• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.825-829
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• 1993

As a part of the Department of Energy's Environmental Restoration and Waste Management Program, long-reach manipulators are being considered for the retrieval of waste from large storage tanks. Long-reach manipulators may have characteristics significantly different from those of typical industrial robots because of the flexibility of long links needed to cover the large workspace. To avoid structural vibrations during operation, control algorithms employing various types of shaping filters were investigated. A new approach that uses imbedded simulation was developed and compared with others. In the new approach, generation of joint trajectories considering link flexibility was also investigated.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.972-976
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• 2015

The objective of this research is to verify the quantitative efficiency of a bimanual robotic task. Bimanual robots can realize dexterous and complicated motions using two cooperating arms. However, its motion planning and control method are not simple for implementing flexible tasks such as assembly. In this paper, the proposed motion planning method is used to find an optimal solution satisfying a designed cost function and constraints with regard to the kinematics and redundancy of the bimanual robot. The simulation results show that the lifetime of the manipulator can be changed by the proposed cost function consisting of angular velocity and angular acceleration of each joint in the same assembly task.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2030-2039
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• 2001

Recently, various inpipe inspection robots are developed and its effective values are increased in industrial use. However, it is so difficult to make a inpipe inspection robot system which has flexible mobility and accuracy of inspection in pipelines. Especially, it is very important to know the exact crack position. In this paper, we are to present a lately developed inpipe inspection robot system which can resolve the above Problems. The robot is configured as an articulated structure like a snake. Two active driving vehicles are located in front and rear of the inspection robot respectively and passive modules such as a nondestructive testing module and a control module are chained between the active vehicles. Special feature of the robot system is a ground interface, which is able to show informations of robot and pipelines. By using this, so called virtual map in this paper, user is able to know the pipelines'feature and crack position.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.759-763
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.