• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.31-38
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• 2024

A mobile manipulator is capable of handling a wide range of workspaces by overcoming the limitations of mobility inherent in existing fixed-base manipulators. To simulate the mobile manipulator, two contact operations should be considered in the physics engines. One of these operations is the grasp stability between the gripper and the object, while the other involves the contact between the wheels of the mobile robot and the ground during driving. However, it is still difficult to choose an appropriate physics engine for simulating these contact operations of the mobile manipulator. In this paper, the performance of physics engines for simulating the mobile manipulator is evaluated. Firstly, the grasp stability of the physics engine is quantitatively evaluated based on the contact force discontinuity. Secondly, when the mobile robot is controlled by open or closed-loop control methods, differences in the path taken by the mobile robot depending on the physics engine are analyzed. To assess the performance of robot simulation, three dynamic simulators-MuJoCo, CoppeliaSim, and IsaacSim-are used along with five physics engines: MuJoCo, Newton, ODE, Bullet, and PhysX.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1181-1184
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• 1996

As usual systems, robot manipulators have also physical constraints for operating. It is a difficult problem that we operate manipulator in the minimal time under these constraints. In this paper, we solve this problem dividing it into two steps. In the first step, we find the minimal time trajectories by optimizing qubic polynomial joint trajectories using evolutionary algorithms. In the second step, we optimize controller for robot manipulator to track precisely trajectories optimized in the previous step.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.490-493
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• 1996

This paper presents a control scheme for the motion of a 2 DOF robot manipulator. Robot manipulators are multivariable nonlinear systems. Fuzzy logic is avaliable human-like control without complex mathematical operation and is suitable to nonlinear system control. In this paper, Implementation of fuzzy logic control of robotic manipulators shows. Algorithm has been performed with simulation packages MATRIXx and SystemBuild.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1455-1458
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• 1996

In this paper trajectory tracking control problems are described for a robot manipulator by using pneumatic actuator. Under the assumption that the so-called independent joint control is applied to the control system, the dynamic model for each link is identified as a linear second-order system with input time-delay by the step response. Then, an optimal servo controller is designed by taking account of such a time-delay. The effectiveness of the proposed control method is illustrated through some simulations and experiments for the robot manipulator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.263-266
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• 1997

In this paper we will develope the immnized recurrent neural network control system of a robot manipulator with high robustness in dynamically changing environment conditions. Immune system detects and eliminates the non-self materials called antigen such as virus, bacteria and so on which come from inside and outside of the living system, so plays an important role in maintaining its own system against dynamically changing environments. We apply this concept to a robot manipulator and evaluate the effectiveness of the above proposed system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.267-270
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• 1997

As an approach to develope a control system with robustness in changing control environment conditions, this paper will propose a robot manipulator control system using multilayer neural network and immune algorithm. The proposed immune algorithm which has the characteristics of immune system such as distributed and anomaly detection, probabilistic detection, learning and memory, consists of the innate immune algorithm and the adaptive immune algorithm. We will demonstrate the effectiveness of the proposed control system with simulations of a 2-link robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.188-193
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• 1989

This paper is proposed of multirate nonlinear controller for robot manipulator. The proposed controller is obtained by structure changes of feedback controller with C.T.M and for time differences commanded in caculating each term of controller, multirate sampling is used. And more robust controller is proposed by considering one-step ahead predictive action. In order to evaluate proposed controller, computer simulation is performed for a 3 D.O.F robot manipulator with varying load.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.642-646
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• 1992

Trajectory tracking control problems are described for a two-link robot manipulator with artificial rubber muscle actuators. Under the assumption that the so-called independent joint control is applied to the control system, the dynamic model for each link is identified as a linear second-order system with time-lag by the step response. Two control laws such as the feedforward and the computed torque control methods, are experimentally applied for controlling the circular trajectory of an actual robot manipulator.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.716-719
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• 1999

In this paper, a real-time control system for robot manipulator is implemented using real-time operating system with capabilities of multitasking, intertask communication and synchronization, event-driven, priority-driven scheduling, real-time clock control, etc. The hardware system with VME bus and related devices is developed and applied to implement a dynamic learning control scheme for robot manipulator. Real-time performance of the proposed dynamic learning controller is tested for tasks of tracking moving objects and compared with the conventional servo controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.519-526
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• 1990

In this paper we propose the resolved motion controller using a neural network for a robot manipulator. Neural identifier designed by a neural network is trained by using a feedback force as an error signal. The identifier approximates the output of a unknown nonlinear system by monitoring both the input and the output of this system. If the neural network is sufficiently trained well, it does not require either strict modelling of the manipulator or precise parameter estimation. The effectiveness of the proposed controller is demonstrated by computer simulation using a two-link planar robot.