Bi-wheel rescue robot with sEMG powered robotic gripper over IoT framework in emergency and rescue operations

Abstract

In this paper, we will deliberate two different techniques of maneuvering a bi-wheel rescue robot with robotic gripper (BRRRG) to precisely mandate the positioning of the robot using sEMG and android mobile application over the IoT framework. The Electromyogram of a subject is captured non-invasively, amplified, rectified, filtered and quantified to precisely control the robotic gripper based on set prehensile patterns. The signals of interest are acquired from two different muscles of the upper forearm namely Flexor Carpi Radialis and Flexor Carpi Ulnaris. Android based mobile application is designed to appropriately position the chassis of the robot from anywhere in the world. It is observed that with set prehensile patterns every subject’s muscle contraction varies and hence the study presents the variation in threshold voltage for each test subject based on the gender, age and muscle buildup. With the grip offset of 0.39% and accuracy of 93-95%, its application in the field of emergency rescue can be further explored. The proposed system is designed such that the threshold voltage can be easily programmed and the uniformity with which a subject can control the robotic grip is studied.