• 디자인학연구
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• 제19권3호
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• pp.183-194
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• 2006

It is expected that the typical lifestyle of the future will be transformed into an opulent and comfortable existence as the quality of life improves due to the increase in household income and reduction in working hours. In the meantime, as the standard of living becomes increasingly more comfortable and plentiful, the toll on physical health becomes magnified as a result of obesity and insufficient exercise caused by super nutrition and change in labor conditions. This has instigated a deep awareness in fitness on the part of many people, forcing them to recognize the significance of daily exercise and physical activity. The Elliptical Cross Trainer(ECT), which has drawn wide attention recently, is a non-impact athletic apparatus that not only promotes exercise of the upper body parts in such sports as skiing but also the exercise of lower parts of the body on a treadmill. It is a type of cross training athletic gear that has been developed for aerobic exercise throughout the entire body. It has already formed a market as big as that of the treadmill in Europe, America, etc. Recently, its demand is growing sharply in the Korean markets as well as those in Northeast Asian countries. Despite such demand increase and expansion, since most of the expensive ECTs are exclusively supplied by suppliers in only a few advanced countries, localization of the ECT is urgently required in order to enhance competitiveness of Korean manufacturers and to expand the market. The ECT development project has been in full swing for approximately two year since 2004 in order to secure independent design, as well as engineering and manufacturing processes in efforts to develop a commercially viable ECT.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.182-184
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• 2009

FlexRay는 차세대 차량 및 선박 내 전자 장치간의 통신을 위해 고속의 시리얼 통신, time triggered bus, fault tolerant 통신을 제공하는 새로운 네트워크 통신 시스템의 표준이다. FlexRay Communication Controller(CC)는 FlexRay 프로토콜 규격의 핵심 부분이다. 본 논문에서는 먼저 SDL(Specification and Description Language)를 이용하여 FlexRay CC 프로토콜 규격과 기능 부분을 설계한다. 다음 설계한 SDL 소스를 기반으로 Verilog HDL을 이용하여 하드웨어로 설계한다. 설계한 FlexRay CC는 Samsung $0.35{\mu}m$ 공정을 이용하여 합성하였으며, 그 결과 80 MHz의 속도로 동작하는 것으로 나타났다. 또한 FlexRay 시스템의 동작을 확인하기 위해 로봇에 적용되는 음원위치 추정 시스템에 응용하였다. 응용 시스템은 ALTERA Excalibur ARM EPAX4F672C3을 이용하여 검증하였으며 성공적으로 동작함을 확인하였다.

• 제어로봇시스템학회논문지
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• 제22권10호
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• pp.833-838
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• 2016

This paper presents a performance evaluation of various feature-initialization algorithms for forward-viewing mono-camera based simultaneous localization and mapping (SLAM), specifically in indoor environments. For mono-camera based SLAM, the position of feature points cannot be known from a single view; therefore, it should be estimated from a feature initialization method using multiple viewpoint measurements. The accuracy of the feature initialization method directly affects the accuracy of the SLAM system. In this study, four different feature initialization algorithms are evaluated in simulations, including linear triangulation; depth parameterized, linear triangulation; weighted nearest point triangulation; and particle filter based depth estimation algorithms. In the simulation, the virtual feature positions are estimated when the virtual robot, containing a virtual forward-viewing mono-camera, moves forward. The results show that the linear triangulation method provides the best results in terms of feature-position estimation accuracy and computational speed.

• 로봇학회논문지
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• 제7권3호
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• pp.205-215
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• 2012

A GPS sensor is widely used in many areas such as navigation, or air traffic control. Particularly, the car navigation system is equipped with GPS sensor for locational information. However, when a car goes through a tunnel, forest, or built-up area, GPS receiver cannot get the enough number of satellite signals. In these situations, a GPS receiver does not reliably work. A GPS error can be formulated by sum of bias error and sensor noise. The bias error is generated by the geometric arrangement of satellites and sensor noise error is generated by the corrupted signal noise of receiver. To enhance GPS sensor accuracy, these two kinds of errors have to be removed. In this research, we make the road database which includes Road Database File (RDF). RDF includes road information such as road connection, road condition, coordinates of roads, lanes, and stop lines. Among the information, we use the stop line coordinates as a feature point to correct the GPS bias error. If the relative distance and angle of a stop line from a car are detected and the detected stop line can be associated with one of the stop lines in the database, we can measure the bias error and correct the car's location. To remove the other GPS error, sensor noise, the Kalman filter algorithm is used. Additionally, using the RDF, we can get the information of the road where the car belongs. It can be used to help the GPS correction algorithm or to give useful information to users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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• pp.1-8
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• 2023

In recent years, robots have been utilized in various industries to reduce workload and enhance work efficiency. The following mobility offers users convenience by autonomously tracking specific locations and targets without the need for additional equipment such as forklifts or carts. In this paper, deep learning techniques were employed to recognize individuals and assign each of them a unique identifier to enable the recognition of a specific person even among multiple individuals. To achieve this, the distance and angle between the robot and the targeted individual are transmitted to respective controllers. Furthermore, this study explored the control methodology for mobility that tracks a specific person, utilizing Simultaneous Localization and Mapping (SLAM) and Proportional-Integral-Derivative (PID) control techniques. In the PID control method, a genetic algorithm is employed to extract the optimal gain value, subsequently evaluating PID performance through simulation. The SLAM method involves generating a map by synchronizing data from a 2D LiDAR and a depth camera using Real-Time Appearance-Based Mapping (RTAB-MAP). Experiments are conducted to compare and analyze the performance of the two control methods, visualizing the paths of both the human and the following mobility.

• 한국건설관리학회논문집
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• 제17권3호
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• pp.32-42
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• 2016

현재 국내 대부분의 토목 건축 구조물이 BIM 정보가 부재한 상황에서 준공 BIM(as-built BIM)의 수요가 점차 증가하고 있다. 준공 BIM 구축을 위한 공간자료 취득에는 고밀도의 포인트 클라우드를 생성할 수 있는 레이저 스캐너가 주로 활용되고 있다. 하지만 기존의 고정식 스캔 시스템은 이동이 번거롭고, 정밀한 위치 선정이 필요 하며, 스캔 자료 정합을 위해 별도의 표지를 설치하거나 공액점을 추출하는 과정이 필요하다. 본 연구에서는 수작업을 최소화하기 위해 기존의 고정식 스캔 시스템을 대체할 수 있는 이동식 스캔 시스템을 제안하고자 하며, 기반 기술로 graph-based SLAM을 적용하였다. 테스트 장비는 총 세 개의 2차원 스캐너를 탑재하고 있으며, 중앙의 한 개는 수평으로 설치되어 graph 구축을 통한 이동경로취득에 사용되었고, 좌우 두 개는 수직으로 설치되어 시스템 진행의 연직 방향으로 주변 구조물에 대한 3차원 스캔 정보 취득에 사용되었다. 개발된 graph-based SLAM은 이동경로 상에 누적된 위치오차를 해소하기 위한 loop closure 처리 방법으로 Adaboost 기계학습을 적용하였다. 이는 특히 본 연구에서 사용한 장비와 같이 기계학습을 위한 다수의 feature 정보를 제공할 수 있는 멀티 스캐너 시스템에 적합한 방식이며, 두 실내공간을 대상으로 한 테스트에서 단일 스캐너 대비 false positive rate를 각각 7.9% 및 13.6%까지 줄일 수 있었다. 최종적으로 연구대상지역의 2차원 및 3차원 지도 구축을 통해 개발된 graph-based SLAM의 효용성을 확인하였다.