오현동 교수
Hyondong Oh
KAIST 기계공학과 · 공학
연구실 소개
오현동 교수의 연구실은 무인 항공기(UAV)를 활용한 정밀 탐색 및 추적 기술, 특히 화학·생물·방사성 물질 누출과 같은 긴급 상황에서의 오염 확산 예측과 실시간 감시에 중점을 두고 있습니다. 기반 기술로는 비선형 예측 제어, 베이지안 추론, 경로 최적화, 다중 드론 간 협동 제어를 활용하여, 복잡한 환경에서의 효율적이고 정확한 목표 탐지 및 추적을 구현합니다. 특히 도로 지도 기반 추적, 스탠드오프 궤도 제어, 소스 위치 추정 등 실용적인 응용을 고려한 알고리즘 개발에 힘쓰고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monito
This paper presents a methodology for coordinated standoff tracking of moving target groups using multiple unmanned aerial vehicles (UAVs). The vector field guidance approach for a single UAV is first applied to track a group of targets by defining a variable standoff orbit to be followed, which can keep all targets within the field-of-view of the UAV. A new feedforward term is included in the guidance command considering variable standoff distance, and the convergence of the vector field to the
This paper proposes a strategy for performing an efficient autonomous search to find an emitting source of sporadic cues of noisy information. We focus on the search for a source of unknown strength, releasing particles into the atmosphere where turbulence can cause irregular gradients and intermittent patches of sensory cues. Bayesian inference, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source location and strength in respons
A coordinated standoff target tracking strategy using path shaping for multiple unmanned aerial vehicles (UAVs) is presented. In performing a tracking mission of a ground target of interest, UAVs are to approach a target and to keep a standoff distance from it with a prescribed inter-vehicle angular separation around the target in order to track it while acquiring accurate target information. Since fixed-wing UAVs without hovering capability fly efficiently at a nominal airspeed, it is desirable
This paper presents a road-network search route planning algorithm by which multiple autonomous vehicles are able to efficiently visit every road identified in the map in the context of the Chinese postman problem. Since the typical Chinese postman algorithm can be applied solely to a connected road-network in which ground vehicles are involved, it is modified to be used for a general type of road map including unconnected roads as well as the operational and physical constraints of unmanned aer
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