Development and Analysis of a Drone Control Interface Using EMG Sensor and Arduino Uno R3

Abstract

A new drone control interface was created for this research project which utilized the Analog EMG Sensor (OYMotion) along with an Arduino UNO R3 in order to test its effectiveness as a demand for more organic ways of interacting with drones led to the initiation of this project. The analysis of electric signals that came from muscular contractions has been accomplished through employing the EMG sensor accompanied by Robot Gravity: IO Expansion Shield (Model: SEN0240) designed especially for Arduino UNO R3. Arduino IDE played a significant role in developing and integrating of the proposed control interface to work on DJI Tells drone and the translation of EMG sensory output into input signal enabled us to direct and maneuver drones. Various commands were used to instruct the DJI Tells drone to perform movements including takeoff and landing alongside other directional maneuvers. Based on the project's outcomes, it can be concluded that its suggested drone control interface is both feasible and efficient, and valuable information was gathered on solving issues related to human-drone interactions via the use of muscle signals. The results suggest that using Bio-based interface controls may be a propitious route towards achieving more innate and straightforward drone operation methods, and areas for future investigation consist of perfecting control algorithms and examining how machine learning techniques can improve flexibility and effectiveness in controlling interfaces.