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Stepwise Large-Scale Multi-Agent Task Planning Using Neighborhood Search

文献类型： 外文期刊

作者： Zeng, Fan 1 ; Shirafuji, Shouhei 2 ; Fan, Changxiang 3 ; Nishio, Masahiro 4 ; Ota, Jun 5 ;

作者机构： 1.Univ Tokyo, Grad Sch Engn, Dept Precis Engn, Tokyo 1138656, Japan

2.Kansai Univ, Dept Engn Mech, Fac Engn Sci, Osaka 5650842, Japan

3.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China

4.Toyota Motor Co Ltd, Adv R&D & Engn Co, R&D & Engn Management Div, Strateg R&D Planning Dept, Toyota 4710826, Japan

5.Univ Tokyo, Sch Engn, Res Artifacts, Ctr Engn RACE, Tokyo 1138656, Japan

关键词： Heuristic search; multi-agent system; neighborhood search; plangraph; task planning

期刊名称：IEEE ROBOTICS AND AUTOMATION LETTERS （ 影响因子：4.6； 五年影响因子：5.5 ）

ISSN： 2377-3766

年卷期： 2024 年 9 卷 1 期

页码：

收录情况： SCI

摘要： This letter presents a novel stepwise multi-agent task planning method that incorporates neighborhood search to address large-scale problems, thereby reducing computation time. With an increasing number of agents, the search space for task planning expands exponentially. Hence, conventional methods aiming to find globally optimal solutions, especially for some large-scale problems, incur extremely high computational costs and may even fail. In this letter, the proposed method easily achieves the goals of multi-agent task planning by solving an initial problem using a minimal number of agents. Subsequently, tasks are reallocated among all agents based on this solution and the solutions are iteratively optimized using a neighborhood search. While aiming to find a near-optimal solution rather than an optimal one, the method substantially reduces the time complexity of searching to a polynomial level. Moreover, the effectiveness of the proposed method is demonstrated by solving some benchmark problems and comparing the results obtained using the proposed method with those obtained using other state-of-the-art methods.

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