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논문 기본 정보

저자정보
(국립부경대학교) (국립부경대학교) (국립부경대학교) (국립부경대학교) (국립부경대학교) (한화오션) (국립부경대학교)
저널정보
대한조선학회 대한조선학회 논문집 대한조선학회논문집 제61권 제5호(통권 제257호)
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피인용 2

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    초록·키워드

    This paper focuses on the cooperation between Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vessel (USV). It aims to develop efficient guidance and control algorithms for USV based on obstacle identification and path planning from aerial images captured by UAV. Various obstacle scenarios were implemented using the Robot Operating System (ROS) and the Gazebo simulation environment. The aerial images transmitted in real-time from UAV to USV are processed using the computer vision-based deep learning model, You Only Look Once (YOLO), to classify and recognize elements such as the water surface, obstacles, and ships. The recognized data is used to create a two-dimensional grid map. Algorithms such as A<SUP>*</SUP> and Rapidly-exploring Random Tree star (RRT<SUP>*</SUP>) were used for path planning. This process enhances the guidance and control strategies within the UAV-USV collaborative system, especially improving the navigational capabilities of the USV in complex and dynamic environments. This research offers significant insights into obstacle avoidance and path planning in maritime environments and proposes new directions for the integrated operation of UAV and USV.

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      UCI(KEPA) : I410-151-25-02-090885018