Design and Development of a Robust Control Platform for a 3-Finger Robotic Gripper Using EMG-Derived Hand Muscle Signals in NI LabVIEW

Abstract

Robots are increasingly present in everyday life, replacing human involvement in various domains. In situations involving danger or life-threatening conditions, it is safer to deploy robots instead of humans. However, there are still numerous applications where human intervention remains indispensable. The strategy to control a robot can be developed based on intelligent adaptive programmed algorithms or by harnessing the physiological signals of the robot operator, such as body movements, brain EEG, and muscle EMG which is a more intuitive approach. This study focuses on creating a control platform for a 3-finger gripper, utilizing Electromyography (EMG) signals derived from the operator’s forearm muscles. The developed platform consisted of a Robotiq three-finger gripper, a Delsys Trigno wireless EMG, as well as an NI CompactRIO data acquisition platform. The control process was developed using NI LabVIEW software, which extracts, processes, and analyzes the EMG signals, which are subsequently transformed into control signals to operate the robotic gripper in real-time. The system operates by transmitting the EMG signals from the operator's forearm muscles to the robotic gripper once they surpass a user-defined threshold. To evaluate the system's performance, a comprehensive set of regressive tests was conducted on the forearm muscles of three different operators based on four distinct case scenarios. Despite of the gripper’s structural design weakness to perform pinching, however, the results demonstrated an impressive average success rate of 95% for tasks involving the opening and closing of the gripper to perform grasping. This success rate was consistent across scenarios that included alterations to the scissor configuration of the gripper.