Gesture-Powered Robotic Hand for Remote High-Risk Operations

Abstract

Robotic hands are now an indispensable component in industries such as military, medical, and industrial manufacturing because they can replicate hand movements of humans with great accuracy. They are especially essential when working in environments that pose risks to humans. Intuitive control of such systems is made possible through gesture recognition technology. This paper examines sophisticated methods and devices that increase efficiency and responsiveness of robotic hands. The flex sensors in the system pick up movement of fingers that is interpreted using an Arduino Nano microcontroller. The movement of the robotic hand is simulated in real time through servo motors. There is a good connection via wireless transceivers for remote control. The system has effective power management to facilitate effective operation. Deep learning and optimization algorithms are investigated for improving control precision. The robotic hand successfully mirrored human gestures with reliable responsiveness. Real-time control was achieved even under remote conditions.