Robotic Assistance for Physical Human-Robot Interaction Using a Fuzzy RBF Hand Impedance Compensator and a Neural Network Based Human Motion Intention Estimator

This paper proposes a robotic assistance control scheme for intuitive teaching tasks by integrating the motion intention of human and real-time hand impedance compensation based upon a fuzzy RBF (Radial Basis Function) compensator. The motion intention of a human is estimated using a feedforward neural network. The parameters of the proposed fuzzy RBF hand impedance compensator are adjusted by considering hand impedance and robot dynamics. Three robotic assistance control schemes are compared: 1) robot without an impedance compensator; 2) robot with a constant-parameter impedance compensator; 3) robot with a fuzzy RBF hand impedance compensator. Several experiments have been conducted to verify the effectiveness of the proposed approach by comparing the contour error, exerted force and task time spent on teaching tasks. Experimental results indicate that the proposed fuzzy RBF hand impedance compensator has the best assistance results among the tested robotic assistance control schemes.

Automated summary

This paper presents a robotic assistance control scheme integrating human motion intention and real-time hand impedance compensation, where a fuzzy RBF hand impedance compensator yielded the best assistance results among tested robotic assistance control schemes.