The invention relates to a multi-sensor
information fusion method for a sterilization
robot, and aims to solve the problems that when multi-sensor
information fusion is carried out on the sterilization
robot in the medical field, the uncertainty is relatively high, the operation parameters of the sterilization
robot in different scenes cannot be accurately determined, the autonomy is relatively poor, and the like. The method comprises the following steps: generating a target classification training
data set, obtaining feature information by
processing a
hybrid deep neural network fused with multi-source signals, establishing a
decision fusion model based on a voting evidence theory, and performing training to obtain a sterilization robot operation parameter model. According to the invention, the deep neural network is combined with an improved evidence
theory method; visual signals and sensor signals of the sterilization robot are organically fused together through
deep learning in combination with an improved evidence
theory method, possible redundancy and contradiction among multi-sensor information are eliminated,
complementation is performed, the uncertainty is reduced, and then operating parameters of the sterilization robot are output more accurately.