Young-Mok Koo

An intelligent motion control of two wheel driving robot based voice recognition

Recently, it is possible to control the motion by the information on the robots own postures, because a type of motion and gesture produces almost the same pattern of noise every time. In this paper, we describe an voice recognition control system for robot system which can robustly recognize voice by adults and children in noisy environments. We evaluate the performance of robot control system in a communication robot placed in a real noisy environment. Voice is captured using a wireless microphone. To suppress interference and noise and to attenuate reverberation, we implemented a multi-channel system consisting of an outlier-robust generalized side-lobe canceller technique and a feature-space noise suppression using criteria. Voice activity periods are detected based end-point detection.

A Study on Intelligent Control of Real-Time Working Motion Generation of Bipped Robot

In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model 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 biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.Keywords : Learning control, Biped Robot, Neural Network, Real-Time

A study on visual feedback control of SCARA robot arm

This paper presents how it is effective to use many features for improving the accuracy of the visual servoing control for SCARA robot. Some rank conditions, which relate the image Jacobian, and the control performance are derived. It is also proven that the accuracy is improved by increasing the number of features. Effectiveness of the redundant features is verified by the real time experiments on a Dual-Arm Robot manipulator system.

A study on grasping control of hand fingers 12 joints

Recently it is very important to control robot hands more compact and integrated sensors in order to increase compensate the grasping capability and to reduce cabling through the finger in the manipulator. As a matter of fact, the miniaturization and cabling harness represents a significant limitation to the design of small sized precise sensor. The main focus of this research is on a flexible grasping control of hand fingers, which consists of a flexible multi-fingered hand-arm system.

A study on robust control of articulated robot arm with seven joints

In this paper, we present two kinds of robust control schemes for robot system which has the parametric uncertainties. In order to compensate these uncertainties, we use the neural network control system that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation and tune the parameters. We also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error. To reduce the number of neural of network, we consider the properties of robot dynamics and the decomposition of the uncertainty function. The proposed controllers are robust not only to the structured uncertainty such as payload parameter, but also to the unstructured one such as friction model and disturbance. The reliability of the control scheme is shown by computer simulations and experiment of robot manipulator with 7 axis.

A study on travelling control of Mobile robot by voice commend

In this paper, we describe an voice recognition control technology for Mobile robot system which can robustly recognize voice by adults and children in noisy environments. We evaluate the performance of robot control system in a communication robot placed in a real noisy environment. Voice is captured using a wireless microphone. To suppress interference and noise and to attenuate reverberation, we implemented a multi-channel system consisting of an outlier-robust generalized side-lobe canceller technique and a feature-space noise suppression using criteria. Voice activity periods are detected based end-point detection.

A study on travelling control of humanoid type mobile robot with three wheel

We Present a new technique to control of mobile robot for trajectory Tracking based fuzzy perception concept with robot named HMRO-I. The main focus of this paper is obtaining a fuzzy perception of the environment in the design of each reactive behavior and solving the problem of behavior combination to implement a fuzzy behavior based control architecture. It should be remarked that, the proposed technique of the nonholonomic constraints are considered in the design of each behavior. Furthermore, in order to improve the capabilities of the intelligent control system and its practical applicability, teleoperation and planned behaviors, together with their combination with reactive ones, have been considered. Experimental results, of an application to control the HMRO-I Robot autonomous vehicle, demonstrate the robustness of the proposed method.

A study on gripping control of robotic hand with ten joints for cooperative working

Recently it is very important to control robot hands more compact and precise sensors in order to increase compensate the grasping capability and to reduce cabling through the finger in the manipulator. As a matter of fact, the miniaturization and cabling harness represents a significant limitation to the design of small sized precise sensor. The main focus of this research is on a robust grasping control of hand fingers, which consists of a flexible multi-fingered with ten joints.

A Study on Precise Control of Industrial Robot Arm for Manufacturing Process Automation

In this paper, we present two kinds of robust control schemes for robot system which has the parametric uncertainties. In order to compensate these uncertainties, we use the neural network control system that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation and tune the parameters. We also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error. To reduce the number of neural of network, we consider the properties of robot dynamics and the decomposition of the uncertainty function. The proposed controllers are robust not only to the structured uncertainty such as payload parameter, but also to the unstructured one such as friction model and disturbance. The reliability of the control scheme is shown by computer simulations and experiment of robot manipulator with 7 axis.