• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.249-255
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• 2007

Under the situation of a fire, it is difficult for a rescue robot to use sensors such as vision sensor, ultrasonic sensor or laser distance sensor because of diffusion, refraction or block of light and sound by dense smoke. But, braille blocks that are installed for the visaully impaired at public places such as subway stations can be used as a map for autonomous mobile robot's localization and navigation. In this paper, we developed a laser sensor stan device which can detect braille blcoks in spite of dense smoke and integrated the device to the robot developed to carry out rescue mission in various hazardous disaster areas at KIST. We implemented MCL algorithm for robot's attitude estimation according to the scanned data and transformed a braille block map to a topological map and designed a nonlinear path tracking controller for autonomous navigation. From various simulations and experiments, we could verify that the developed laser sensor device and the proposed localization method are effective to autonomous tracking of braille blocks and the autonomous navigation robot system can be used for rescue under fire.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.259-266
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• 2015

This study presents new scheme for various walking pattern of biped robot under the limitted enviroments. We show that the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multilayer backpropagation neural network identification is simulated to obtain a learning control solution of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The main advantage of our scheme is that we do not require any knowledge about the system dynamic and nonlinear characteristic, and can therefore treat the robot as a black box. It is also shown that the neural network is a powerful control theory for various trajectory tracking control of biped robot with same learning-vase. That is, we do net change the control parameter for various trajectory tracking control. Simulation and experimental result show that the neural network is practically feasible and realizable for iterative learning control of biped robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.12-18
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• 2010

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.12
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• pp.995-1006
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• 1989

If robots have the ability to track the parts on a moving conveyor belt, the efficiency of the manipulation tasks will be increased. This paper presents a motion planning algorithm for conveyor tracking. Tracking trajectory of a robot manipulator is determined by belt speed, initial part position, and initial robot position. Torque limit, maximum velocity, maximum acceleration and maximum jerk are also taken into account. To obtain the tracking solution, the problem is converted to the linear quadratic tracking problem. We describe the manipulator dynamics as second order state equation using parametric functions. Constraints on torques and smoothness are converted to those on input and state variables. The solution of the state equation which minimizes the performance index is obtained by dynamic programming method. Numerical examples are then presented to demonstrate the utility of the motion planning method developed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.169-170
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• 2008

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.561-566
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• 2002

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.201.2-201.2
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.87-88
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• 2010

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.146-151
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• 2001

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.499-500
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• 2008