This study focuses on the development of a Last-Mile delivery service using unmanned vehicles to deliver goods directly to the end consumer utilizing drones to perform autonomous delivery missions and an image-based precision landing algorithm for handoff to a robot in an intermediate facility. As the logistics market continues to grow rapidly, parcel volumes increase exponentially each year. However, due to low delivery fees, the workload of delivery personnel is increasing, resulting in a decrease in the quality of delivery services. To address this issue, the research team conducted a study on a Last-Mile delivery service using unmanned vehicles and conducted research on the necessary technologies for drone-based goods transportation in this paper. The flight scenario begins with the drone carrying the goods from a pickup location to the rooftop of a building where the final delivery destination is located. There is a handoff facility on the rooftop of the building, and a marker on the roof must be accurately landed upon. The mission is complete once the goods are delivered and the drone returns to its original location. The research team developed a mission planning algorithm to perform the above scenario automatically and constructed an algorithm to recognize the marker through a camera sensor and achieve a precision landing. The performance of the developed system has been verified through multiple trial operations within ETRI.

Aircraft landing is a complex and challenging flight phase in which pilots are required to allocate attention efficiently to the surrounding environment. A comprehensive understanding of pilot situation awareness (SA) is needed for successful landing. This study was to predict pilot SA during landing using eye tracking data. The experiments were carried out with 5 repetitive simulated landings for four expert and four novice pilots using eye tracking equipment. Three eye tracking features (visit frequency, dwell time ratio, scan path entropy) were developed for reflecting three level of SA model (perception, comprehension, projection). Prediction of SA was performed by developing multiple regression model. Visit frequency of expert pilots was 138%, 47%, 85%, 67%, 117%, and 91% higher than novice pilots in RPM, VVI, altimeter, heading, airspeed, and attitude areas of interest (AOIs) respectively; while 50% and 33% lower in runway and outside AOIs respectively. Dwell time ratio of expert pilots was 38% and 42% higher than novice pilots in runway and outside AOIs respectively; while 62%, 62%, and 65% lower in altimeter, airspeed, and attitude AOIs respectively. Scan path entropy of expert pilots was 33% higher than novice pilots in outside AOI; while 29% lower in attitude AOI. Coefficient of determination for the prediction model for SA was 80.6%. The results of this study can be used as objective data of strategy establishment or training feedback for novice pilots.

해상교통안전진단이 시행된지 1년 10개월이 지났으나, 아직까지 제도의 정착을 위해 필요한 사항을 안전진단대행기 관의 입장에서 제언하고자 한다. 홍보부족, 정식진단 혹은 자체진단의 문제, 진단항목의 결정, 자체진단서의 명칭, 이용자의견수 렴, 턴키사업, 심의 절차에 대한 제언을 하고자 한다.

대형 비행기 조종사들은 지상으로부터 높은 조종실의 위치, 빠른 접근속도와 수직강하속도로 인하여 불충분한 시각정보를 바탕으로 착륙 조작을 하여야만 한다. 본 연구에서는 이러한 대형 비행기의 특징으로 인하여 비행기 착륙의 최종 조작인 당김(flare) 조작을 할 때 대형 비행기 조종사들이 정확한 높이지각을 하기 어렵다는 사실을 검증하였다. 연구 1에서는 조종석 외부로 보이는 공항에 대한 사진을 제시하고 높이 추정을 하도록 하여 정적 시각 단서만으로 정확한 높이지각이 가능한지를 살펴보았다. 85feet의 고도에서는 정확한 높이지각을 하나 당김 조작을 준비해야 하는 높이인 55feet부터 낮은 고도에서는 정확한 높이를 추정하지 못하였다. 연구 2에서는 착륙의 전 과정을 녹화한 화면을 보여주어 높이 추정을 하도록 하여 동적 시각 단서와 정적 시각 단서를 모두 제공하여 주었다. 시각단서가 연구 1보다 풍부하였으므로 50feet까지 정확한 높이지각을 하였으나 연구 1의 결과와 마찬가지로 당김 조작을 시작하는 높이인 30feet 이하의 저고도에서의 높이지각이 모두 부정확하였다. 시각 단서의 해석에 경험이 중요한 요인이므로 경험이 많은 기장과 경험이 적은 부기장 간의 높이 추정에 차이가 있는지를 연구 1과 연구 2에서 모두 비교하여 보았으나 차이는 유의하지 않았다.

Flight of an autonomous unmanned aerial vehicle (UAV) generally consists of four steps; take-off, ascent, descent, and finally landing. Among them, autonomous landing is a challenging task due to high risks and reliability problem. In case the landing site where the UAV is supposed to land is moving or oscillating, the situation becomes more unpredictable and it is far more difficult than landing on a stationary site. For these reasons, the accurate and precise control is required for an autonomous landing system of a UAV on top of a moving vehicle which is rolling or oscillating while moving. In this paper, a vision-only based landing algorithm using dynamic gimbal control is proposed. The conventional camera systems which are applied to the previous studies are fixed as downward facing or forward facing. The main disadvantage of these system is a narrow field of view (FOV). By controlling the gimbal to track the target dynamically, this problem can be ameliorated. Furthermore, the system helps the UAV follow the target faster than using only a fixed camera. With the artificial tag on a landing pad, the relative position and orientation of the UAV are acquired, and those estimated poses are used for gimbal control and UAV control for safe and stable landing on a moving vehicle. The outdoor experimental results show that this vision-based algorithm performs fairly well and can be applied to real situations.