Human Clustering for A Partner Robot Based on Particle Swarm Optimization
Indra Adji Sulistijono, Naoyuki Kubota · 2006
This paper proposes swarm intelligence for a perceptual system of a partner robot. The robot requires the capability of visual perception to interact with a human. Basically, a robot should perform moving object extraction and clustering for visual perception used in the interaction with a human. In this paper, we propose a total system for human classification for a partner robot by using particle swarm optimization, k-means, self organizing maps and back propagation. The experimental results show that the partner robot can perform the human clustering and classification