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.

This study introduces the accurate correction method of bearing position error of mobile robots using Stargazer sensor. The mobile robots require some vital functions including map building, localization, path planning, obstacle avoidance for autonomous navigation. In most cases, the localization of angular pose of a robot is essential because its result has a great effect on the performance of the other functions. We demonstrated the validity of the proposed method with the results of real experiments and applied it to the photographer robot for correct bearing position error at the moment of taking a picture.

Acoustic based localization is essential to operate autonomous robotic systems in underwater environment where the use of sensorial data is limited. This paper proposes a localization method using artificial underwater acoustic sources. The proposed method acquires directional angles of acoustic sources using time difference of arrivals of two hydrophones. For this purpose, a probabilistic approach is used for accurate estimation of the time delay. Then, Gaussian sum filter based SLAM technique is used to localize both acoustic sources and underwater vehicle. It is performed by using bearing of acoustic sources as measurement and inertial sensors as prediction model. The proposed method can handle directional ambiguity of time difference based source localization by generating Gaussian models corresponding to possible locations of both front and back sides. Through these processes, the proposed method can provide reliable localization method for underwater vehicles without any prior information of source locations. The performance of the proposed method is verified by experimental results conducted in a real sea environment.

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.

본 연구는 친환경단감과원의 부유(Diospyros kaki ‘Fuyu’) 과실을 대상으로 창원과 창녕지역에서 발생한 저온내습(2012년 11월 2일)으로 인한 가을 동상해발생과 식재위치(표고) 및 나무 내에서의 착과 방향의 관계를 알아보고자 수행되었다. 창녕지역 과원의 과중과 과실횡경은 착과방향에 의해 강하게 영향을 받았으며(p<0.01), 과실종경과 동상해발생은 식재위치(p<0.01)와 깊은 관계가 있는 것으로 나타났다. 과실경도와 과실종경은 각각 착과방향과 식재위치의 영향을 받는 것으로 밝혀졌다(p<0.05). 특히 동상해는 과원 하단에 식재된 나무의 과실(15.2%)과 잎(약 90%)에서 심하게 나타나 동상해의 주요 요인이 과원의 지형적 특징인 것으로 나타났다. 과실 동상해를 입은 과실은 과피에 수침상 반점이나 흔적이 발생했다. 한편 창원지역의 과원에서는 과실횡경이 식재위치의 영향을 받은 것(p<0.05)을 제외하고 식재위치와 착과방향이 과실의 생육 및 동상해발생에 영향을 주지 않았다.

Acoustic signal is crucial for the autonomous navigation of underwater vehicles. For this purpose, this paper presents a method of acoustic source localization. The proposed method is based on the probabilistic estimation of time delay of acoustic signals received by two hydrophones. Using Bayesian update process, the proposed method can provide reliable estimation of direction angle of the acoustic source. The acquired direction information is used to estimate the location of the acoustic source. By accumulating direction information from various vehicle locations, the acoustic source localization is achieved using extended Kalman filter. The proposed method can provide a reliable estimation of the direction and location of the acoustic source, even under for a noisy acoustic signal. Experimental results demonstrate the performance of the proposed acoustic source localization method in a real sea environment.

Each four polycyclic aromatic hydrocarbons (PAHs) was reacted with OH radical at 1.5 Å distance by CAChe MOPAC 2000 program. These results were compared to those reported experimental results. Reaction positions of all four PAHs corresponded with predicted positions in which ⊿E(HOMO-LUMO) was approximately 4.7. Finally oxygen of OH radical combined with PAH and quinone form of products were produced. These results indicate that the proposed determining the ⊿E(HOMO-LUMO) can be effectively applied to predict reaction position of recalcitrant compounds such as dioxins, PCBs, POPs, and etc.

In this paper, we present a practical palce and object recognition method for guiding visitors in building environments. Recognizing palces or objects in real world can be a difficult problem due to motion blur and camera noise. In this work, we present a modeling method based on the bidirectional interactionbetween places and objects for simulataneous reinforcement for the robust recognition. The unification of visual context including scene context, object context, and temporal context is also. The proposed system has been tested to guide visitors in a large scale building environment(10 topological places, 80 3D objects)