# AirSimMulti

**Repository Path**: caomengnb/AirSimMulti

## Basic Information

- **Project Name**: AirSimMulti
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-04
- **Last Updated**: 2024-07-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

**With the official version of AirSim adding full support for multiple vehicles as well as recording from multiple cameras, this version is deprecated.**

# Welcome to AirSim

AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop 
with popular flight controllers such as Pixhawk for physically and visually realistic simulations. It is developed as an Unreal plugin that can 
simply be dropped in to any Unreal environment you want. 

Our goal is to develop AirSim as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms 
for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way.

**Check out the quick 1.5 minute demo**

[![AirSim Demo Video](docs/images/demo_video.png)](https://youtu.be/-WfTr1-OBGQ)

# Development Status

This project is under heavy development. The current release is in beta and all APIs are subject to change. The next major features currently in works are standalone mode, 
several API enhancements and Python client support. We welcome contributions!

# How to Get It
## Prerequisites
To get the best experience you will need Pixhawk or compatible device and a RC controller. This enables the "hardware-in-loop simulation" for 
more realistic experience. [Follow these instructions](docs/prereq.md) on how to get it, set it up and other alternatives.

## Windows
There are two ways to get AirSim working on your machine. Click on below links and follow the instructions.

1.  [Build it and use it with Unreal](docs/build.md)
2.  [Use the precompiled binaries](docs/use_precompiled.md)

## Linux
[Build AirSim on Ubuntu 16.04](docs/linux_build.md)


# How to Use It

## Video Tutorials
- [Walkthrough Demo Video](https://youtu.be/HNWdYrtw3f0)
- [AirSim Setup  Video](https://youtu.be/1oY8Qu5maQQ) (shows you all the setup steps)

## Manual flights
If you have a Pixhawk flight controller (or compatible device) and a remote control you can manually control the drones in the simulator 
and fly around.

![record screenshot](docs/images/DroneGIF-03.gif)

[More details](docs/manual_flight.md)

## Gathering training data
There are two ways you can generate training data from AirSim for deep learning. The easiest way is to simply press the record button on the lower right corner. 
This will start writing pose and images for each frame. 

![record screenshot](docs/images/record_data.png)

If you would like more data logging capabilities and other features, [file a feature request](https://github.com/Microsoft/AirSim/issues) or contribute changes. 
The data logging code is pretty simple and you can modify it to your heart's desire.

A more complex way to generate training data is by writing client code that uses our APIs. This allows you to be in full control of how, what, where and when you want to log data. See the next section for more details.

For MavLink enabled drones, you can also use our [Log Viewer](docs/log_viewer.md) to visualize the streams of data.

You can also [playback recorded logs](docs/playback.md) for side-by-side comparison between real drone and the simulator.

## Programmatic control
The AirSim exposes easy to use APIs in order to retrieve data from the drones that includes ground truth, sensor data as well as various images. It also exposes 
APIs to control the drones in a platform independent way. This allows you to use your code to control different drones platforms, for example, Pixhawk or DJI Matrice, 
without making changes as well as without having to learn internal protocols details. 

These APIs are also available as a part of a separate independent cross-platform library so you can deploy them on an offboard computer on your vehicle. 
This way you can write and test your code in simulator and later execute it on the real drones. Transfer learning and related research is one of our focus areas.  See [custom drones](docs/custom_drone.md).

[More details](docs/apis.md)

# Participate
## Paper
You can get additional technical details in [our paper (preprint)](https://arxiv.org/abs/1705.05065). Please cite this as:
```
@inproceedings{airsim2017fsr,
  author = {Shital Shah and Debadeepta Dey and Chris Lovett and Ashish Kapoor},
  title = {AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles},
  year = {2017},
  booktitle = {Field and Service Robotics},
  eprint = {arXiv:1705.05065},
  url = {https://arxiv.org/abs/1705.05065}
}
```

## Contribute
We welcome contributions to help advance research frontiers. 

- [More on our design](docs/design.md)
- [More on our code structure](docs/code_structure.md)
- [Contribution Guidelines](docs/contributing.md)

## Contact
Join the [AirSim group at Facebook](https://www.facebook.com/groups/1225832467530667/) to stay up to date or ask any questions.

## FAQ

If you run into problems, check the [FAQ](docs/faq.md) and feel free to post issues on the [AirSim github](https://github.com/Microsoft/AirSim/issues).

## License
This project is released under MIT License. Please review [License file](LICENSE) for more details.