• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1860-1861
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• 2006

In this paper, we design a robust controller of biped robot system with uncertainties, using recurrent neural network. In our proposed control system, we use the self-recurrent wavelet neural network (SRWNN). The SRWNN makes up for the weak points in wavelet neural network(WNN). While the WNN has fast convergence ability, it dose not have a memory. So the WNN cannot confront unexpected change of the system. However, the SRWNN, having advantage of WNN such as fast convergence, can easily encounter the unexpected change of the system. For stable walking control of biped robot, we use sliding mode control (SMC). Here, uncertainties are predicted by SRWNN. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out computer simulations with a biped robot model to verify the effectiveness of the proposed control system,.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.15-21
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• 2004

A number of Humanoids are introduced including ASIMO, HRP-2 Promet, Johnniee, Babybot, and KHR-2. Most researches are focused on the development of stable biped walking of Humanoids and it is not easy to endow an Humanoid with intelligence and service technology until now in the sense that the operation time of a Humanoid is limited less than 30 minutes even in the case that the battery is used only for the control of actuators in a Humanoid. In this paper, a brief survey on Humanoids is proposed and the concept of 'Network-based Humanoid', a Humanoid being able to provide intelligence for human-friendly services using ubiquitous networks, is introduced briefly.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2411-2413
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• 2003

A new small humanoid robot system is developed in this paper. The humanoid robot has total 20 DOFs : 6 DOFs in each legs, 3 DOFs in each arms, and 2 DOFs in head, 34cms in height, and 2kgs in weight. The robot has the following characteristics: (1) PDA as host controller (2) network-based joint controller (3) wireless camera attached in robot's head (4) mechanism design by CATIA and high speed laser prototyping (5) graphic MMI(Man-Machine Interface) utilizing the CATIA data. By using ADXL inclination sensor, we implement the rope swing with the robot leg motion as well as walking.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.518-523
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• 2015

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.354-362
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• 2010

This paper proposes a new method for a hierarchical analysis of human gait by dividing the motion into gait direction and gait posture using the tool of dynamic Bayesian network. Based on Factorial HMM (FHMM), which is a type of DBN, we design the Gait Motion Decoder (GMD) in a circular architecture of state space, which fits nicely to human walking behavior. Most previous studies focused on human identification and were limited in certain viewing angles and forwent modeling of the walking action. But this work makes an explicit and separate modeling of pedestrian pose and posture to recognize gait direction and detect orientation change. Experimental results showed 96.5% in pose identification. The work is among the first efforts to analyze gait motions into gait pose and gait posture, and it could be applied to a broad class of human activities in a number of situations.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2279-2284
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• 2005

Generally, biped walking is difficult to control because it is a nonlinear system with various uncertainties. In this paper, we design a robust control system based on sliding-mode control (SMC) of 5-link biped robot using the wavelet neural network(WNN), in order to improve the efficiency of position tracking performance of biped locomotion. In our control system, the WNN is utilized to estimate uncertain and nonlinear system parameters, where the weights of WNN are trained by adaptive laws that are induced from the Lyapunov stability theorem. Finally, the effectiveness of the proposed control system is verified by computer simulations.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.894-899
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• 2008

Measured sensor datum from a quadruped robotics is commonly used for recognizing physical environment information which controls the posture of robotics. We can advance the ambulation with this sensed information and need to synthesize various sensors for obtaining accurate data, but most of these sensors are expensive and require excessive load for the operation. Those defects can be serious problem when it comes to the prototype's practicality and mass production, and maintenance of the system. This paper suggests virtual sensor technology for avoiding previous defects and presents ways to apply a theory to a walking robotics through virtual sensor information which is trained with several kinds of actual sensor information from the prototype system; the general algorithm is initially based on the neural network theory of back propagation. In specific, we verified a possibility of replacing the virtual sensor with the actual one through a reaction force measurement experiment.

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.242-248
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• 2007

Robot system development consists of several sub-tasks such as layout design, motion planing, and sensor programming etc. In general, on-line programming and debugging for such tasks demands burdensome time and labor costs, which motivates an off-line graphic simulation system. MSRS(Microsoft Robotics Studio) released in recent years is an appropriate tool for the graphic simulation system since it supports CCR(Concurrency and Coordination Runtime), DSS(Decentralized System Services), and dynamics simulation based on PhysX and graphic animation as well. In this paper, we developed an MSRS based network simulation system for quadruped walking robots, which controls virtual 3D graphic robots existing in remote side through internet.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1946-1948
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• 2001

본 논문에서는 저급 관성 센서를 이용한 개인 항법 장치의 모델 및 오차 보정 기법을 제시하고 성능 평가를 위하여 시뮬레이션을 수행하였다. 걸음 검출에 의한 보행 항법에서 중요한 변수인 보폭은 신경 회로망(Neural Network)을 이용하여 결정하였고, 자이로 바이어스 등에 의하여 누적되는 오차는 GPS와의 결합에 의하여 추정, 보상하였다. 이때 GPS와의 결합은 칼만필터를 이용하였으며 칼말필터를 구성하는데 필요한 오차 모델 및 결합 방법을 제시하였다. WNS/GPS 결합에 의하여 오차의 발산을 막을 수 있으며 GPS신호가 중간에 단절되는 경우에도 오차가 발산하지 않고 좋은 결과를 유지함을 보인다.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.259-266
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• 2015

This study presents new scheme for various walking pattern of biped robot under the limitted enviroments. We show that the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multilayer backpropagation neural network identification is simulated to obtain a learning control solution of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The main advantage of our scheme is that we do not require any knowledge about the system dynamic and nonlinear characteristic, and can therefore treat the robot as a black box. It is also shown that the neural network is a powerful control theory for various trajectory tracking control of biped robot with same learning-vase. That is, we do net change the control parameter for various trajectory tracking control. Simulation and experimental result show that the neural network is practically feasible and realizable for iterative learning control of biped robot.