인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
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 proposes a cooperative capture system in which two allied unmanned surface vehicles (USVs) jointly deploy a capture net to intercept noncooperative vessels in naval environments. To overcome the limitations of traditional rule-based methods subjected to environmental uncertainties, a multi-agent reinforcement learning (MARL) framework is developed within a high-fidelity, unity-based simulation environment incorporating the Gerstner wave and wind disturbance models. Each agent utilizes an egocentric polar coordinate observation space; distance values are normalized via a nonlinear rational function to prioritize close-range sensitivity, while angular discontinuities are resolved through trigonometric transformations. The Multi-Agent POsthumous Credit Assignment algorithm is adopted within a Centralized Training with Decentralized Execution architecture, enabling robust strategy learning through posthumous credit assignment even when agents exit the environment. The reward structure is designed hierarchically, combining sparse event rewards for capture success with dense continuous rewards based on formation maintenance, target approach, and heading alignment. Quantitative experimental results demonstrate that the proposed MARL-based approach significantly outperforms the modified Line-of-Sight guidance law, achieving a 65% capture success rate and a three-fold reduction in formation collapse under dynamic sea conditions. This study confirms that the proposed framework effectively enables autonomous USVs to learn complex, coordinated maneuvers for physical maritime interdiction, providing a robust solution for securing maritime security.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
