This repository contains code and resources for an independent research project focused on developing an autonomous robot. The robot leverages LiDAR for navigation and computer vision for real-time object recognition.
##Hardware Components
- LiDAR Sensor: For mapping and obstacle detection.
- Camera: For visual perception and object recognition.
- Microcontroller: For processing sensor data and controlling the robot's movement.
- Power Supply: Battery or power source to run the robot.
- Chassis: Physical structure to house components and provide mobility.
- Wheels/Tracks: For movement across various terrains.
- LiDAR Navigation: Real-time mapping and obstacle avoidance using LiDAR sensors.
- Vision-Based Object Recognition: Object detection and classification with deep learning models.
- Modular Codebase: Separate modules for sensor data processing, navigation, and control.
src/— Source code for navigation, perception, and control modules.models/— Pre-trained and custom-trained models for object recognition.docs/— Documentation, research notes, and setup guides.requirements.txt— Python dependencies for development and deployment.
- Clone this repository.
- Install dependencies:
pip install -r requirements.txt - Set up your environment and hardware as described in
docs/setup.md. - Connect LiDAR and camera hardware.
- Run the main script to start the robot stack.
- Fuse LiDAR and movemnt controller for robust autonomous navigation.
- Object recognition accuracy in real-world and simulated environments.
- Explore improvements in sensor integration and real-time performance.
This independent study was supported by mentor acknowledged in docs/acknowledgements.md.
For educational and research use only. See LICENSE for details.