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
In recent years, robotics technology has made significant advancements, particularly in multi-robot systems where efficient task allocation plays a crucial role in maximizing productivity and minimizing operational time. Previous research has explored various approaches to solving the Multi-Robot Task Allocation problem, but many have faced challenges in task distribution efficiency. To address this issue, we propose a Seeding Genetic Algorithm based on the A* algorithm and DBSCAN clustering. The A* algorithm performs path optimization in a grid environment with obstacles, while DBSCAN clusters tasks to enhance efficient task allocation. By seeding GA with these optimized solutions, the algorithm achieves faster convergence and higher solution quality. Simulations conducted on two maps with different robot configurations show that the A*-DBSCAN Seeding GA outperforms traditional GA and Greedy methods. The proposed method reduced the makespan, and its statistical significance was verified through ANOVA tests. This research contributes to improving multi-robot collaboration in industrial applications, offering an effective solution to the MRTA problem, reducing task completion time, and enhancing system efficiency.
본문·목차
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UCI(KEPA) : I410-151-25-02-092713739
