The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses on indoor mobile robot position recognition and driving experiment using QR Code during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the driving directions of QR Code-aware movable robots during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the application of QR Code position recognition during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the application of QR Code recognition system during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the position recognition control using QR Code during the development of QR Code-aware indoor mobility robots.

본 연구의 목적은 노화에 따른 작업기억능력의 저하에 영향을 받는 자막인식위치에 대해 탐구하는 것이다. 이를 위해, 본 연구에는 주니어 집단(평균 나이: 26세, 표준편차: 3.06, N=27)과 시니어 집단(평균 나이: 61.69세, 표준편 차: 4.18, N=26)이 참여했으며, 실험 과제로는 실험 참가자들의 작업기억능력을 측정하기 위한 N-back 과제와 자막 인식위치를 측정하기 위한 동영상자막확인 과제가 사용되었다. N-back 과제 수행 결과, 시니어 집단이 주니어 집단 보다 과제에 대한 반응속도가 느리고 정답률이 낮게 나타나, 시니어 집단은 주니어 집단에 비해 작업기억능력의 저하가 나타났다는 것을 의미했다. 또한, 동영상자막확인 과제 수행 결과, 노화에 따른 작업기억능력의 저하에 부정적인 영향을 받는 자막위치는 화면의 ‘좌측-아래’이고 긍정적인 영향을 받는 자막위치는 화면의 ‘좌측-가운데’으로 나타났다. 나머지 화면 위치에서는 노화에 따른 작업기억능력의 저하에 영향을 받지 않았다. 결과적으로 본 연구 결과를 통해서 연령의 증가에 따른 작업기억능력의 저하에 부정적 혹은 긍정적 영향을 받는 영상 속 자막인식위치에 대해서 살펴볼 수 있었으며, 이는 영상에 자막을 제시해야할 경우 시청자의 연령을 고려하여 자막위치를 선정하면 효율적으로 시청자에게 정보를 제공할 수 있다는 것을 의미했다.

This study aimed to examine the effects of transformational leadership on employees’ trust, perceived support from superiors, organizational citizenship behavior, and moderating effects of locus of control. Using the Amos program, this study tested reliability and fitness of the research model and verified five hypotheses based on empirical data from 233 employee samples in coffee shops. The result of this study shows that positive consideration and charisma of transformational leadership positively influenced employees’ level of trust toward superiors and perceived support from superiors. Trust had positive effects on promotion of organizational citizenship behavior of employees. Lastly, analysis of the moderating effect of locus of control showed that a lower level of extrinsic control and higher level of intrinsic control were both positively correlated with greater receptiveness to transformational leadership. The findings in this study identified several significant factors of employee effectiveness influenced by transformational leadership in the coffee shop industry. Limitations and future research directions are also discussed.

PURPOSES: This study is to develop a road traffic sign recognition and automatic positioning for road facility management. METHODS: In this study, we installed the GPS, IMU, DMI, camera, laser sensor on the van and surveyed the car position, fore-sight image, point cloud of traffic signs. To insert automatic position of traffic sign, the automatic traffic sign recognition S/W developed and it can log the traffic sign type and approximate position, this study suggests a methodology to transform the laser point-cloud to the map coordinate system with the 3D axis rotation algorithm. RESULTS: Result show that on a clear day, traffic sign recognition ratio is 92.98%, and on cloudy day recognition ratio is 80.58%. To insert exact traffic sign position. This study examined the point difference with the road surveying results. The result RMSE is 0.227m and average is 1.51m which is the GPS positioning error. Including these error we can insert the traffic sign position within 1.51m CONCLUSIONS: As a result of this study, we can automatically survey the traffic sign type, position data of the traffic sign position error and analysis the road safety, speed limit consistency, which can be used in traffic sign DB.

전파를 이용한 실내 위치인식 기술은 현재 다양한 환경에서 연구되고 있다. 그 중 철골구조로 이루어진 선박은 전파의 반사에 의해 수신율은 높지만 레인징 오차가 크게 발생한다. 이러한 환경에서 발생하는 위치측정 오차를 줄이기 위하여 본 연구에서는 IEEE 802.15.4a의 CSS 기반으로 변형 이변측위와 초전센서를 이용한 선내 위치인식보정 알고리즘을 제안한다. 제안한 시스템은 선내 복도와 같은 좁은 통로에서 CSS의 특성분석을 통하여 이동노드와 고정노드 사이의 적합한 수신거리를 추정하여 고정노드의 수를 줄이고 또한 전파의 반사와 회절에 의한 레인징 오차가 크게 변동하는 코너영역에서 제안한 변형 이변측위기법과 초전센서를 이용하여 이동구간을 추적하여 위치를 인식하였다. 실험결과 제안한 알고리즘이 일반적인 방법 대비 86.2 %의 선내 위치인식 정확도와 효율이 향상됨을 확인하였다.

In this paper, the uAPSS(u-APartment Service System) that is based on location-aware technology is designed and implemented for a luxury apartment. On the real luxury apartment the developed system has been employed and tested to provide convenient and secure living for residents. It provides services such as emergency call, intelligent elevator operation, and hands-free door access based on the location of the residents with personal device as called smart tag. It can also be applied to other service areas such as the location-aware u-Service for hospitals, high-rising complex buildings, silver towns, etc.

In this paper, the u-Service system that is based on location-aware technology is designed for a silver town. It provides services such as emergency call, intelligent elevator operation, and hands-free door access based on the location of the residents with personal device as called smart tag. It can also be applied to other service areas such as the location-aware u-Service for Hospital, high-rising complex building, APT, etc.

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

This paper presents a 6-DOF relocalization using a 3D laser scanner and a monocular camera. A relocalization problem in robotics is to estimate pose of sensor when a robot revisits the area. A deep convolutional neural network (CNN) is designed to regress 6-DOF sensor pose and trained using both RGB image and 3D point cloud information in end-to-end manner. We generate the new input that consists of RGB and range information. After training step, the relocalization system results in the pose of the sensor corresponding to each input when a new input is received. However, most of cases, mobile robot navigation system has successive sensor measurements. In order to improve the localization performance, the output of CNN is used for measurements of the particle filter that smooth the trajectory. We evaluate our relocalization method on real world datasets using a mobile robot platform.

Localization of underwater vehicle is essential to use underwater robotic systems for various applications effectively. For this purpose, this paper presents a method of two-dimensional SLAM for underwater vehicles equipped with two hydrophones. The proposed method uses directional angles for underwater acoustic sources. A target signal transmitted from acoustic source is extracted using band-pass filters. Then, directional angles are estimated based on Bayesian process with generalized cross-correlation. The acquired angles are used as measurements for EKF-SLAM to estimate both vehicle location and locations of acoustic sources. Through these processes, the proposed method provides reliable estimation for two dimensional locations of underwater vehicles. Experimental results demonstrate the performance of the proposed method in a real sea environment.

In this paper, we propose a new method for improving the accuracy of localizing a robot to find the position of a robot in indoor environment. The proposed method uses visible light for indoor localization with a reference receiver to estimate optical power of individual LED in order to reduce localization errors which are caused by aging of LED components and different optical power for each individual LED, etc. We evaluate the performance of the proposed method by comparing it with the performance of traditional model. In several simulations, probability density functions and cumulative distribution functions of localization errors are also obtained. Results indicate that the proposed method is able to reduce localization errors from 7.3 cm to 1.6 cm with a precision of 95%.