Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry ...

Ball Tracking with OpenCV | red - green - blue - yellow - Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry Pi Pico with OpenCV & Python | Object tracking in Python using openCV | Color recognition with Opencv and Python | Ball Tracking with OpenCV | Object Tracking Based on Color Recognition with OpenCV and Python | Wireless Robot Control Using Raspberry Pi Pico. *********************************************************** 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 a color recognition-based object tracking wireless robot control system using a Raspberry Pi Pico, OpenCV, and Python involves several steps. This project will allow the robot to detect and track objects of a specific color and then control its movement wirelessly. Here's a basic outline of the process: Hardware Required: 1. Raspberry Pi Pico RP2040 ARM CORETEX M0+ 2. Robot chassis with motors and wheels 3. Motor driver board (e.g., L298N or L293D) 4. Webcam or camera module compatible with Raspberry Pi Pico 5. Power source (batteries or power supply) Software Required: 1. Thonny IDE (Python IDE for Raspberry Pi) 2. Python libraries: OpenCV and PiGPIO (for motor control) Steps: 1. Set Up Raspberry Pi Pico: • Connect the motor driver to the Raspberry Pi Pico's GPIO pins. • Connect the motors to the motor driver. 2. Install Required Libraries: • Install OpenCV and PiGPIO libraries on your Raspberry Pi Pico using Thonny IDE. 3. Capture and Process Video: • Use the Raspberry Pi Pico's camera module or a USB webcam to capture video frames. • Process the frames using OpenCV to identify objects of the desired color using color thresholding techniques. 4. Object Tracking: • Implement object tracking algorithms (e.g., color-based tracking) to continuously locate the target object within the video frames. 5. Wireless Control: • Set up a wireless communication protocol between your control device (e.g., smartphone or computer) and the Raspberry Pi Pico. You can use technologies like Bluetooth or Wi-Fi for this purpose. 6. Control Logic: • Create a control logic that receives instructions wirelessly and controls the robot's movement based on the tracked object's position. • Convert the object's position within the frame to motor control signals. For example, if the object is on the left side of the frame, increase the left motor speed, and if it's on the right, increase the right motor speed. 7. Motor Control: • Use the PiGPIO library to control the motors' speed and direction through the motor driver board. • Adjust motor speeds based on the control logic's instructions to navigate the robot toward the tracked object. 8. Execution: • Run the Python script on the Raspberry Pi Pico to start the color recognition-based object tracking and wireless control process. • Use your control device to wirelessly send commands to the robot and observe its movement. 9. Testing and Optimization: • Test the system and make necessary adjustments to the color recognition and tracking algorithms for better accuracy and responsiveness. • Optimize the motor control logic for smoother and more accurate movement. you will need to dive deeper into each step, referring to documentation and tutorials for each specific component and library. Additionally, ensure safety precautions while working with the hardware components, especially the motors and power supply.