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.
초록·키워드
Nowadays, mobile robots are being widely employed in various settings, including factories, homes, and everyday tasks. Achieving successful implementation of autonomous robot movement largely depends on effective route planning. Therefore, it is not surprising that there is a growing trend in studying and improving the intelligence of this technology. Deep reinforcement learning has shown remarkable performance in decision-making problems and can be effectively utilized to address path planning challenges faced by mobile robots. This manuscript focuses on investigating path planning problems using deep reinforcement learning and multi-sensing information fusion technology. The manuscript elaborates on the significance of path planning, providing comprehensive research encompassing path planning algorithms, deep reinforcement learning, and multi-sensing information fusion. Also, the fundamental theory of deep reinforcement learning is introduced, followed by the design of a multimodal perception module based on image and lidar. A semantic segmentation approach is employed to bridge the gap between simulated and real environments. To enhance strategy, a lightweight multimodal data fusion network model is carefully developed, incorporating modality separation learning. Overall, in this paper, we explore the use of a deep reinforcement learning architecture for conducting path planning experiments with mobile robots. The results obtained from these experiments demonstrate promising outcomes.
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.
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