인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.6
- 수록면
- 608 - 620 (13page)
- DOI
- 10.5302/J.ICROS.2025.25.0048
이용수
초록· 키워드
In multi-robot systems, task planning is essential for operating multiple robots simultaneously, and a task assignment algorithm assigns tasks to each robot and determines the execution order. This algorithm requires tasks and their specifications as input, and the process of generating these is called mission planning. In real-world environments, mission planning is typically performed manually by operators, which is inefficient and workload-intensive. To address this issue, recent studies have focused on autonomous mission planning using large language models (LLMs) to automate mission planning. In this study, we developed an LLM-based autonomous mission planning system to effeciently operate a multirobot system. This mission planning framework utilizes an LLM to decompose high-level missions into subtasks, generate specifications, and transform them into a format that can be processed by task assignment algorithms. Moreover, it employs a four-stage Actor-Critic structure to improve mission analysis accuracy and consider task dependencies. Additionally, an optimization-based algorithm is utilized for task assignment and scheduling, mitigating the mathematical limitations of the LLM. Specially, we apply the designed framework to a multi-robot system deployed in disaster scenarios, solve task assignment and scheduling, and validate its performance through 3D simulations.
#large language model
#mission planning
#task planning
#task assignment
#task decomposition and generation
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목차
- Abstract
- I. 서론
- II. 자율 임무 계획 프레임워크
- III. 작업 분해 및 생성
- IV. 실험 및 분석
- V. 결론
- REFERENCES