Name
Title | ||
|---|---|---|
Representing Multi-Robot Structure through Multimodal Graph Embedding for the Selection of Robot Teams |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICRA40945.2020.9197389 | ICRA |
DocType | Volume | Issue |
Conference | 2020 | 1 |
Citations | PageRank | References |
3 | 0.41 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| brian reily | 1 | 54 | 4.35 |
| Christopher Reardon | 2 | 73 | 9.46 |
| Hao Zhang | 3 | 189 | 23.73 |