• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.164-172
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• 2000

To follow a moving target keeping a certain distance it is essential for a mobile robot to detect the target first and to measure its pose and velocity. This paper proposes a new solution for this problem using the accumulated ultrasonic image which is constructed by accumulating the returned ultrasonic signal along the time axis for a certain number of measurement periods. A moving target is separated by selecting the trajectory whose inclination is different from others in the imag since the inclination of a trajectory represents the relative speed of the target against the mobile robot. The proposed algorithm was implemented on a mobile robot and has shown that the robot follows a moving target successfully.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1068-1076
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• 2005

In order to increase the autonomous navigation capability of a mobile robot, it is very crucial to develop a method for the robot to be able to recognize a priori hon structured environmental characteristics. This paper proposes an ultrasonic sensor based real-time method for recognizing a priori known structured indoor environmental characteristics like a wall and comer Unlike the methods reported in the literature the information obtained from the sensor can be processed in real-time by extended Kalman filter to update estimations of the position and orientation of robot with respect to known environmental characteristics.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.338-343
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• 1999

In this paper, the fuzzy neural network for the obstacle avoidance, which consists of the straight-line navigation and the barrier elusion navigation, is proposed and examined. For the straight-line navigation, the fuzzy neural network gets two inputs, angle and distance between the line and the mobile robot, and produces one output, steering velocity of the mobile robot. For the barrier elusion navigation, four ultrasonic sensors measure the distance between the barrier and the mobile robot and provide the distance information to the network. Then the network outputs the steering velocity to navigate along the obstacle boundary. Training of the proposed fuzzy neural network is executed in a given environment in real-time. The weights adjusting uses the back-propagation of the gradient of error to be minimized. Computer simulations are carried out to examine the efficiency of the real time learning and the guiding ability of the proposed fuzzy neural network. It has been shown that the mobile robot that employs the proposed fuzzy neural network navigates more safely with and less trembling locus compared with the previous reported efforts.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.297-305
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• 1999

Autonomous mobile robots are required to achieve multiple goals while responding quickly to the dynamic environments. An appropriate robot control architecture, which clearly and systematically defines the relationship among the inputs, the processing functions and the outputs, thus needs to be embedded in the robot controller. This paper proposes a kind of hybrid control architecture which combines the key features of the two well-known robot control architectures; hierarchical and behavioral- based. The overall control architecture consists of three layers, i.e. the highest planner, the middle plan executor, and the lowest monitor and behavior-based controller. In the planned situation, only one behavior module is chosen by the logical coordinator in the plan executor according to the way point bin. In the exceptional situation, the central controller in the plan executor issues an additional control command to reach the planned way point. Several simulations and experiments with autonomous mobile robot show that the proposed architecture enables the robot controller to achieve the multiple sequential goals even in dynamic and uncertain environments.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1096-1101
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• 2009

In this paper, an embedded system has been designed for force control application to interact between a robot arm and a human operator. Force induced by the human operator is converted to the desired position information for the robot to follow. For smooth operations, the impedance force control algorithm is utilized to represent interaction between the robot and the human operator by filtering the force. To improve the performance of position control of the robot arm, a velocity term has been obtained and tested by several filters. A PD controller for position control has been implemented on an FPGA as well. Experimental studies are conducted with the ROBOKER to test the functionality of the designed hardware.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.289-293
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• 2006

In this paper, we developed a ground power supply system for a mobile robot moving in the constrained region. By using an external scan circuit through the electrodes, it is also possible to detect the absolute position and heading angle of the robot without any additional position sensors. Since the heavy weighted-battery for electric power and the expensive absolute position sensors are not necessary to the robot by using for proposed system, the mobile robot system becomes cost-effective and dynamically fast.

• Advances in robotics research
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• v.1 no.2
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• pp.171-183
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• 2014

Distributed planning and decision making can be beneficial from the robustness, adaptability and fault tolerance in multi-robot systems. Distributed mechanisms have not been employed in three dimensional transportation systems namely aerial and underwater environments. This paper presents a distributed cooperation mechanism on multi robot transportation problem in three dimensional environments. The cooperation mechanism is based on artificial capital market, a newly introduced market based negotiation protocol. In the proposed mechanism contributing in transportation task is defined as asset. Each robot is considered as an investor who decides if he is going to invest on some assets. The decision is made based on environmental constraint including fuel limitation and distances those are modeled as capital and cost. Simulations show effectiveness of the algorithm in terms of robustness, speed and adaptability.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.816-824
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• 2004

In this paper, the framework for accelerating the development of personal robots is presented, which includes the technology such as modularization with its own processing and standardization open to the other developers. Its basic elements are Module-D(Module of DRP I) characterized functionally and VM-D(Virtual Machine of DRP I) arbitrating Module-Ds. They can suggest the effective ways for integrating various robotic components and interfacing among them. Based on this framework, we developed a fully modularized personal robot called DRP I(Dynamically Reconfigurable Personal robot). Its hardware components are easily attached to and detached from the whole system. In addition, each software of the components is functionally distributed. For the materialization of the proposed idea, we mainly focus on the dynamically reconfigurable feature of DRP I.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.34-40
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• 2005

In this paper, we describe the modeling for the 3D robot laser scanning system consisting of a laser stripe projector, camera, and 5-DOF robot and propose its calibration method. Nonlinear radial distortion in the camera model is considered for improving the calibration accuracy. The 3D range data is calculated using the optical triangulation principle which uses the geometrical relationship between the camera and the laser stripe plane. For optimal estimation of the system model parameters, real-coded genetic algorithm is applied in the calibration process. Experimental results show that the constructed system is able to measure the 3D position within about 1mm error. The proposed scheme could be applied to the kinematically dissimilar robot system without losing the generality and has a potential for recognition for the unknown environment.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.698-701
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• 1996

A novel neural network control scheme is proposed to identify the inverse dynamic model of robot manipulator and to compensate for uncertainties in robot dynamics. The proposed controller is called reference compensation technique(RCT) by compensating at reference input trajectory. The proposed RCT scheme has many benefits due to the differences in compensating position and learning algorithm. Since the compensation is done outside the plant it can be applied to many control systems without modifying the inside controller. It performs well with low controller gain because the operating range of input values is small and the output of the neural network controller is amplified through the controller gain. The back-propagation algorithm is used to train and simulations of three link robot manipulator are carried out to prove the proposed controller's performances.