Computer Vision Controlled Robot Arm
This project explores the connection between computer vision and robotics by implementing a robot arm that can be controlled through visual hand-based input. Instead of relying only on traditional controllers, the system uses vision-based detection to interpret user movement and convert it into physical robotic motion. The project helped build practical experience in robotics, hardware control, and real-time AI interaction.
The Challenge
Robotic arms are commonly controlled through manual controllers, predefined scripts, or specialized interfaces. These approaches can be less intuitive for beginners and limit natural human-machine interaction. The project aims to make robot arm control more interactive by using computer vision as a natural control layer.
The Solution
The solution uses a computer vision pipeline to detect hand movement or gestures and translate them into control signals. These signals are sent to the robot arm hardware, allowing the arm to respond to the user’s movement in real time. This creates an intuitive bridge between human gestures and robotic actuation.
Architecture
The workflow starts with a camera capturing the user’s hand movement. Python processes the video stream using OpenCV and MediaPipe, extracts hand landmarks or gesture positions, converts those values into movement commands, and sends the commands to the hardware controller. The robot arm then moves according to the interpreted command.