DESIGN AND DEVELOPMENT OF A MACHINE LEARNINGBASED GESTURE-CONTROLLED 3D PRINTED ROBOTIC HAND USING COMPUTER VISION

This paper introduces a 3D-printed humanoid robotic hand made from a bio-based plastic material, PLA(Polylactic Acid), controlled through human-machine collaboration utilizing machine learning and computervision algorithms. The system is designed to replicate real-time actions through gesture recognition, a noninvasivetechnique that enables a wide range of real-time interactions. A Kinect sensor is employed due to itsmultiple features, allowing for precise and versatile user interaction. The paper suggests using a non-invasive3D sensor to regulate manipulation in real time. The Kinect sensor can find objects in three dimensions andrecognise gestures in a realistic way. Using skeletal data from the Kinect, the status of the user’s right arm iscaptured and sent to the 3D-printed robotic hand. Mimicking human movements in this way is an effectivemethod for controlling robotic operations. The system is calibrated to recognize specific gestures, accuratelyidentifying the user’s hand state and angles, streamlining the process and enhancing overall efficiency.Keywords: 3D Printing, PLA, Computer Vision, Kinect Sensor, Gesture Recognition