Design and Implementation of a Gesture-Controlled 4-DOF Robotic Manipulator

The set up and development of a four degree-of-freedom (4-DOF) robotic manipulator comprised of lightweight acrylic and powered by \((SG90)\) servo motors. In order to avoid the need for conventional kinematic and dynamic computations, machine learning (ML) based gesture detection is impacted by an appealing hand gesture interface driven by OpenCV and MediaPipe. This methodology enhances competency and user accessibility while diminishing robotic control. A bluetooth module coupled with an Arduino Nano enables wireless connectivity, facilitating the real-time transmission of gesture signals that are translated into specific servo movements. Extensive performance assessments that include force, torque and energy efficiency assessments verify the manipulators? reliability and immaculate performance during real-world testing. Although the unified integration of gesture recognition and mechanical control validates robust potential within multiple domains, for instance teleoperation in hazardous circumstances, adaptive robotics for people with disabilities, collaborative educational platforms, and unconventional human-machine interaction, its sectional and lightweight design ensures portability. In general, the suggested approach offers a cost-effective, flexible, and wireless method for performing robotic manipulation.