• 제어로봇시스템학회논문지
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• 제22권7호
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• pp.562-569
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• 2016

This paper proposes a navigation system with a robust localization method for an underwater unmanned vehicle. For robust localization with IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and depth sensors, the EKF (Extended Kalman Filter) has been utilized to fuse multiple nonlinear data. Note that the GPS (Global Positioning System), which can obtain the absolute coordinates of the vehicle, cannot be used in the water. Additionally, the DVL has been used for measuring the relative velocity of the underwater vehicle. The DVL sensor measures the velocity of an object by using Doppler effects, which cause sound frequency changes from the relative velocity between a sound source and an observer. When the vehicle is moving, the motion trajectory to a target position can be recorded by the sensors attached to the vehicle. The performance of the proposed navigation system has been verified through real experiments in which an underwater unmanned vehicle reached a target position by using an IMU as a primary sensor and a DVL as the secondary sensor.

• 제어로봇시스템학회논문지
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• 제13권2호
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• pp.114-120
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• 2007

This paper introduces a mobile robotic system being developed for urban search and rescue. In order to search human victims in narrow spaces, we developed two types of serially linked mobile robots, named KAEROT-Centipede and KAEROT-SnakeTV1, that can climb over large vertical steps or travel inside narrow vertical pipes. To send such mobile robots to the disaster areas coping with large obstacles, we also developed a assistant mobile robot, named KAEROT-QuadTrack, that has 4 articulated track modules. This paper describes the mechanical structure and control architecture of the serially linked mobile robots and the supporting configuration for torque reduction of the assistant mobile robot during spinning motion that usually requires large driving torque. The experimental results show that such robotic systems have good mobility over the various terrains in disaster areas.

• 제어로봇시스템학회논문지
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• 제14권12호
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• pp.1197-1204
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• 2008

This paper presents an asymptotic formation control scheme for a group of underactuated autonomous underwater vehicles (AUVs) where only three control inputs - surge force, yaw moment and pitch moment are available for each vehicle's six degree of freedom (DOF) underwater motion. Usually, the dynamics agents applied in most of the formation algorithms presented so far have been modeled as particle systems, which is a simple double-integrator system. Therefore, these algorithms cannot be directly applicable to the practical systems, especially to the underwater vehicles whose dynamics are highly nonlinear. Moreover, the vehicles considered in this paper are underactuated. The formation control is derived using general potential function method, and the corresponding potential function consists of two parts: interactions between vehicles and virtual-leader following. Proposed formation scheme guarantees asymptotic local stability of closed-loop system. Numerical simulations are carried out to illustrate the effectiveness of proposed formation scheme.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.448-457
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• 1998

A neural network based control algorithm with fuzzy compensation is proposed for the automated adjustment in the production of electronic end-products. The process of adjustment is to tune the variable devices in order to examine the specified performances of the products ready prior to packing. Camcorder is considered as a target product. The required test and adjustment system is developed. The adjustment system consists of a NNC(neural network controller), a sub-NNC, and an auxiliary algorithm utilizing the fuzzy logic. The neural network is trained by means of errors between the outputs of the real system and the network, as well as on the errors between the changing rate of the outputs. Control algorithm is derived to speed up the learning dynamics and to avoid the local minima at higher energy level, and is able to converge to the global minimum at lower energy level. Many unexpected problems in the application of the real system are resolved by the auxiliary algorithms. As the adjustments of multiple items are related to each other, but the significant effect of performance by any specific item is not observed. The experimental result shows that the proposed method performs very effectively and are advantageous in simple architecture, extracting easily the training data without expertise, adapting to the unstable system that the input-output properties of each products are slightly different, with a wide application to other similar adjustment processes.

• 제어로봇시스템학회논문지
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• 제3권4호
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• pp.363-372
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• 1997

In this paper, the design and the implementation of the adaptive learning rate neural network controller for an articulate robot, which is being developed (or) has been developed in our Automatic Control Laboratory, are mainly discussed. The controller reduces software computational load via distributed processing method using multiple CPU's, and simplifies hardware structures by the time-division control with TMS32OC31 DSP chip. Proposed neural network controller with adaptive learning rate structure using expert's heuristics can improve learning speed. The proposed controller verifies its superiority by comparing response characteristics of conventional controller with those of the proposed controller that are obtained from the experiments for the 5 axis vertical articulated robot. We, also, present the generalization property of proposed controller for unlearned trajectory and the change of load through experimental data.

• 제어로봇시스템학회논문지
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• 제21권1호
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• pp.65-69
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• 2015

A vision-based blank alignment unit for a press automation line is introduced in this paper. A press is a machine tool that changes the shape of a blank by applying pressure and is widely used in industries requiring mass production. In traditional press automation lines, a mechanical centering unit, which consists of guides and ball bearings, is employed to align a blank before a robot inserts it into the press. However it can only align limited sized and shaped of blanks. Moreover it cannot be applied to a process where more than two blanks are simultaneously inserted. To overcome these problems, we developed a press centering unit by means of vision sensors for press automation lines. The specification of the vision system is determined by considering information of the blank and the required accuracy. A vision application S/W with pattern recognition, camera calibration and monitoring functions is designed to successfully detect multiple blanks. Through real experiments with an industrial robot, we validated that the proposed system was able to align various sizes and shapes of blanks, and successfully detect more than two blanks which were simultaneously inserted.

• 제어로봇시스템학회논문지
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• 제21권1호
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• pp.7-13
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• 2015

Unmanned air vehicles (UAVs) are getting popular not only as a private usage for the aerial photograph but military usage for the surveillance, reconnaissance and supply missions. For an UAV to successfully achieve these kind of missions, geolocation (localization) must be implied to track an interested target or fly by reference. In this research, we adopted multi-sensor fusion (MSF) algorithm to increase the accuracy of the geolocation and verified the algorithm using two multicopter UAVs. One UAV is equipped with an optical camera, and another UAV is equipped with an optical camera and a laser range finder. Throughout the experiment, we have obtained measurements about a fixed ground target and estimated the target position by a series of coordinate transformations and sequential Kalman filter. The result showed that the MSF has better performance in estimating target location than the case of using single sensor. Moreover, the experimental result implied that multi-sensor geolocation algorithm is able to have further improvements in localization accuracy and feasibility of other complicated applications such as moving target tracking and multiple target tracking.

• 로봇학회논문지
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• 제11권3호
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• pp.127-132
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• 2016

For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.

• 로봇학회논문지
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• 제8권1호
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• pp.20-28
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• 2013

Large workspace and strong grasping force are required when a robot manipulates big and/or heavy objects. In that situation, bimanual manipulation is more useful than unimanual manipulation. However, the control of both hands to manipulate an object requires a more complex model compared to unimanual manipulation. Learning by human demonstration is a useful technique for a robot to learn a model. In this paper, we propose an imitation learning method of bimanual object manipulation by human demonstrations. For robust imitation of bimanual object manipulation, movement trajectories of two hands are encoded as a movement trajectory of the object and a force trajectory to grasp the object. The movement trajectory of the object is modeled by using the framework of dynamic movement primitives, which represent demonstrated movements with a set of goal-directed dynamic equations. The force trajectory to grasp an object is also modeled as a dynamic equation with an adjustable force term. These equations have an adjustable force term, where locally weighted regression and multiple linear regression methods are employed, to imitate complex non-linear movements of human demonstrations. In order to show the effectiveness our proposed method, a movement skill of pick-and-place in simulation environment is shown.

• 제어로봇시스템학회논문지
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• 제20권6호
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• pp.689-694
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• 2014

An SPLC (Safety Programmable Logic Controller) must be designed to meet the highest safety standards, IEEE 1E, and should guarantee a level of fault-tolerance and high-reliability that ensures complete error-free operation. In order to satisfy these criteria, I/O modules, communication modules, processor modules and bus modules of the SPLC have been configured in triple or dual modular redundancy. The redundant modules receive the same data to determine the final data by the voting logic. Currently, the processor of each rx module performs the voting by deciding on the final data. It is the intent of this paper to prove the improvement on the current system, and develop a voting system for multiple data on a system bus level. The new system bus protocol is implemented based on a TCN-MVB that is a deterministic network consisting of a master-slave structure. The test result shows that the suggested system is better than the present system in view of its high utilization and improved performance of data exchange and voting.