Representing Multi-Robot Structure through Multimodal Graph Embedding for the Selection of Robot Teams
Representing Multi-Robot Structure through Multimodal Graph Embedding for the Selection of Robot Teams
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DOI：
10.1109/icra40945.2020.9197389
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Brian Reily;Christopher M. Reardon;Hao Zhang-
中科院分区：
文献类型：
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作者：
Brian Reily;Christopher M. Reardon;Hao Zhang-
越来越多的机器人系统的规模和复杂性被用来解决大规模的问题，如区域勘探和搜索和救援。人机协作中的一个关键决策是将多机器人系统分成多个团队，以解决单独的问题或在大范围内完成任务。为了解决多机器人系统中的团队选择问题，我们提出了一种新的多模态图嵌入方法来构建一个统一的表示，融合多种信息模态来描述和划分多机器人系统。关系模态被编码为有向图，该有向图可以编码非对称关系，该非对称关系被嵌入到每个机器人的统一表示中。然后，构造的多模态表示用于确定基于无监督学习的团队。我们进行实验，以评估我们的方法专家定义的团队编队，大规模的模拟多机器人系统，和系统的物理机器人。实验结果表明，我们的方法成功地决定正确的团队的多方面的内部结构描述多机器人系统的基础上，并优于基线方法的基础上，只有一种模式的信息，以及其他基于图嵌入的划分方法。
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 per-form 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.