Build & plugin installation process is the same as the original one. Please see the introduction pages in the official document. Just put the AirSim plugin to your uproject, open a level, set the gamemode to be AirSimGameMode and start Play. Now you can control scene/capture data with AirSim Python client script.
We list some of notable changes/typical use-cases:
- You no longer need to write down settings.json. The AirSimGameMode begins with ComputerVisionPawn by default. You can set the capture image size, postprocess settings from the AirSim Python client dynamically.
- We support MetaHuman models developed by Quixel. Download it from Quixel Bridge and export it to your uproject. Please see /PythonClient/bestat/generator_script.py how to use the customized API's to manipulate these models.
- You can spawn static meshes with simSpawnObject API. To change the current level, use simLoadLevel API.
- Default Segmentation initialization setting is changed to None. You need to set segmentation ID explicitly on the editor or API to objects of your interest.
Known issues:
- simSetSegmentationID will not be reflected immediately. it looks that it takes few seconds until it is reflected.
- if metahuman's hair groom uses physics simulation, it can disturbed too much on move. we recommend to turn it off manually (see https://docs.unrealengine.com/4.26/ja/WorkingWithContent/Hair/QuickStart/).
- depth value of hair groom boundary is blended with its background. any way to solve this?
The AirSim team is trying to get a better understanding of how AirSim and other simulation engines are being used across the community. If you have a couple of minutes, please take a look and respond to the AirSim survey as it does have an impact on the features and direction of the platform:
AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. Similarly, we have an experimental release for a Unity plugin.
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
Drones in AirSim
Cars in AirSim
For more details, see the use precompiled binaries document.
View our detailed documentation on all aspects of AirSim.
If you have remote control (RC) as shown below, you can manually control the drone in the simulator. For cars, you can use arrow keys to drive manually.
AirSim exposes APIs so you can interact with the vehicle in the simulation programmatically. You can use these APIs to retrieve images, get state, control the vehicle and so on. The APIs are exposed through the RPC, and are accessible via a variety of languages, including C++, Python, C# and Java.
These APIs are also available as part of a separate, independent cross-platform library, so you can deploy them on a companion computer on your vehicle. This way you can write and test your code in the simulator, and later execute it on the real vehicles. Transfer learning and related research is one of our focus areas.
Note that you can use SimMode setting to specify the default vehicle or the new ComputerVision mode so you don't get prompted each time you start AirSim.
There are two ways you can generate training data from AirSim for deep learning. The easiest way is to simply press the record button in the lower right corner. This will start writing pose and images for each frame. The data logging code is pretty simple and you can modify it to your heart's content.
A better way to generate training data exactly the way you want is by accessing the APIs. This allows you to be in full control of how, what, where and when you want to log data.
Yet another way to use AirSim is the so-called "Computer Vision" mode. In this mode, you don't have vehicles or physics. You can use the keyboard to move around the scene, or use APIs to position available cameras in any arbitrary pose, and collect images such as depth, disparity, surface normals or object segmentation.
Press F10 to see various options available for weather effects. You can also control the weather using APIs. Press F1 to see other options available.
- Video - Setting up AirSim with Pixhawk Tutorial by Chris Lovett
- Video - Using AirSim with Pixhawk Tutorial by Chris Lovett
- Video - Using off-the-self environments with AirSim by Jim Piavis
- Reinforcement Learning with AirSim by Ashish Kapoor
- The Autonomous Driving Cookbook by Microsoft Deep Learning and Robotics Garage Chapter
- Using TensorFlow for simple collision avoidance by Simon Levy and WLU team
More technical details are available in AirSim paper (FSR 2017 Conference). 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}
}
Please take a look at open issues if you are looking for areas to contribute to.
We are maintaining a list of a few projects, people and groups that we are aware of. If you would like to be featured in this list please make a request here.
Join our GitHub Discussions group to stay up to date or ask any questions.
We also have an AirSim group on Facebook.
- Python wrapper for Open AI gym interfaces.
- Python wrapper for Event camera simulation
- Voxel grid construction
- Programmable camera distortion
- Wind simulation
- Azure development environment with documentation
- ROS wrapper for multirotor and car.
For complete list of changes, view our Changelog
If you run into problems, check the FAQ and feel free to post issues in the AirSim repository.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This project is released under the MIT License. Please review the License file for more details.