Project Hub /IoT & Connected Devices/Environmental & Weather Monitoring/Object Finder Robot

Beginner

RASPBERRY PI

Project Specific

3 – 6 Hours

Complete Kit

Object Finder Robot

The Object Finder Robot uses OpenCV, computer vision, and a pre-trained TensorFlow model running on a Raspberry Pi to detect and classify specific objects.

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.

Raspberry Pi 3 - Model B+ 1.4Ghz 64-bit quad-core Processor with 1 GB Ram (Original) Rs. 5,400.00
ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 810.00
TowerPro SG90 Mini Servo – 180 degree Rotation – Standard Quality Rs. 179.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

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How It Works

The Object Finder Robot is an intelligent, vision-enabled system powered by a Raspberry Pi 4 Model B. At the core of the system is the Camera module, which continuously captures visual feed from the robot's surroundings. This video stream is processed directly on the Raspberry Pi using Python libraries like OpenCV, NumPy, and SciPy to handle image arrays and pixel data. To recognize specific items, the system utilizes TensorFlow and dlib to execute pre-trained machine learning and computer vision models, identifying the target object within the camera's field of view. Once the targeted object is successfully recognized and its position in the frame is determined, the software computes the necessary adjustments. It then sends control signals to a servo motor, which physically pans or tilts the camera mechanism to keep the object centered. Users can interact with and monitor the robot locally using a graphical user interface created with the guizero library. Power is supplied to the entire assembly via an external power supply to ensure both the Raspberry Pi and the servo motor receive stable electrical current during operation.

Why Build This

Gain hands-on experience combining machine learning models with physical hardware components like cameras and motors.

Understand how modern computer vision frameworks like OpenCV and TensorFlow process real-world video streams on edge devices.

Build a practical foundation in robotics and IoT systems that can be applied to automated tracking and smart surveillance projects.

Real-World Application

Automated object tracking, warehouse item location, smart surveillance, and assistive robotics for finding misplaced items.

Skills You'll Learn

Computer vision implementation

Machine learning model inference

Servo motor control

Graphical user interface development

Python programming

Image array processing

Safety Precautions

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

Technology Tags

RASPBERRY PI

OPENCV

TENSORFLOW

PYTHON

COMPUTER VISION

SERVO MOTOR

GUI

MACHINE LEARNING

ROBOTICS

Frequently Asked Questions

Do I need an internet connection to run the object detection?

No, once the required Python packages and pre-trained TensorFlow models are downloaded and installed on your Raspberry Pi, the system can run object detection completely offline.

Can I use any standard web camera instead of the specified Camera module?

While USB webcams can work with OpenCV in Python, the project specifically integrates with the Raspberry Pi Camera module for optimized performance and direct hardware interfacing.

Why is a separate power supply needed for the servo motor?

Servo motors can draw more current than the Raspberry Pi's GPIO pins can safely provide, so an independent power source prevents voltage drops and sudden system reboots.

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-00199