• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.6
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• pp.31-37
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• 2002

This paper presents a new motion control strategy for singularity avoidance in 6 DOF articulated robot manipulators, based on a speed limiting algorithm for joint positions and velocities. For a given task, the robot is controlled so that the joints move with acceptable velocities and positions within the reachable range of each joint by considering the velocity limit. This paper aims at the development of a new efficient method to control robot motion near and at singularities. The proposed method has focused on generating the optimal joint trajectory for a Cartesian end-effector path within the speed limit of each joint by using the speed limit avoidance as well as the acceleration/deceleration scheme. The proposed method was verified using MATLAB-based simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.703-709
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• 2000

This paper proposes a compliance control strategy for the robot manipulators accidentally interact-ing with an unknown environment. In this proposed method each in the diagonal stiffness matrix corre-sponding to the task coordinate in a Cartesian space is adaptively adjusted during contact along the corresponding axis based on the contact force with its environment. This method can be used for both unconstrained and constrained motions without any switching mechanism which often causes undesirable instability and/or vibrational motion of the end-effector. The experimental results show the effectiveness of the proposed method by employing a two link direct drive manipulator interacting with an unknown environment.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.898-904
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• 1987

A new control algorithm using the VSS theory is developed for accurate trajectory control of robot manipulators. This paper focuses on the implementation of VSS controller with smoothing laws in the design of effective tracking control for robotic arms. The VSS controller for multi-linkage robot arm is realized using balance condition and its simplification which possesses powerful smoothing capability to reduce or even remove undesirable chattering and meanwhile keep the robust characteristic to reject system uncertainties. The design principle of selecting different smoothing methods, which aims at achieving trade-off between smoothing and robust behaviors while considering the actual system constraints, is also given and further confirmed through experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.32-35
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• 1987

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a desired trajectory in spite of the presence of nonlinearties and parameter uncertainties in robot dynamic models. In this paper, an adaptive control scheme for a robot manipulator is proposed to design the self-tuning controller which combines the pole placement with the extended linearized perturbation model. And this control scheme has two components: a feadforward control and a feedback compensation control. Based on this, the controller is demonstrated by the simulation about position control of a three-link manipulator with payload and parameter uncertainty.

• Journal of Advanced Navigation Technology
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• v.10 no.2
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• pp.113-120
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• 2006

In this paper, a vision-based robust controller for tracking the desired trajectory a robot manipulator is proposed. The trajectory is generated to move the feature point into the desired position which the robot follows to reach to the desired position. To compensate the parametric uncertainties of the robot manipulator which contain in the control input, the robust controller is proposed. In addition, if there are uncertainties in the Jacobian, to compensate it, a vision-based robust controller which has control input is proposed as well in this paper. The stability of the closed-loop system is shown by Lyapunov method. The performance of the proposed method is demonstrated by simulations and experiments on a two degree of freedom 5-link robot manipulators.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.760-763
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• 1997

Using telesensor programming method, we control the space robot which has two 2-DOF manipulators. To make this control system, we devide total works by elemental operation. And we make a simulation system that can simulate sensors and robot. In the simulation system, we make a sensor data and robot paths by "Teaching by showing" method. And using these data, we control the real space robot. This off-line method can solve long time delay problem in teleoperation.operation.

• Journal of Mechanical Science and Technology
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• v.14 no.10
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• pp.1081-1088
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• 2000

In this paper, a compliance control strategy for robot manipulators that employs a self-adjusting stiffiness function is proposed. Based on the contact force, each entry of the diagonal stiffness matrix corresponding to a task coordinate in the operational space is adaptively adjusted during contact along the corresponding axis. The proposed method can be used for both the unconstrained and constrained motions without any switching mechanism which often causes undesirable instability and/or vibrational motion of the end-effector. The experimental results involving a two-link direct drive manipulator interacting with an unknown environment demonstrates the effectiveness of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.756-765
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• 2001

An effective dexterous motion control method of redundant robot manipulators based on neural optimization network is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption, and avoiding physical limits of actuator, while performing a given task. The method employs a neural optimization network with parallel processing capability, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure(JGMM) is proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is then controlled by neural optimization networks in such a way that 1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, 2) physical limits of joints are not violated, and 3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by JGMM. To show the validity of the proposed method, several numerical examples are illustrated.

• The Journal of Korea Robotics Society
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• v.10 no.3
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• pp.139-145
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• 2015

This paper proposed a method of cooperative control of three mobile robots for carrying an object placed on a floor together. Each robot moves to the object independently from its location to a pre-designated location for grasping the object stably. After grasping the common object, the coordination among the robots has been achieved by a master-slave mode. That is, a trajectory planning has been done for the master robot and the distances form the master robot to the two slave robots have been kept constant during the carrying operation. The localization for mobile robots has been implemented using the encoder data and inverse kinematics since the whole system does not have the slippage as much as a single mobile robot. Before the carrying operation, the lifting operations are implemented using the manipulators attached on the top of the mobile robots cooperatively. The real cooperative lifting and carrying operations are implanted to show the feasibility of the master-slave mode control based on the kinematics using the mobile manipulators developed for this research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.285-288
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• 2005

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose a neuro-adaptive controller for robot manipulators using the radial basis function network(RBFN) that is a kind of a neural network. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between the actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that the parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed neuro-adaptive controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.