Representing Multi-Robot Structure through Multimodal Graph Embedding for the Selection of Robot Teams
This addresses team selection in multi-robot systems for applications like area exploration and search and rescue, but it is incremental as it builds on existing graph embedding methods.
The paper tackles the problem of selecting teams in multi-robot systems by proposing a multimodal graph embedding method to create unified representations from multiple information modalities, and it shows that this approach outperforms baseline methods based on single modalities or other graph embedding techniques.
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.