• Journal of the Korean Society for Precision Engineering
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• v.25 no.3
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• pp.141-146
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• 2008

The purpose of this study is to develop the algorithm which can control muscle power assist robot especially for elderly. Recently, wearable robots for power assistance are developed by many researchers, and its application fields are also variable such as for medical or military equipment. However, there are many technical barriers to develop the wearable robot. This study suggest a control method improving performance of a wearable robot system by using a EMG signal of major muscles and a force sensor signal as command signal of system. The result of the robot Prototype efficiency experiment, the case of Maximum Isometric motion it suggest 100% power of muscle, the man need only 66% of MVIC(Maximum Voluntary Isometric Contraction) to lift 5kg dumbbell without robot assist. However the man needs only 52% of MVIC to lift 5kg dumbbell with robot assist. Therefore 20% muscle power increased with robot assist. Also, we designed light weight robot mechanism that extract the command signal verified and drive the wanted motions.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.81-86
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• 2004

This paper describes self-localization of a mobile robot from the multiple candidates of landmarks in outdoor environment. Our robot uses omnidirectional vision system for efficient self-localization. This vision system acquires the visible information of all direction views. The robot uses feature of landmarks whose size is bigger than that of others in image such as building, sculptures, placard etc. Robot uses vertical edges and those merged regions as the feature. In our previous work, we found the problem that landmark matching is difficult when selected candidates of landmarks belonging to region of repeating the vertical edges in image. To overcome these problems, robot uses the merged region of vertical edges. If interval of vertical edges is short then robot bundles them regarding as the same region. Thus, these features are selected as candidates of landmarks. Therefore, the extracted merged region of vertical edge reduces the ambiguity of landmark matching. Robot compares with the candidates of landmark between previous and current image. Then, robot is able to find the same landmark between image sequences using the proposed feature and method. We achieved the efficient self-localization result using robust landmark matching method through the experiments implemented in our campus.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.85.4-85
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• 2001

A learning controller is presented for repetitive walking motion of biped robot. The learning control scheme learns the approximate inverse dynamics input of biped walking robot and uses the learned input pattern to generate an input profile of different walking motion from that learnt. In the learning controller, the PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. The proposed learning control scheme is ...

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.632-637
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• 1992

Recently many dynamics control algorithms using robot dynamic equation have been proposed. One of them, Kawato's feedback error learning scheme requires neither an accurate model nor parameter estimation and makes the robot motion closer to the desired trajectory by repeating operation. In this paper, the feedback error learning algorithm is implemented to control a robot system, 5 DOF revolute type movemaster. For this purpose, an actuator dynamic model is constructed considering equivalent robot dynamics model with respect to actuator as well as friction model. The command input acquired from the actuator dynamic model is the sum of products of unknown parameters and known functions. To compute the control algorithm, a parallel processing computer, transputer, is used and real-time computing is achieved. The experiment is done for the three major link of movemaster and its result is presented.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.145-153
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• 2000

This paper discusses the dynamic modeling and robust control development for a differentially steered mobile robot subject to wheel slip according to high load. Consideration of wheel slip is crucial for high load applications such as construction automation tasks because wheel slip acts as a severe disturbance to the system. It is shown that the uncertainty terms due to the wheel slip satisfy the matching condition for the sliding mode control design. From the full dynamic model of the mobile robot, a reduced ideal model is extracted to facilitate the control design. The sliding mode control method ensures the dynamic tracking performance for such a mobile robot. Numerical simulation shows the promise of the developed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.501-507
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• 2013

This paper presents the development and balancing control of GYROBO, a one wheeled mobile robot system. GYROBO is a disc type one wheel mobile robot that has three actuators, a drive motor, a spin motor, and a tilt motor. The dynamics and kinematics of GYROBO are analyzed, and simulation studies conducted. A one-wheeled robot, GYROBO is built and its balancing control is performed. Experimental studies of GYROBO's balancing abilities are conducted to demonstrate the gyroscopic effects generated by the spin and tilt angles of a flywheel.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1031-1035
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• 1993

In this paper, we suggest a new algorithm diminishing the chattering in sliding mode control by setting a dead-band along the switching line on the phase plane although nonlinear terms of an nonlinear system are regarded as disturbances and apply this algorithm to the trajectory control of SCARA robot By this algorithm, we can expect the high performance of the trajectory trajet of an industrial robot which needs a robust and simple algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.341-345
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• 1986

In the course of robot control system building, there are problems in the position control loop of 3rd axis of robot manipulator. The problems are summerized as two: one is uncontrollability of position and the other is oscillation. And these problems are analyzed through experiment, and it is known that the cause of problems in torsional vibration of 3rd axis. So that these two problems are solved by noise immunity enhancement and lowering of PI controller gain.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.74-74
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• 2000

This paper presents a neural network controller for a rigid-link electrically-driven robot. The proposed controller is designed in conjunction with three neural networks approximating for complicated nonlinear functions. Particularly, the fact, different from conventional schemes, is that the neural network based current observer is used. Therefore, no accurate measurement of the actuator driving current is required. In the proposed controller-observer scheme, the derived weight update rule guarantees the stability of closed-loop system in the sense of Lyapunov. The effectiveness and performance of the proposed method are demonstrated through computer simulation.