인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study addresses a coverage path planning (CPP) problem for maritime search and rescue (SAR) operations of multiple unmanned aerial vehicles (UAVs). For a given search area, the problem is to determine the path of each UAV while visiting all the search regions for the objective of minimizing the longest UAV search time, i,e, maximum among the UAV search times. Due to the problem complexity, a heuristic algorithm is proposed that consists of two phases: (a) grid decomposition of search area; and (b) path determination. In the first phase, the search area is represented as a graph after decomposed into the squared grids with the same size in such a way that the one with the minimum number of grids is selected among those obtained by border lines and their perpendicular vectors. Then, using the graph, the second phase determines the UAV paths by two steps: (b.1) obtaining an initial path of each UAV using a greedy algorithm; and (b.2) balancing the allocations of the search regions to UAVs and improving the current paths by dynamic reallocations of search regions and path modifications using the ant colony optimization (ACO) algorithm. Computational experiments were done on real data, and the test results show that the two-phase algorithm proposed in this study gives more balanced allocations of search regions to UAVs and hence improves the previous algorithm significantly.
본문·목차
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