Research on intention recognition method based on radial basis function neural network

Abstract

A robot should be endowed with certain collaboration experience to recognize human’s behavioral intention. This paper provides a method based on machine learning to recognize the collaborator’s intention. A radial basis function neural network model was built for offline practice of a robot to recognize intention. Some collaboration skills can be obtained by the robot by building a map between the collaborator’s intention and the system state, deducing human’s intention based on the dynamic characteristics of collaborator and robot and taking the collaborator’s intention as the feedforward information for controlling the robot so as to estimate the human’s intention online based on collaborator’s force and robot’s motion characteristics during collaboration. The proposed method can overcome the difficulties in building the human-robot collaboration model by traditional method, especially the complicated human motion model, and difficulties in estimation of impedance parameters of human body. An experiment was conducted on a motion platform with single degree of freedom. The results prove that the collaborator’s force is reduced while synchronization of human-robot collaboration is improved, so that the compliance of collaborated motion is also improved.