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손목오프셋을 갖는 6축 로봇을 위한 효과적인 역기구학 해 방법
범진환,임생기,손명현 대한기계학회 1994 대한기계학회논문집 Vol.18 No.6
An algorithm is developed for solving the inverse kinematic problem of a 6-degree-of-freedom robot with a wrist offset for which the closed form inverse solutions are not obtainable, but knowledge of one joint variable allows closed form solutions of the remaining joint variables. The algorithm does not require Forward Kinematics nor Jacobian but uses the implicit kinematic relationships between joint variables and the given hand position. An iterative back substitution method is used to solve the inversion and the optimal conditions of the convergence are incoporated. An example is given to illustrate the concepts, the solution procedure and its convergency.
박태원,범진환,한형석,김명규,김광배 대한기계학회 1993 대한기계학회논문집 Vol.17 No.1
본 연구에서는 첫째로 병진 캠 전용해석 및 설계 프로그램을 개발하였다. 이 개발된 프로그램들을 기구해석전용 프로그램에 접속시켜 VTR Deck에 대한 전반적인 해석 및 설계가 가능하도록 하였다. VTR(Video Tape Recorder) has very complicated mechanisms composed of various cams, links, gears and so on. To satisfy kinematic requirements of VTR components, various geometric constraints between rigid bodies and a translational cam design program are developed. Mechanisms of VTR are divided into functional groups like a control part, a loading part and a tape guide part. Each group is modeled for kinematic and dynamic analysis. Finally, all groups are combined together for a complete VTR model and loads required for each function of VTR controls are studied. Detailed description of developed programs are presented and result are discussed.
채장범,범진환,Kim Jung-Min 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.8
In this paper, an adaptive neural controller is proposed for visual servoing of robot manipulators with camera-in-hand configuration. The controller is designed as a combination of a PI kinematic controller and feedforward neural network controller that computes the required torque signals to achieve the tracking. The visual information is provided using the camera mounted on the end-effector and the defined error between the actual image and desired image positions is fed to the PI controller that computes the joint velocity inputs needed to drive errors in the image plane to zero. Then the feedforward neural network controller is designed such that the robot's joint velocities converges to the given velocity inputs. The stability of combined PI kinematic and feedforward neural network computed torque is proved by Lyapunov theory. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary off learning. Simulation results are carried out for a three degrees of freedom microbot robot manipulator to evaluate the controller performance.
Naveen Kumar,범진환,Vikas Panwar,채장범 한국정밀공학회 2012 International Journal of Precision Engineering and Vol. No.
In this paper, a hybrid trajectory tracking controller is designed for redundant robot manipulators, consisting of RBF neural network and an adaptive bound on disturbances. The controller is composed of computed torque type part, RBF neural network and an adaptive controller. The controller achieves end-effector trajectory tracking as well as subtask tracking effectively. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive controller is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable in the sense of Lyapunov. Finally, numerical simulation studies are performed on a 3R planar robot manipulator to show the effectiveness of the control scheme.