AI Wireless Hand🖐️Gesture🤟Controlled Robot🤖 Using Raspberry Pi Pico, RP2040, ARM Cortex-M0+ with OpenCV & Python | AI Wireless Hand Gesture Recognition & Home Automation Using Raspberry Pi Pico with OpenCV & Python | Wireless Zigbee Multiple Color(R-G-B-Y-O) Detection Robot🤖Using Raspberry Pi Pico with Python-OpenCV | Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry Pi Pico with OpenCV & Python. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/ https://www.svskits.in/ http://svsembedded.in/ http://www.svskit.com/ M1: +91 9491535690 M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects https://svsembedded.com/ieee_2022.php 135 Projects https://svsembedded.com/ieee_2021.php 151 Projects https://svsembedded.com/ieee_2020.php 103 Projects https://svsembedded.com/ieee_2019.php 61 Projects https://svsembedded.com/ieee_2018.php 171 Projects https://svsembedded.com/ieee_2017.php 170 Projects https://svsembedded.com/ieee_2016.php 67 Projects https://svsembedded.com/ieee_2015.php 55 Projects https://svsembedded.com/ieee_2014.php 43 Projects https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr… *********************************************************** Creating an AI wireless hand gesture-controlled robot using a Raspberry Pi Pico with OpenCV and Python is an exciting project! This guide will provide you with a general overview of the steps involved. Keep in mind that this is a complex project, and you may need to adapt the instructions based on your specific components, requirements, and programming skills. Let’s get started: Materials Needed: 1. Raspberry Pi Pico 2. Raspberry Pi Camera Module 3. Motor driver (e.g., L298N or L293D) 4. DC motors and wheels 5. Power source (battery pack) 6. Chassis for the robot 7. Jumper wires 8. Hand-worn sensor (e.g., flex sensors, accelerometer, or IMU) 9. Wi-Fi module (optional, for wireless control) Steps: 1. Set Up Raspberry Pi Pico: • Set up the Raspberry Pi Pico with the required firmware and libraries. • Make sure you have a Python development environment set up on the Pico. 2. Install OpenCV: • Install OpenCV on your Raspberry Pi Pico. You can use the pip package manager to do this. 3. Connect Hardware: • Connect the motor driver to the Raspberry Pi Pico’s GPIO pins. • Connect the DC motors to the motor driver’s output pins. • Connect the Raspberry Pi Camera Module to the Pico. 4. Capture Hand Gestures: • Use the Raspberry Pi Camera Module to capture real-time images of the hand gestures. • Process the images using OpenCV to detect and extract the hand gesture features. 5. Implement Gesture Recognition: • Train a machine learning model (such as a Convolutional Neural Network or SVM) to recognize hand gestures. • Preprocess the captured images, extract relevant features, and feed them to the model for classification. • Map each recognized gesture to a specific robot command (e.g., move forward, turn left, stop, etc.). 6. Wireless Communication (Optional): • If you want to control the robot wirelessly, you can add a Wi-Fi module to the Raspberry Pi Pico. • Create a simple server on the Pico that listens for commands from a remote device (e.g., smartphone or computer). 7. Robot Movement: • Depending on the recognized gesture, send commands to the motor driver to control the movement of the robot. • Implement the logic to make the robot move forward, backward, turn left, turn right, and stop. 8. Testing and Refinement: • Test the hand gesture-controlled robot in a controlled environment. • Refine the gesture recognition algorithm and the robot’s response based on the testing results. 9. Enclosure and Aesthetics: • Design and build a chassis for the robot to protect its components and give it a finished look. 10. User Interface (Optional): • Create a user interface on a remote device (e.g., a smartphone app or a web page) to visualize the robot’s camera feed and control its movements.
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