• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.782-788
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• 2000

In this paper, we present a new approach to visual feedback control using image-based visual servoing with stereo vision. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only around a desired location but also at other locations. The suggested technique can guide a robot manipulator to the desired location without providing a priori knowledge such as the relative distance to the desired location or the model of an object even when the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by experimental results and compared with conventional control methods for an assembly robot.

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.119-128
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• 2007

Emotion interaction between human and robot is an important element for natural interaction especially for service robot. We propose a hybrid emotion generation architecture and detailed design of reactive process in the architecture based on insight about human emotion system. Reactive emotion generation is to increase task performance and believability of the service robot. Experiment result shows that it seems possible for the reactive process to function for those purposes, and reciprocal interaction between different layers is important for proper functioning of robot's emotion generation system.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.20-27
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• 2014

In this paper, an exact reshaping method for the motor dynamics of a flexible-joint robot is proposed using an integral manifold approach. Obtaining the exact model for both motor-side and link-side dynamics of a flexible-joint robot is difficult due to its under-actuated nature and complex dynamics. Despite the simple structure of the motor-side dynamics, they are difficult to model accurately for a flexible-joint robot due to motor disturbances, especially when speed reducers such as harmonic drives are installed. An integral manifold feedback control (IMFC) is proposed to reshape the motor dynamics. Based on the integral manifold approach, it is theoretically proved that the IMFC reshapes motor dynamics exactly even with bounded disturbances such as motor friction. The performance of the proposed IMFC is verified experimentally using a single degree-of-freedom flexible-joint robot under gravity conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.252-258
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• 1997

This paper presents neural networks for hybrid position/force control which is a type of position and force control for robot manipulators. The performance of conventional hybrid position/force control is excellent in the case of the exactly-known dynamic model of the robot, but degrades seriously as the uncertainty of the model increases. Hence, the neural network control scheme is presented here to overcome such shortcoming. The introduced neural term is designed to learn the uncertainty of the robot, and to control the robot through uncertainty compensation. Further more, the learning rule of the neural network is derived and is shown to be effective in the sense that it requires neither desired output of the network nor error back propagation through the plant. The proposed scheme is verified through the simulation of hybrid position/force control of a 6-dof robot manipulator.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6034-6039
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• 2012

In this paper, this may appear to exacerbate it met slopes of the mobile robot moves to overcome this by driving can occur if the mobile robot system has its own sleep problems driving progress in until you hit the target and solvedriving straight driving safer model for adaptive fuzzy control method of mobile robot based control algorithm is proposed. First, we propose a model based adaptive fuzzy controller, if possible, the dynamics model of the mobile robot, including model-based controller is designed to determine if you can check the condition of the mobile robot climbing and driving the mobile robot to overcome the slope and the to overcome driving control. Enough considering the ground friction forces and ensure the stability of the mobile robot system and the disturbance compensation, etc. In this case, the controller design will be possible. In addition, the nonlinear model, the dynamic characteristics of the mobile robot control method of adaptive fuzzy control techniques in the design that you want to fully reflect Non-holonomic system of mobile robots and solve sleep problems, and will be useful enough, it was verified through computer simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.297-298
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• 2009

• The Journal of Korea Robotics Society
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• v.13 no.3
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• pp.174-181
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• 2018

For a mobile robot that travels along a terrain consisting of various geology, information on tire force and friction coefficient between ground and wheel is an important factor. In order to estimate the lateral force between ground and wheel, a lot of information about the model and the surrounding environment of the vehicle is required in conventional method. Therefore, in this paper, we are going to estimate lateral force through simple model (Minimal Argument Lateral Slip Curve, MALSC) using only minimum data with high estimation accuracy and to improve estimation reliability of the friction coefficient by using the estimated lateral force data. Simulation is carried out to analyze the correlation between the longitudinal and transverse friction coefficients and slip angles to design the simplified lateral force estimation model by analysing simulation data and to apply it to the actual field environment. In order to verify the validity of the equation, estimation results are compared with the conventional method through simulation. Also, the results of the lateral force and friction coefficient estimation are compared from both the conventional method and the proposed model through the actual robot running experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.760-768
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• 2007

This paper introduces an emotional behavior decision model based on linear system for intelligent service robots. An emotional model should make different behavior decisions according to the purpose of the robots. We propose an emotional behavior decision model which can change the character of intelligent service robots and make different behavior decisions although the situation and environment remain the same. We defined each emotional element such as reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics by state dynamic equations. The proposed system model is a linear dynamic system. If you want to add one external stimulus or behavior, you need to add just one dimensional vector to the matrix of external stimulus or behavior dynamics. The case of removing is same. The change of reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics also follows the same procedure. We implemented a cyber robot and an emotional head robot using 3D character for verifying the performance of the proposed emotional behavior decision model.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.288-295
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• 2011

The paper proposes a localization method of an underwater robot using Monte Carlo Localization(MCL) approach. Localization is one of the fundamental basics for autonomous navigation of an underwater robot. The proposed method resolves the problem of accumulation of position error which is fatal to dead reckoning method. It deals with uncertainty of the robot motion and uncertainty of sensor data in probabilistic approach. Especially, it can model the nonlinear motion transition and non Gaussian probabilistic sensor characteristics. In the paper, motion model is described using Euler angles to utilize the MCL algorithm for position estimation of an underwater robot. Motion model and sensor model are implemented and the performance of the proposed method is verified through simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.537-542
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• 2014

This paper proposes a control system to keep the balance of a unicycle robot. The robot consists of the disk and wheel, for balancing and driving respectively, and the tile angle is measured and used for balancing by the IMU sensor. A PID controller is designed based on a non-model based algorithm to prove that it is possible to control the unicycle robot without any approximated linear system model such as the sliding mode control algorithm. The PID controller has the advantage that it is simple to design the controller and it does not require an unnecessary complex formula. In this paper, assuming that the pitch and roll axis are dynamically decoupled, each of the two controllers are designed separately. A reaction wheel pendulum method is used for the control of the roll axis, that is, for balancing and an inverted pendulum concept is used for the control of the pitch axis. To confirm the performance of the proposed controllers using MATLAB Simulink, the dynamic equations of the robot are derived.