• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.666-671
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• 1993

A new robust control law is proposed for uncertain rigid robots and two composite robust control laws for flexible-joint manipulators which contain uncertainties. The uncertainty, is nonlinear and (possibly fast) time-varying. Therefore, the uncertain factors such as imperfect modeling, function, payload change, and external disturbances are all addressed. Based only on the possible bound of the uncertainty, a robust controller is constructed for the rigid counterpart of the flexible-joint robot Some feedback control terms are then added to the robust control law to stabilize the elastic vibrations at the joints. To show that the proposed composite robust control laws are indeed applicable to flexible-joint robots, a singular perturbation approach and the stability study based on Lyapunov function are proposed.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.94-99
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• 1993

In this paper, the method for navigation and obstacle avoidance of an autonomous mobile robot is proposed. It is based on the fuzzy inference system which enables to deal with imprecise and uncertain information, and on the neural network which enables to learn input and output pattern data obtained from ultrasonic sensors. For autonomous navigation, the wall-following navigation utilizing input and output data by an expert's control action is constructed. An approach by the neural network is developed for the obstacle avoidance because of the redundant input data. For an autonomous navigation, the fuzzy control and the control of the neural network are integrated and its feasibility is demonstrated by means of experiment.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.328-333
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• 1994

This paper presents a real-time calculation method to generate the trajectory of robot manipulator for the purpose of avoiding collision. In order to model 3-D workspace, we use octree which has been used for fast collision detection. The levels of octree are used as the cost function to represent the distance between the manipulator and the obstacles. This criterion is not exact, but, due to this, we can obtain the approximate feasible trajectory extremely quickly. We will show the effectiveness of our method with some simulation examples. For example, the proposed method can solve a problem within 1 second on Intel 80486 processor running at 33 MHz. It has taken more than half an hour with one of the previously proposed methods.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.324-327
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• 1994

In this paper, a new design method of variable structure model following control system(VSMFCS) for robot manipulators is proposed. The proposed controller overcomed reaching phase problem by using function augmenting scheme to the sliding surface. Therefore, it can be guaranteed that the overall system always has a robust property against parameter variations and external disturbances. Furthermore, the proposed controller does not use the model state, .chi.$_{m}$, different from other previous works. Regardless of not using the model state, the model following error dynamics, virtual dynamics, is shown to be globally exponentially stable. The efficiency of the proposed method has been demonstrated by an example.e.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.566-571
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• 1994

This paper presents in image-based visual servo control scheme for tracking a workpiece with a hand-eye coordinated robotic system using the fuzzy-neural-network. The goal is to control the relative position and orientation between the end-effector and a moving workpiece using a single camera mounted on the end-effector of robot manipulator. We developed a fuzzy-neural-network that consists of a network-model fuzzy system and supervised learning rules. Fuzzy-neural-network is applied to approximate the nonlinear mapping which transforms the features and theire change into the desired camera motion. In addition a control strategy for real-time relative motion control based on this approximation is presented. Computer simulation results are illustrated to show the effectiveness of the fuzzy-neural-network method for visual servoing of robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.40-43
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• 1996

An off-line programming, OLP, system is widely used in automation fines. To help an on-line robot system to carry out desirable tasks planned by the off-line simulation, an approach to the real-time communication is presented. The OLP system developed consists of a software, a host computer(PC), a SCARA robot body, four servo drivers, and four independent joint controllers. This study focuses on the software where real-time communication is included. The software, can be used in teaching, trajectory planning, real-time running, and performance evaluation. The evaluation of different control algorithms is one of the merits of the software. The software can give servo commands for task running. A comparison of generated and corresponding actual trajectories provides the evaluation of task performance. The safety, of the OLP system is ensured by alarming malfuntions of the system. The OLP system developed can reduce the teaching time and increase the user's convenience.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.739-742
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• 1996

Several figures representing velocity transmission from joint space to task space are analyzed and compared with each other. The figures include velocity ellipsoid derived from Jacobian matrix, scaled velocity ellipsoid derived from normalized joint velocities, polytope derived by numerical scaling, and polytopes derived by linear combinations of Jacobian column vectors. The results show that the optimal directions given by the measures are not the same and the conventional velocity ellipsoid is not good choice as optimization measure as far as the moving direction is concerned. Simulation examples for 3 d.o.f. redundant robot manipulators in 2-dimensional task space are given for comparison study.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.302-305
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• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.470-473
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• 1996

This paper presents an iterative learning control scheme for industrial manipulators. Based upon the frequency-domain analysis, the input update law of the learning controller is given together with a sufficient condition for the convergence of the iterative process in the frequency domain. The proposed learning control scheme is structurally simple and computationally efficient since it is independent joint control depending only on locally measured variables and it does not involve the computation of complicated nonlinear manipulator dynamics. Moreover, it is capable of canceling the unmodeled dynamics of the manipulator without even the parametric model. Several important aspects of the learning scheme inherent in the frequency-domain design are discussed and the control performance is demonstrated through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1140-1143
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• 1996

A new approach based on artificial potential function is proposed for the obstacle avoidance of redundant manipulators. Unlike the so-called "global" path planning method, which requires expensive computation for the path search before the manipulator starts to move, this new approach, "local" path planning, researches the path in real-time using the local distance information. Previous use of artificial potential function has exhibited local minima in some complex environments. This thesis proposes a potential function that has no local minima even for a cluttered environment. This potential function has been implemented for the collision avoidance of a redundant robot in Simulation. The simulation also employ an algorithm that eliminates collisions with obstacles by calculating the repulsive potential exerted on links, based on the shortest distance to object.