Robot Motion Control Using EMG Signals and Expert System for Teleoperation

Abstract

In this paper an approach for a human robot interface (HRI) is proposed, based on electromyographic (EMG) signals interpretation, utilizing a rule-based expert system. The developed approach uses the EMG signals during the motion of the elbow and wrist joint of a human for moving the arm on a plane. After processing, these signals are passed through the rule-based expert system in order to move a KUKA LWR robot according to the movement of the human forearm. Signals from the bicep, triceps, flexor carpi, and extensor carpi muscles are extracted using four surface EMG electrodes, one in each muscle. These signals are then normalized, rectified and passed through a root mean square (RMS) algorithm twice. The main advantage of the proposed method compared to other EMG analysis and implementation is that this system makes use of only 4 EMG signals and does not need the interference of other position tracking sensors or machine learning techniques. The experimental results show that a rule-based expert system can be used adequately for the teleoperation of a two joints planar robotic arm.