Dynamic regulation process of facial expression robot
表情机器人的动态情绪调节过程研究

This paper deals with the emotion state-space model and the implementation of robot facial expression based on the probabilistic finite-state machine for the dynamic emotion regulation. The emotion state space is defined and the stimulating transition probabilities of different emotion state are acquired. The inhibitory characteristic coefficient and the human machine relationship coefficient are merged with Grossian emotional regulation process. The corresponding performances of parameters are enhanced using the genetic algorithm optimization and the real-time regulation of selfadaptive mutation probabilistic operator and cross-over operator. The quantitative analysis of the model parameters is made. The results generated by the emotion expression model are verified using the 23-degree-of-freedom expression robot platform. Moreover, the interactive effects are analyzed by the statistical algorithm. It also shows that the emotion expression model can acquire online expressive results and get rid of the single expressive interaction mode comparing to traditional algorithms.