Bi-Wheel Rescue Robot with sEMG Powered Robotic Gripper Over IOT Framework in Emergency and Rescue Operations
P. Shiva Subhashini1, N. Bhoopal2, Pandurang S. Mirajkar3, G. Govardhana Chary4
1P. Shiva Subhashini, Department of Electronics and Communication Engineering B V Raju Institute of Technology, Hyderabad, Telangana.
2N. Bhoopal, Department of Electronics and Communication Engineering B V Raju Institute of Technology, Hyderabad, Telangana.
3Pandurang S. Mirajkar, Assistant Professor, Department of Electronics and Communication Engineering B V Raju Institute of Technology, Hyderabad, Telangana.
4G. Govardhana Chary, Department of Electronics and Communication Engineering B V Raju Institute of Technology, Hyderabad, Telangana.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 24 May 2019 | PP: 469-475 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F10960486S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Electromyography, IOT Internet of Things, WiFi – Wireless Fidelity, Robotic Gripper, Forearm muscles.
Scope of the Article: Communication