, the existence of toe joint much affects gait pattern generation and contributes to natural walking, which is similar to the human gait or faster walking like running. Since a conventional biped robot has the feet which consist of soles without toes, it seems difficult to walk naturally. For realizing the gait to be similar to human one, toes are necessary to the biped robot. In this paper, the effect of the toe joint for gait pattern generation is studied. In order to find the effect of toe joint, a biped r...

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.659-667
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• 2011

The study of bipedal robot is towards similar shape and function with human. In this paper, we propose a human-like walking pattern compatible to the flexible foot with toe and heel structure. The new walking pattern for a bipedal robot consists of ZMP, center of mass (CoM), and ankle trajectory and is drawn by considering human kinesiology. First, the ZMP trajectory moves forward without stopping at a point even in the single support phase. The corresponding CoM trajectory to the ZMP one is derived by solving differential equations. As well, a CoM trajectory for the vertical axis is added by following the idea of human motion. The ankle trajectory closely mimics the rotational motion of human ankles during taking off and landing on the ground. The advantages of the proposed walking pattern are demonstrated by showing improved stability, decreased ankle torque, and the longer step length capability. Specifically, it is interesting to know that the vertical CoM motion is able to compensate for the initial transient response.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.37-38
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• 2007

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.132-140
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• 1999

This paper treats the estimation of human impedance and their application to human-robot collaboration work. Initially, we perform an experiment at which the human becomes a slave and the robot behaves like a master having F/T sensor on its end. The human impedance expressed in terms of mass, damping, and stiffness properties are estimated based on the force data measured by F/T sensor and the commanded position data of the robot. To show the effectiveness of the estimated human impedance, we perform the second experiment at which the roles of the human and the robot are reversed. It is shown that the robot using the estimated human impedance follows the trajectory commanded by human very smoothly.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.490-493
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• 1996

This paper presents a control scheme for the motion of a 2 DOF robot manipulator. Robot manipulators are multivariable nonlinear systems. Fuzzy logic is avaliable human-like control without complex mathematical operation and is suitable to nonlinear system control. In this paper, Implementation of fuzzy logic control of robotic manipulators shows. Algorithm has been performed with simulation packages MATRIXx and SystemBuild.