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An Optimal Task Allocation Approach for Large-Scale Multiple Robotic Systems with Hierarchical Framework and Resource Constraints
Liang Ren; Yingying Yu; Zhiqiang Cao; Zhiyong Wu; Junzhi Yu; Chao Zhou; Min Tan
Source PublicationIEEE Systems Journal
2017-11
IssuePP(99)Pages:1-4
Abstract

In this paper, a hierarchical coalition approach is proposed. The proposed approach is conducted under a hierarchical framework, which is composed of individual robots in the bottom layer and managers in higher layers. A bottom­–up resources vector updating process for each manager is executed. Next, a top­–down resources comparison process between the task and the managers is used to generate candidate robot coalitions. Specifically, different managers may be combined when necessary to meet the resources requirement of some complex tasks. Furthermore, a matching degree between the task and candidate robot coalition is utilized for an optimized coalition selection. The effectiveness of the proposed approach is verified by simulations.

KeywordHierarchical Framework Large-scale Multiple Robotic System Resource Constraints Task Allocation
WOS IDWOS:000451262300083
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20936
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liang Ren,Yingying Yu,Zhiqiang Cao,et al. An Optimal Task Allocation Approach for Large-Scale Multiple Robotic Systems with Hierarchical Framework and Resource Constraints[J]. IEEE Systems Journal,2017(PP(99)):1-4.
APA Liang Ren.,Yingying Yu.,Zhiqiang Cao.,Zhiyong Wu.,Junzhi Yu.,...&Min Tan.(2017).An Optimal Task Allocation Approach for Large-Scale Multiple Robotic Systems with Hierarchical Framework and Resource Constraints.IEEE Systems Journal(PP(99)),1-4.
MLA Liang Ren,et al."An Optimal Task Allocation Approach for Large-Scale Multiple Robotic Systems with Hierarchical Framework and Resource Constraints".IEEE Systems Journal .PP(99)(2017):1-4.
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