Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : May 29, 2024 - June 01, 2024
This study proposes a social network-based approach for quantitative analysis of a robotic swarm. Social network computes the relationship among a group of individuals using a complex network methodology. This method shows the temporal dynamics of the number of communities forming a swarm. In addition, this paper discusses collective behavior with respect to the statistical variance in the directions of robots. These metrics were applied to the path formation behavior of a robotic swarm. The results showed that the robots do not change their number of communities significantly while achieving path formation. Furthermore, the robots belonging to the same communities tend to show velocity alignment. These behavior features implied the correlation to the task performance of a robotic swarm.
