한국의 수자원 총량중 이용량은 26.3%로 강으로부터의 취수 비율은 3%에 불과하며, 중국, 인도, 이탈리아, 남아공 등과 함께 물 스트레스 기준 중~고에 해당한다. 따라서 본 연구에서는 댐 수자원 확보를 위한 적지선정과 관련하여 연구하였다. 미국, 일본 및 한국의 댐 건설절차에 대한 조사 결과, 정량적인 기준 및 계산 방법 또는 공식이 부재하였다. 댐 적지선정의 정량적 기준 제시를 위하여 먼저 기후 변화 시나리오를 조사, 분석하였으 며, IPCC제 5 차 평가 보고서에 제시된 12.5 km 격자 해상도의 RCP 4.5 및 8.5를 SWAT와 HEC-ResSim를 이용하여 유역 연구에 적용하였다. 댐 시뮬레이션 결과를 토대로 홍수 및 가뭄의 감소 효과를 정량적으로 제시하였다. 그리고 댐 적지 선정 지표 결정과, AHP 기법을 이용하여 평가지표에 대한 전문가 설문을 실시하여 가중치를 부여하였으며, 이를 통해 댐 적지 선정 함수식(FSDS)을 제안하였다. 함수식의 경우 4곳의 기 설치된 유 역에 대하여 보정 및 검증 하였으며, FSDS와 GIS의 'Model builder'를 사용하여 댐 적지선정 자동화 모델을 개발하였다.

An increasing number of researches and developments for personal or professional service robots are attracting a lot of attention and interest industrially and academically during the past decade. Furthermore, the development of intelligent robots is intensively fostered as strategic industry. Until now, most of practical and commercial service robots are worked by remotely operated controller. The most important technical issue of remote control is a wireless communication, especially in the indoor and unstructured environments where communication infrastructures might be destroyed by various disasters. Therefore we propose a multi-robot following navigation method for securing the valid communication distance extension of the remote control based on WPAN(Wireless Personal Area Networks). The concept and implementation of following navigation are introduced and the performance verification is performed through real navigation experiments in real or test-bed environments.

Various robot platforms have been designed and developed to perform given tasks in a hazardous environment for the purpose of surveillance, reconnaissance, search and rescue, and etc. We have considered a terrain adaptive hybrid robot platform which is equipped with rapid navigation on flat floors and good performance on overcoming stairs or obstacles. Since our special consideration is posed to its flexibility for real application, we devised a design of a transformable robot structure which consists of an ordinary wheeled structure to navigate fast on flat floor and a variable tracked structure to climb stairs effectively. Especially, track arms installed in front side, rear side, and mid side are used for navigation mode transition between flatland navigation and stairs climbing. The mode transition is determined and implemented by adaptive driving mode control of mobile robot. The wheel and track hybrid mobile platform apparatus applied off-road driving mechanism for various professional service robots is verified through experiments for navigation performance in real and test-bed environment.

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

We propose and develop Home Security robot system based on Sensor Network (HSSN) configured by sensor nodes including radio frequency (RF), ultrasonic, temperature, light and sound sensors. Our system can acknowledge security alarm events that are acquired by sensor nodes and relayed in the hop-by-hop transmission way. There are sensor network, Home Security Mobile Robot (HSMR) and Home Server(HS) in this system. In the experimental results of this system, we presented that our system has more enhanced performance of response to emergency context and more speedy and accurate path planning to target position for arriving an alarm zone with obstacle avoidance and acquiring the context-aware information.