• 로봇학회논문지
• /
• 제3권4호
• /
• pp.308-314
• /
• 2008

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze the result representation of SLAM using RBPF (RBPF-SLAM) when particle diversity is preserved. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. Thus, we propose several result representations and provide the analysis of the representations. For the analysis, estimation errors and their variances, and consistency of RBPF-SLAM are dealt in this study. According to the simulation results, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.

• 대한기계학회논문집A
• /
• 제38권9호
• /
• pp.1013-1018
• /
• 2014

자기연마가공은 연마입자와 자성입자를 혼합한 공구의 유연성을 이용하여, 공작물 표면을 폴리싱하는 특수가공법이다. 기존 연구의 대부분은 가공 정밀도를 향상시키기 위해서 연마입자의 크기를 달리 하는 것에 관한 내용들이다. 그러나 자기연마 가공에서는 연마입자의 크기뿐만 아니라, 자성입자의 크기도 가공에 많은 영향을 미칠 것으로 판단되며 이에 대한 연구가 반드시 필요하다. 따라서 본 연구에서는 크기가 다른 자성입자들을 사용하여 자기연마가공의 효과를 평가하였다. 자성입자는 철분말을 사용하였으며, 직경이 평균 8, 78, $250{\mu}m$의 크기이다. 공작물의 표면거칠기 향상 정도를 비교하여 자성입자의 크기가 자기연마가공의 정밀도에 미치는 효과를 평가하였다. 자성입자의 크기는 표면거칠기의 향상에 많은 영향을 미치며, 직경이 $78{\mu}m$일 때 가장 좋은 표면거칠기의 향상을 나타내었다.

• 로봇학회논문지
• /
• 제9권3호
• /
• pp.170-177
• /
• 2014

This paper proposes a novel upward-looking camera-based global localization using a ceiling image map. The ceiling images obtained through the SLAM process are integrated into the ceiling image map using a particle filter. Global localization is performed by matching the ceiling image map with the current ceiling image using SURF keypoint correspondences. The robot pose is then estimated by the coordinate transformation from the ceiling image map to the global coordinate system. A series of experiments show that the proposed method is robust in real environments.

• 로봇학회논문지
• /
• 제2권1호
• /
• pp.29-39
• /
• 2007

We present an implementation of particle filter algorithm for global localization and kidnap recovery of mobile robot. Firstly, we propose an algorithm for efficient particle initialization using sonar line features. And then, the average likelihood and entropy of normalized weights are used as a quality measure of pose estimation. Finally, we propose an active kidnap recovery by adding new particle set. New and independent particle set can be initialized by monitoring two quality measures. Added particle set can re-estimate the pose of kidnapped robot. Experimental results demonstrate the capability of our global localization and kidnap recovery algorithm.

• 대한수학회지
• /
• 제36권2호
• /
• pp.403-419
• /
• 1999

We introduce a Hamiltonian system which consists of two balls in the vertical line colliding elastically with each other and the floor. Wojtkowski proved that for the system of two linear balls with a linear potential (with gravity), there is a periodic orbit which becomes linearly stable if m1

• Journal of Power Electronics
• /
• 제16권2호
• /
• pp.610-620
• /
• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.901-905
• /
• 2005

Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Much research has been done to improve performance of MCL so far. Although MCL is capable of estimating the robot pose even for a completely unknown initial pose in the known environment, it takes considerable time to give an initial estimate because the number of random samples is usually very large especially for a large-scale environment. For practical implementation of the MCL, therefore, a reduction in sample size is desirable. This paper presents a novel approach to reducing the number of samples used in the particle filter for efficient implementation of MCL. To this end, the topological information generated off- line using a thinning method, which is commonly used in image processing, is employed. The topological map is first created from the given grid map for the environment. The robot scans the local environment using a laser rangefinder and generates a local topological map. The robot then navigates only on this local topological edge, which is likely to be the same as the one obtained off- line from the given grid map. Random samples are drawn near the off-line topological edge instead of being taken with uniform distribution, since the robot traverses along the edge. In this way, the sample size required for MCL can be drastically reduced, thus leading to reduced initial operation time. Experimental results using the proposed method show that the number of samples can be reduced considerably, and the time required for robot pose estimation can also be substantially decreased.

• 한국환경보건학회지
• /
• 제49권1호
• /
• pp.30-36
• /
• 2023

Background: Among various pollutants, fine particle (PM_2.5, defined as particle less than 2.5 nm in aerodynamic diameter) shows the most consistent association with adverse health effects. There is scientific evidence documenting a variety of adverse health outcomes due to exposure to PM_2.5. Objectives: This study aims to assess the health benefits of that would be achieved by meeting the World Health Organization's air quality guidelines for PM_2.5 using AirQ+ and BenMAP. Methods: We estimated PM_2.5 related health benefits in Korea from implementing the World Health Organization's air quality guidelines (annual average 5 ㎍/m³ and 10 ㎍/m³) and Korea's National Ambient Air Quality Standard (annual average 15 ㎍/m³). We used World Health Organization's AirQ+ and U.S. Environmental Protection Agency's Environmental Benefits Mapping and Analysis Program. Results: The annual number of avoided PM_2.5 related premature deaths exceeding WHO guideline levels was assessed using both AirQ+ and BenMAP. We estimated that the health benefits of attaining the World Health Organization's air quality guidelines for PM_2.5 (annual average 5 ㎍/m³) would suggest an annual reduction of 26,128 (95% confidence interval [CI]: 17,363~34,024) and 26,853 (95% CI: 18,527~34,944) premature deaths. Conclusions: Our study provided useful information to policy makers and confirms that the reduction of PM_2.5 concentration would result in significant health benefits in Korea.

• 로봇학회논문지
• /
• 제3권1호
• /
• pp.33-41
• /
• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• 제어로봇시스템학회논문지
• /
• 제20권3호
• /
• pp.372-379
• /
• 2014

This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.