Representing Multi-Robot Structure through Multimodal Graph Embedding for the Selection of Robot Teams

TL;DR

This paper introduces a multimodal graph embedding technique for representing multi-robot systems, enabling effective team selection through unsupervised learning, and demonstrates its superiority over single-mode and existing methods in various experiments.

Contribution

A novel multimodal graph embedding approach that fuses multiple relationship modalities to improve multi-robot team division.

Findings

Successfully determines correct robot teams in various scenarios

Outperforms baseline methods using single-mode information

Effective in both simulated and physical robot systems

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.