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• W3013072493 abstract "Multi-robot systems of increasing size and complexity are used to solve large-scale problems, such as area exploration and search and rescue. A key decision in human-robot teaming is dividing a multi-robot system into teams to address separate issues or to accomplish a task over a large area. In order to address the problem of selecting teams in a multi-robot system, we propose a new multimodal graph embedding method to construct a unified representation that fuses multiple information modalities to describe and divide a multi-robot system. The relationship modalities are encoded as directed graphs that can encode asymmetrical relationships, which are embedded into a unified representation for each robot. Then, the constructed multimodal representation is used to determine teams based upon unsupervised learning. We perform experiments to evaluate our approach on expert-defined team formations, large-scale simulated multi-robot systems, and a system of physical robots. Experimental results show that our method successfully decides correct teams based on the multifaceted internal structures describing multi-robot systems, and outperforms baseline methods based upon only one mode of information, as well as other graph embedding-based division methods." @default.
• W3013072493 created "2020-04-03" @default.
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• W3013072493 date "2020-03-26" @default.
• W3013072493 modified "2023-10-18" @default.
• W3013072493 title "Representing Multi-Robot Structure through Multimodal Graph Embedding for the Selection of Robot Teams" @default.
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• W3013072493 doi "https://doi.org/10.48550/arxiv.2003.12164" @default.
• W3013072493 hasPublicationYear "2020" @default.
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