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3 – 6 Hours

Partial Kit

AI-Based Hand Tracking for Robotic Arm Control

Control a robotic arm servo in real time using hand tracking powered by AI image processing and MediaPipe.

Originally published on ElectronicsForU

View original tutorial

Parts You'll Need

This project is sourced from another site, not written by us — this list is our best match of their components against what we stock, not a verified bill of materials. Always check the original blog (linked above) for the actual parts list before buying.

TowerPro SG90 Mini Servo – 180 degree Rotation – Standard Quality Rs. 179.00
Cable for Arduino Nano (USB 2.0 A to USB 2.0 Mini B) 30cm Rs. 69.00
ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 810.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

We don't carry yet: Other Dev Board (Indusboard / Maixduino / misc)

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How It Works

This project establishes a real-time, touchless control loop between a browser-based AI vision system and a physical robotic arm. At the heart of the setup is a webcam connected to a computer running an HTML and JavaScript environment utilizing Google's MediaPipe framework. The webcam captures video frames of the user's hand, while MediaPipe processes these frames locally in the browser to detect key hand landmarks and extract specific coordinate data, such as finger pinch distances or palm positions. The browser translates these visual coordinates into directional commands and transmits them directly to the IndusBoard Coin microcontroller via the WebSerial API over a USB Type-C cable. Upon receiving the serial data, the IndusBoard Coin processes the numeric values and adjusts the Pulse Width Modulation (PWM) signal sent to the Microgear Servo Motor. Because the servo's physical shaft position corresponds directly to the incoming PWM duty cycle, the robotic arm smoothly mirrors the gestures tracked by the AI vision pipeline in real time, creating an end-to-end edge-processing and actuation pipeline.

Why Build This

Design sophisticated human-machine interfaces (HMIs) by bridging browser-based machine learning inference with embedded microcontroller hardware.

Implement practical touchless control schemes suitable for sterile, hazardous, or high-precision industrial automation tasks.

Master asynchronous communication protocols by streaming live coordinate data from a JavaScript web application to an ESP32-class microcontroller over a serial connection.

Real-World Application

Industrial environments requiring touchless control, such as welding stations, assembly lines, soldering setups, and training simulators.

Skills You'll Learn

Computer vision integration

WebSerial API communication

Servo motor pulse-width modulation control

Browser-based machine learning inference

Serial data parsing on microcontrollers

Safety Precautions

No specific precautions noted for this build — always follow general electronics safety practices.

Technology Tags

MACHINE LEARNING

MEDIAPIPE

HAND TRACKING

SERVO MOTOR

WEBSERIAL API

INDUSBOARD COIN

ROBOTICS

ARDUINO

COMPUTER VISION

Frequently Asked Questions

Do I need an internet connection to run the hand-tracking AI model?

No, MediaPipe runs locally inside your web browser using client-side JavaScript, meaning it can execute entirely offline once the page and model weights are loaded.

How does the IndusBoard Coin communicate with the browser-based application?

Communication relies on the WebSerial API, which allows modern web browsers to establish a direct serial connection to the microcontroller over the USB Type-C cable.

Can I control multiple servo motors with this setup?

Yes, by expanding the JavaScript code to track multiple hand landmarks or gestures and sending a corresponding data array over serial, you can parse and map multiple PWM outputs on the IndusBoard Coin using the esp32servo library.

Gallery

This page is part of Kitkraft's Project Hub — a curated collection of electronics & robotics projects gathered from the maker community to help students and hobbyists discover and learn to build. Kitkraft does not claim authorship of the original project, and makes no representation as to the accuracy, completeness or safety of third-party source material. The summary and "How It Works" explanation above are AI-generated, rewritten with reference to the original tutorial — not a reproduction of it. Full credit and a direct link to the original source are provided above. If you're the original creator and have a concern about how your work is represented here, please contact us and we'll address it promptly.

Kitkraft Project Hub — curated from the maker community, credited at the source.

PRJ-01325