In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

This study developed an unmanned autonomous moving algae collection device (HAMA-bot) to remove high-density algae concentrated in the waterfront of urban agricultural reservoirs, and analyzed the effect of algae removal after field application to medium-sized urban reservoir. The algae reduction effect (Chl-a) of the study site in the reservoir by the HAMA-bot operation showed an average 18.5% higher in the treatment area compared to the control area before operation, while the average reduction of 24% in the treatment area after operation. In addition, the Chl-a removal rate, which directly analyzed the influent and effluent of HAMA-bot, showed a very high level with an average of 96.9% (94.7~99.2%). Currently, it is optimized for urban reservoirs and manufactured on a small scale, but it is a useful tool that can be applied on a large scale to large dams and rivers, and it is considered that the field applicability would be improved with the optimized scale.

The diversity of smart EV(electric vehicle)-related industries is increasing due to the growth of battery-based eco-friendly electric vehicle component material technology, and labor-intensive industries such as logistics, manufacturing, food, agriculture, and service have invested in and studied automation for a long time. Accordingly, various types of robots such as autonomous mobile robots and collaborative robots are being utilized for each process to improve industrial engineering such as optimization, productivity management, and work management. The technology that should accompany this unmanned automobile industry is unmanned automatic charging technology, and if autonomous mobile robots are manually charged, the utility of autonomous mobile robots will not be maximized. In this paper, we conducted a study on the technology of unmanned charging of autonomous mobile robots using charging terminal docking and undocking technology using an unmanned charging system composed of hardware such as a monocular camera, multi-joint robot, gripper, and server. In an experiment to evaluate the performance of the system, the average charging terminal recognition rate was 98%, and the average charging terminal recognition speed was 0.0099 seconds. In addition, an experiment was conducted to evaluate the docking and undocking success rate of the charging terminal, and the experimental results showed an average success rate of 99%.

As technologies have been more quickly developed in this 4th Industry Revolution era, their application to defense industry has been also growing. With these much advanced technologies, we attempt to use Manned-Unmanned Teaming systems in various military operations. In this study, we consider the Location-Routing Problem for reconnaissance surveillance missions of the maritime manned-unmanned surface vehicles. As a solution technique, the two-phase method is presented. In the first location phase, the p-median problem is solved to determine which nodes are used as the seeds for the manned vehicles using Lagrangian relaxation with the subgradient method. In the second routing phase, using the results obtained from the location phase, the Vehicle Routing Problems are solved to determine the search routes of the unmanned vehicles by applying the Location Based Heuristic. For three network data sets, computational experiments are conducted to show the performance of the proposed two-phase method.

With the continuous development of science and technology, unmanned ship has gradually become a hot spot in the field of marine research. In practical applications, unmanned ships need to have long-range navigation and high efficiency, so that they can accurately perform tasks in the marine environment. As one of the key technologies of unmanned ship, path planning is of great significance to improve the endurance of unmanned ship. In order to meet the requirements, this paper proposes a path planning method for long distance unmanned ships based on reinforcement learning angle precedence ant colony improvement algorithm. Firstly, canny operator is used to automatically extract navigation environment information, and then MAKLINK graph theory is applied for environment modelling. Finally, the basic ant colony algorithm is improved and applied to the path planning of unmanned ship to generate an optimal path. The experimental results show that, compared with the traditional ant colony algorithm, the path planning method based on the improved ant colony algorithm can achieve a voyage duration of nearly 7 km for unmanned ships under the same sailing environment, which has certain practicability and popularization value.

본 연구에서는 무인항공기인 드론을 활용한 VDMS(Vision-based Displacement Measurement System)를 통해 동적변위계측 정 확도와 동특성 추정 신뢰성 검증을 위한 동적실험을 실시하였다. 비행하는 드론의 이동 및 회전진동을 보정하기 위해 영상 내부의 변 위가 발생하지 않는 고정점을 활용한 보정밥법을 사용하였으며, 검증을 위해 설치한 범용 센서인 LVDT와 LDS의 변위계측 결과와 비 교하여 그 오차를 시간영역과 진동수영역에서 분석하였다. 3가지 타입의 장비 모두 최대 변위 도달 및 주기 운동 계측에 있어서 대체 적으로 유사한 결과를 나타내었다. LDS 기준의 오차 분석 결과, 드론과 LVDT는 가진 진동수 변화에 의한 오차 값은 미비하나, 최대 발생 변위가 작을수록 오차 값은 증가하였다.

본 연구에서는 소나무재선충의 매개충인 솔수염하늘소와 북방수염하늘소에 대하여 무인항공기 (무인헬리콥터)를 이용하여 스피네토람의 약효 및 약해를 조사하였다. 하늘소를 대상으로 등록된 펜토에이트 유제, 비펜트린 액상수화제, 하늘소를 제외한 딱정벌레가 대상인 에토펜프록 스 유제, 디플루벤주론 수화제와 나방류에 방제 약제로 등록된 인독사카브 수화제, 스피네토람 액상수화제 6종을 ULV기로 살포하여 솔수염하 늘소에 대한 섭식독과 접촉독을 확인한 후 선발하였다. ULV 시험 결과, 펜토에이트, 비펜트린, 인독사카브, 스피네토람의 33배, 55배 희석배수 액은 처리후 3일차에 접촉독과 섭식독에서 100% 살충율을 보였으나, 에토펜프록스는 7일차 접촉독 살충율 88.9%(33배), 88.9%(50배), 섭식독 살충율 93.4%(33배, 50배), 디플루벤주론은 7일차 접촉독 살충율 83.3%(33배), 섭식독 80.3%, 53.9%(50배)로 조금 낮은 살충률을 보였다. ULV 시험에서 선발된 가장 적합한 스피네토람의 33배액을 무인항공기로 살포하여 솔수염하늘소와 북방수염하늘소에 대한 감수성을 평가한 결 과, 98.6% - 100%의 살충율을 보였다. 그러나, 해당 약제의 매개충 방제에 적용하기 전에 항공 살포에 의한 꿀벌에 대한 위해성 평가가 필요할 것 으로 판단되었다.

본 논문에서는 해상 위험유해물질(Hazardous Noxious Substances, HNS) 사고의 효과적인 대응을 위해 개발된 부유식 무인이동체 기반 광역탐지 및 모니터링 시스템의 운용 시나리오 설계와 실험 검증 내용을 보인다. 광역탐지 및 모니터링 시스템은 장시간 운용이 가 능하되 제한적 이동이 가능한 무계류형 부이 형태를 갖는 부유식 무인이동체 플랫폼을 기반으로 개발되었으며 임무 수행에 필요한 열화 상 카메라, 레이더, 부유 및 대기 HNS의 탐지를 위한 센서가 탑재되었다. 실험 검증 과정에서는 탐지 센서 성능을 야외 환경에서 실험적 으로 검증하기 위해 이동식 가스 유출 시스템(Portable Gas-exposure System, PGS)을 추가로 설치하였다. 무인 시스템의 원격 및 자율 운용을 위해 전체 운용 소프트웨어는 로봇운영체제(Robot Operating System, ROS) 프레임워크를 기반으로 통합되었다. 내수면 및 실해역에서의 실 험을 통해 개발된 시스템의 운용 및 활용 가능성을 실험적으로 검증하였다.

Recently, unmanned logistics delivery systems, such as UAV (Unmanned Aerial Vehicle, written as drone below) and autonomous robot delivery systems, have been implemented in many countries due to the rapid development of autonomous driving technology. The development of these new types of advanced unmanned logistics delivery systems is essential not only to become a leading logistics company but also to secure national competitiveness. In this paper, the application of the unmanned logistics delivery system was investigated in terms of market trends, overall technology level of last mile delivery drone and autonomous delivery robot. The direction of response to changes in the last mile delivery service market was checked through a comparison of the technological level between domestic companies that produce last mile devices and advanced foreign companies. As a result of this technology level analysis, the difference between domestic companies and advanced companies was shown using tables and figures to show their relative levels. The results of this analysis reflect the opinions of experts in the field of last-mile delivery technology. In addition, the technology level of unmanned logistics delivery systems for each country was analyzed based on the number of related technology patents. Lastly, insights for the technology level analysis of unmanned last mile delivery systems were proposed as a conclusion.