• Title/Summary/Keyword: visual servoing method

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Visual Servoing of manipulator using feature points (특징점을 이용한 매니퓰래이터 자세 시각 제어)

  • 박성태;이민철
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2004.10a
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    • pp.1087-1090
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    • 2004
  • stereo vision system is applied to a mobile manipulator for effective tasks. The robot can recognize a target and compute the position of the target using a stereo vision system. In this paper we persent a visual approach to the problem of object grasping. First we propose object recognization method which can find the object position and pose using feature points. A robot recognizes the feature point to Object. So a number of feature point is the more, the better, but if it is overly many, the robot have to process many data, it makes real-time image processing ability weakly. In other to avoid this problem, the robot selects only two point and recognize the object by line made by two points. Second we propose trajectory planing of the robot manipulator. Using grometry of between object and gripper, robot can find a goal point to translate the robot manipulator, and then it can grip the object successfully.

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A Study on Visual Servoing Image Information for Stabilization of Line-of-Sight of Unmanned Helicopter (무인헬기의 시선안정화를 위한 시각제어용 영상정보에 관한 연구)

  • 신준영;이현정;이민철
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2004.10a
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    • pp.600-603
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    • 2004
  • UAV (Unmanned Aerial Vehicle) is an aerial vehicle that can accomplish the mission without pilot. UAV was developed for a military purpose such as a reconnaissance in an early stage. Nowadays usage of UAV expands into a various field of civil industry such as a drawing a map, broadcasting, observation of environment. These UAV, need vision system to offer accurate information to person who manages on ground and to control the UAV itself. Especially LOS(Line-of-Sight) system wants to precisely control direction of system which wants to tracking object using vision sensor like an CCD camera, so it is very important in vision system. In this paper, we propose a method to recognize object from image which is acquired from camera mounted on gimbals and offer information of displacement between center of monitor and center of object.

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A Co-Evolutionary Approach for Learning and Structure Search of Neural Networks (공진화에 의한 신경회로망의 구조탐색 및 학습)

  • 이동욱;전효병;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.111-114
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    • 1997
  • Usually, Evolutionary Algorithms are considered more efficient for optimal system design, However, the performance of the system is determined by fitness function and system environment. In this paper, in order to overcome the limitation of the performance by this factor, we propose a co-evolutionary method that two populations constantly interact and coevolve. In this paper, we apply coevolution to neural network's evolving. So, one population is composed of the structure of neural networks and other population is composed of training patterns. The structure of neural networks evolve to optimal structure and, at the same time, training patterns coevolve to feature patterns. This method prevent the system from the limitation of the performance by random design of neural network structure and inadequate selection of training patterns. In this time neural networks are trained by evolution strategies that are able to apply to the unsupervised learning. And in the coding of neural networks, we propose the method to maintain nonredundancy and character preservingness that are essential factor of genetic coding. We show the validity and the effectiveness of the proposed scheme by applying it to the visual servoing of RV-M2 robot manipulators.

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3D Feature Based Tracking using SVM

  • Kim, Se-Hoon;Choi, Seung-Joon;Kim, Sung-Jin;Won, Sang-Chul
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1458-1463
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    • 2004
  • Tracking is one of the most important pre-required task for many application such as human-computer interaction through gesture and face recognition, motion analysis, visual servoing, augment reality, industrial assembly and robot obstacle avoidance. Recently, 3D information of object is required in realtime for many aforementioned applications. 3D tracking is difficult problem to solve because during the image formation process of the camera, explicit 3D information about objects in the scene is lost. Recently, many vision system use stereo camera especially for 3D tracking. The 3D feature based tracking(3DFBT) which is on of the 3D tracking system using stereo vision have many advantage compare to other tracking methods. If we assumed the correspondence problem which is one of the subproblem of 3DFBT is solved, the accuracy of tracking depends on the accuracy of camera calibration. However, The existing calibration method based on accurate camera model so that modelling error and weakness to lens distortion are embedded. Therefore, this thesis proposes 3D feature based tracking method using SVM which is used to solve reconstruction problem.

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Structure Pruning of Dynamic Recurrent Neural Networks Based on Evolutionary Computations (진화연산을 이용한 동적 귀환 신경망의 구조 저차원화)

  • 김대준;심귀보
    • Journal of the Korean Institute of Intelligent Systems
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    • v.7 no.4
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    • pp.65-73
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    • 1997
  • This paper proposes a new method of the structure pruning of dynamic recurrent neural networks (DRNN) using evolutionary computations. In general, evolutionary computations are population-based search methods, therefore it is very useful when several different properties of neural networks need to be optimized. In order to prune the structure of the DRNN in this paper, we used the evolutionary programming that searches the structure and weight of the DRNN and evolution strategies which train the weight of neuron and pruned the net structure. An addition or elimination of the hidden-layer's node of the DRNN is decided by mutation probability. Its strategy is as follows, the node which has mhnimum sum of input weights is eliminated and a node is added by predesignated probability function. In this case, the weight is connected to the other nodes according to the probability in all cases which can in- 11:ract to the other nodes. The proposed pruning scheme is exemplified on the stabilization and position control of the inverted-pendulum system and visual servoing of a robot manipulator and the effc: ctiveness of the proposed method is demonstrated by numerical simulations.

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Study on vision-based object recognition to improve performance of industrial manipulator (산업용 매니퓰레이터의 작업 성능 향상을 위한 영상 기반 물체 인식에 관한 연구)

  • Park, In-Cheol;Park, Jong-Ho;Ryu, Ji-Hyoung;Kim, Hyoung-Ju;Chong, Kil-To
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.4
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    • pp.358-365
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    • 2017
  • In this paper, we propose an object recognition method using image information to improve the efficiency of visual servoingfor industrial manipulators in industry. This is an image-processing method for real-time responses to an abnormal situation or to external environment change in a work object by utilizing camera-image information of an industrial manipulator. The object recognition method proposed in this paper uses the Otsu method, a thresholding technique based on separation of the V channel containing color information and the S channel, in which it is easy to separate the background from the HSV channel in order to improve the recognition rate of the existing Harris Corner algorithm. Through this study, when the work object is not placed in the correct position due to external factors or from being twisted,the position is calculated and provided to the industrial manipulator.

Evolution of Neural Network's Structure and Learn Patterns Based on Competitive Co-Evolutionary Method (경쟁적 공진화법에 의한 신경망의 구조와 학습패턴의 진화)

  • Joung, Chi-Sun;Lee, Dong-Wook;Jun, Hyo-Byung;Sim, Kwee-Bo
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.36S no.1
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    • pp.29-37
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    • 1999
  • In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.

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