• 제어로봇시스템학회논문지
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• 제15권6호
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• pp.619-625
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• 2009

FastSLAM which uses the Rao-Blackwellized particle filter is one of the famous solutions to SLAM (Simultaneous Localization and Mapping) problem that estimates concurrently a robot's pose and surrounding environment. However, the particle depletion problem arises from the loss of the particle diversity in the resampling process of FastSLAM. Then, the performance of FastSLAM degenerates over the time. In this work, DIR (Density Information-based Resampling) technique is proposed to solve the particle depletion problem. First, the cluster is constructed based on the density of each particle, and the density of each cluster is computed. After that, the number of particles to be reserved in each cluster is determined using a linear method based on the distance between the highest density cluster and each cluster. Finally, the resampling process is performed by rejecting the particles which are not selected to be reserved in each cluster. The performance of the DIR proposed to solve the particle depletion problem in FastSLAM was verified in computer simulations, which significantly reduced both the RMS position error and the feature error.

• 제어로봇시스템학회논문지
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• 제22권9호
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• pp.745-752
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• 2016

We suggest an efficient Simultaneous Localization and 3D Polygon Map Building (SLAM) method with Kinect depth sensor for mobile robots in indoor environments. In this method, Kinect depth data is separated into row planes so that scan line segments are on each row plane. After grouping all scan line segments from all row planes into line groups, a set of 3D Scan polygons are fitted from each line group. A map matching algorithm then figures out pairs of scan polygons and existing map polygons in 3D, and localization is performed to record correct pose of the mobile robot. For 3D map-building, each 3D map polygon is created or updated by merging each matched 3D scan polygon, which considers scan and map edges efficiently. The validity of the proposed 3D SLAM algorithm is revealed via experiments.

• 대한물리치료과학회지
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• 제8권2호
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• pp.1059-1064
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• 2001

This study is aimed at diagnosing transmutation aspect with the respective of hemiplegia patient's static adaptation of posture which is influenced by anti-gravity excercise, center of gravity shifting movement and segmental movement adaption of the legs and arms by key-point control in the process of Bobatli's treatment approach. The patients for the investigation of this study were selected as a total 17 patients who were doing the physical-therapy in Tae-gu Rehabilitation Center, and diagnosed as hemiplegia patients by Rehabilitation Medicine department in Kyung-buk University hospital. And also, there investigated into temporal transition that keep the attitude with one-leg standing on the ground concerning static attitude adaption on the basis of the before of anti-gravity movement and the after of 4-weeks movement. The findings of this study were as follows: With the respective the time to keep standing pose by one-leg at static attitude transmutation, affected side showed meaningful differences as a l.86/sec, 2.62/sec at 4 weeks later considering the before and after of this experiment.(p<0.01), non-affected side at the aspect of attitude keeping time, but there didn't showed meaningful differences statistically.

• 한국산업융합학회 논문집
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• 제20권1호
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• pp.49-55
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• 2017

This study proposes a new approach to Control and trajectory generation of a 8 DOF human robot arm with computational complexity and singularity problem. To deal with such problems, analytical methods for a redundant robot arm have been researched to enhance the performance of research, we propose an analytical kinematics algorithm for a 8 DOF bipped dual robot arm. Using this algorithm, it is possible to generate a trajectory passing through the singular points and intuitively move the elbow without regarding to the end-effector pose. Performance of the proposed algorithm was verified by simulation test with various conditions. It has been verified that the trajectory planning using this algorithm.

• 로봇학회논문지
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• 제9권3호
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.393-398
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• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• 제어로봇시스템학회논문지
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• 제20권11호
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• pp.1153-1163
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• 2014

An efficient 3D SLAM (Simultaneous Localization and Map Building) method is developed for urban building environments using a tilted 2D LRF (Laser Range Finder), in which a 3D map is composed of perpendicular/horizontal planar polygons. While the mobile robot is moving, from the LRF scan distance data in each scan period, line segments on the scan plane are successively extracted. We propose an "expected line segment" concept for matching: to add each of these scan line segments to the most suitable line segment group for each perpendicular/horizontal planar polygon in the 3D map. After performing 2D localization to determine the pose of the mobile robot, we construct updated perpendicular/horizontal infinite planes and then determine their boundaries to obtain the perpendicular/horizontal planar polygons which constitute our 3D map. Finally, the proposed SLAM algorithm is validated via extensive simulations and experiments.

• 제어로봇시스템학회논문지
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• 제19권4호
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• pp.372-378
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• 2013

In this paper, we research the localization problem of the crawler-type inspection robot for underwater structure which travels an outer wall of underwater structure. Since various factors of the underwater environment affect an encoder odometer, it is hard to localize robot itself using only on-board sensors. So in this research we used a depth sensor and an IMU to compensate odometer which has extreme error in the underwater environment through using Extended Kalman Filter(EKF) which is normally used in mobile robotics. To acquire valid measurements, we implemented precision sensor modeling after assuming specific situation that robot travels underwater structure. The depth sensor acquires a vertical position of robot and compensates one of the robot pose, and IMU is used to compensate a bearing. But horizontal position of robot can't be compensated by using only on-board sensors. So we proposed a localization algorithm which makes horizontal direction error bounded by using kinematics relationship. Also we implemented computer simulations and experiments in underwater environment to verify the algorithm performance.

• 제어로봇시스템학회논문지
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• 제18권6호
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• pp.594-600
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• 2012

Accurate localization is very important to stable navigation of a mobile robot. This paper deals with local localization of a mobile robot especially for outdoor environments. The GPS information is the easiest way to obtain the outdoor position information. However, the GPS accuracy can be severely affected by environmental conditions. To deal with this problem, the GPS and wheel odometry can be combined using an EKF (Extended Kalman Filter). However, this is not enough for safe navigation of a mobile robot in outdoor environments. This paper proposes a novel method using lane features from the road image. The pose data of a mobile robot can be corrected by analyzing the detected lane features. This can improve the accuracy of the localization process substantially.

• 한국전자통신학회논문지
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• 제12권1호
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• pp.123-128
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• 2017

본 논문에서는 카메라 및 기타 센서 처리를 통한 소형차의 자율 주행을 통한 미션 수행에 대해 다루도록 한다. 주어진 차선내에서 안전한 운행을 위해서는 차선내에서의 차량의 자세 정보 추출이 필요하다. 이를 위해 호모그라피를 이용하도록 한다. 주어진 영상을 흑백 이미지로 변환후 이치화와 에지를 이용하여 차선에서 필요한 제어점들을 추출한다. 호모그라피를 이용하여 두 개의 제어점들을 세계 좌표계로 변환후 차량의 각도와 위치를 계산하도록 한다. 칼라 정보를 이용하여 신호등 판단을 하도록 한다. 실험을 통해 주어진 임무를 잘 수행함을 확인할 수 있었다.