• 전기학회논문지P
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• 제56권4호
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• pp.161-167
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• 2007

This paper introduces a dynamic walking control of biped walking robot using intelligent sensor interface and shows an intelligent control method for biped walking robot. For the dynamic walking control of biped walking robot, serious motion controllers are used. They are main controller(using INTEL80C296SA MPU), sub controller(using TMS320LF2406 DSP), sensor controller(using Atmega128 MPU) etc. The used sensors are gyro sensor, tilt sensor, infrared sensor, FSR sensor etc. For the feasibility of a dynamic walking control of biped walking robot, we use the biped walking robot which has twenty-five degrees of freedom(D.O.F.) in total. Our biped robot is composed of two legs of six D.O.F. each, two arms of five D.O.F. each, a waist of two D.O.F., a head of one D.O.F.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1464-1468
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• 2004

This paper presents a learning controller for repetitive gait control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured according to the walking period through the iterative learning, not calculating the complex dynamics of walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation with 12-DOF biped walking robot.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.538-538
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• 2000

This paper presents a learning controller for repetitive gate control of biped robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of teaming control to biped robotic motion is shown via dynamic simulation with 12 dof biped robot.

• 전기학회논문지P
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• 제59권1호
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• pp.29-34
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• 2010

This paper presents an intelligent learning controller for repetitive walking motion of biped walking robot. The proposed learning controller consists of an iterative learning controller and a direct learning controller. In the iterative learning controller, the PID feedback controller takes part in stabilizing the learning control system while the feedforward learning controller plays a role in compensating for the nonlinearity of uncertain biped walking robot. In the direct learning controller, the desired learning input for new joint trajectories with different time scales from the learned ones is generated directly based on the previous learned input profiles obtained from the iterative learning process. The effectiveness and tracking performance of the proposed learning controller to biped robotic motion is shown by mathematical analysis and computer simulation with 12 DOF biped walking robot.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.594-596
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• 2005

This paper present a learning controller for repetitive gate control of biped robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation and experimental results with 24 DOF biped robot.

• 제어로봇시스템학회논문지
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• 제17권3호
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• pp.269-276
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• 2011

This paper proposes a novel control method using phase plane for a hydraulic biped humanoid robot. In biped walking control, it is much more difficult to control the posture of a biped robot in the coronal plane because the supporting area formed by the both feet in the coronal plane is much narrower than that of the sagittal plane. When the biped robot walks stably, the phase portrait of the pelvis in the coronal plane makes an elliptical shape. From this point of view, we develop an ankle torque controller and a foot placement controller for tracking the desired phase portrait during walking. We design these controllers by using simulations of a simplified compass gait biped model to regulate the desired phase portrait of pelvis. The effectiveness of the proposed control method is proved through full-body dynamic walking simulations and real experiments of the SARCOS hydraulic biped humanoid.

• 제어로봇시스템학회논문지
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• 제18권3호
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• pp.258-266
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• 2012

This paper describes a biped walking algorithm for a hydraulic humanoid robot on inclined floors. To realize stable and robust biped walking, the walking algorithm was divided into five control strategies. The first is a joint position control strategy. This strategy is for tracking desired joint position trajectories with a gain switching. The second is a multi-model based ZMP (Zero Moment Point) control strategy for dynamic balance. The third is a walking pattern flow control strategy for smooth transition from step to step. The fourth is an ankle compliance control, which increases the dynamic stability at the moment of floor contact. The last is an upright pose control strategy for robust walking on an inclined floor. All strategies are based on simple pendulum models and include practical sensory feedback in order to implement the strategies on a physical robot. Finally, the performance of the control strategies are evaluated and verified through dynamic simulations of a hydraulic humanoid on level and inclined floors.

• 제어로봇시스템학회논문지
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• 제12권4호
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• pp.315-319
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• 2006

Support vector machines in biped humanoid robot are presented in this paper. The trajectory of the ZMP in biped walking robot poses an important criterion for the balance of the walking robots but complex dynamics involved make robot control difficult. We are establishing empirical relationships based on the dynamic stability of motion using SVMs. SVMs and kernel method have become very popular method for learning from examples. We applied SVM to model the practical humanoid robot. Three kinds of kernels are employed also and each result has been compared. As a result, SVM based on kernel method have been found to work well. Especially SVM with RBF kernel function provides the best results. The simulation results show that the generated ZMP from the SVM can be improve the stability of the biped walking robot and it can be effectively used to model and control practical biped walking robot.

• 드라이브 ㆍ 컨트롤
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• 제13권4호
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• pp.52-58
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• 2016

The mechanical design for biped walking of a humanoid robot doing the Argentina Tango is presented in this paper. Biped walking has long been studied in the area of robotic locomotion. The aim of this paper is to implement an Argentina Tango dancer-like walking motion with a humanoid robot by using a trajectory generation scheme. To that end, this paper uses blending polynominals whose parameters are determined based on PSO (Particle Swarm Optimization) according to conditions that make the most of the Argentina Tango's characteristics. For the stability of biped walking, the ZMP (Zero Moment Point) control method is used. The feasibility of the proposed scheme is evaluated by simulating biped walking with the 3D Simscape robot model. The simulation results show the validity and effectiveness of the proposed method.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.311-314
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• 1995

Biped locomotion can be simply modeled as a linear inverted pendulum mode. This model considers only the CG (center of gravity) of the entire system. But in real biped robot systems, the free-leg motion dynamics is not negligible. So if its dynamics is not considered in designing the reference CG motion, it is badly influence to the ZMP(zero moment point) position of the biped robot walking in the sagittal plane. Therefore, we modeled the biped locomotion similar to the linear inverted pendulum mode but considered the predetermined free-leg dynamics. To verify that the proposed biped locomotion is more stable than the linear inverted pendulum mode, we constructed a biped robot simulator and designed a serco controller to track both the reference motion of the free leg and the reference motion of CG of the biped robot using the computed torque control low. And through simulations, we verified that the proposed walking is better in stability than the one based on the linear inverted pendulum mode.