https://github.com/oaslananka/airsim101_yolov10
Autonomous Vehicle Control System using A* pathfinding, sensor data integration, and YOLO object detection in AirSim.
https://github.com/oaslananka/airsim101_yolov10
airsim autonomous-vehicle autonomousdriving autonomousvehicles deeplearning machinelearning microsoftai object-detection pathfinding python robotics simulation ultralytics vehiclecontrol yolo yolov10
Last synced: about 1 month ago
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Autonomous Vehicle Control System using A* pathfinding, sensor data integration, and YOLO object detection in AirSim.
- Host: GitHub
- URL: https://github.com/oaslananka/airsim101_yolov10
- Owner: oaslananka
- Created: 2024-07-10T19:38:48.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-04-18T22:56:49.000Z (about 2 months ago)
- Last Synced: 2025-04-19T09:19:53.742Z (about 2 months ago)
- Topics: airsim, autonomous-vehicle, autonomousdriving, autonomousvehicles, deeplearning, machinelearning, microsoftai, object-detection, pathfinding, python, robotics, simulation, ultralytics, vehiclecontrol, yolo, yolov10
- Language: Python
- Homepage:
- Size: 13.4 MB
- Stars: 14
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Autonomous Vehicle Control System
This project demonstrates the control of an autonomous vehicle using A* pathfinding, sensor data integration, and object detection with YOLO. The system is designed to operate within a simulated environment provided by AirSim.
[](assests/test.mp4)
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Dependencies](#dependencies)
## Introduction
The Autonomous Vehicle Control System leverages A* pathfinding for navigation, integrates various sensor data for obstacle detection, and employs YOLO for object detection. The project aims to simulate autonomous vehicle behavior in a controlled environment.
## Features
- **A* Pathfinding Algorithm**: Efficient pathfinding from start to goal coordinates.
- **Sensor Data Integration**: Utilizes distance sensors to detect obstacles.
- **Object Detection**: Implements YOLOv5 for real-time object detection.
- **Pure Pursuit Control Algorithm**: Smooth path following for the vehicle.
## Installation
To set up the project, follow these steps:
1. **Clone the Repository**
```bash
git clone https://github.com/oaslananka/Airsim101_Yolov10.git
cd autonomous-vehicle-control
```
2. **Create a Virtual Environment**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install Dependencies**
```bash
pip install -r requirements.txt
```
4. **Install AirSim**
Follow the instructions to install [AirSim](https://github.com/microsoft/AirSim).
## Usage
1. **Configure AirSim**: Ensure AirSim is correctly set up and running.
2. **Run the Main Script**
```bash
python -m core.main
```
3. **Monitor Output**: The vehicle will start navigating the environment based on the defined path.
## Project Structure
```plaintext
autonomous-vehicle-control/
│
├── README.md
├── requirements.txt
├── setup.py
│
├── config/
│ ├── __init__.py
│ ├── coordinates.py
│ └── graph.py
│
├── core/
│ ├── __init__.py
│ ├── astar.py
│ ├── control.py
│ ├── main.py
│ └── sensors.py
│
├── utils/
│ ├── __init__.py
│ └── common.py
│
├── detection/
├── __init__.py
└── object_detection.py
```
## Dependencies
The project relies on several key libraries and frameworks:
- **numpy**: Fundamental package for scientific computing with Python.
- **opencv-python**: Library for computer vision.
- **ultralytics**: Implementation of the YOLO object detection model.
- **airsim**: Open-source simulator for autonomous vehicles from Microsoft AI & Research.
Install all dependencies using the provided \`requirements.txt\` file:
```bash
pip install -r requirements.txt
```
## Acknowledgments
- [AirSim](https://github.com/microsoft/AirSim) by Microsoft for the simulation environment.
- [Ultralytics YOLO](https://github.com/ultralytics) for the object detection model.