[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9733":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":16,"starSnapshotCount":16,"syncStatus":47,"lastSyncTime":48,"discoverSource":49},9733,"AirSim","microsoft\u002FAirSim","microsoft","Open source simulator for autonomous vehicles built on Unreal Engine \u002F Unity, from Microsoft AI & Research","https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002F",null,"C++",18233,4905,572,681,0,3,15,78,16,45,"Other",false,"main",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"ai","airsim","artificial-intelligence","autonomous-quadcoptor","autonomous-vehicles","computer-vision","control-systems","cross-platform","deep-reinforcement-learning","deeplearning","drones","pixhawk","platform-independent","research","self-driving-car","simulator","unreal-engine","2026-06-12 02:02:11","## Project AirSim announcement\r\n\r\nMicrosoft and IAMAI collaborated to advance high-fidelity autonomy simulations through Project AirSim—the evolution of AirSim— released under the MIT license as part of a DARPA-supported initiative.  IAMAI is proud to have contributed to these efforts and has published its version of the Project AirSim repository at [github.com\u002Fiamaisim\u002FProjectAirSim](https:\u002F\u002Fgithub.com\u002Fiamaisim\u002FProjectAirSim).\r\n\r\n## AirSim announcement: This repository will be archived in the coming year \r\n\r\nIn 2017 Microsoft Research created AirSim as a simulation platform for AI research and experimentation. Over the span of five years, this research project has served its purpose—and gained a lot of ground—as a common way to share research code and test new ideas around aerial AI development and simulation. Additionally, time has yielded advancements in the way we apply technology to the real world, particularly through aerial mobility and autonomous systems. For example, drone delivery is no longer a sci-fi storyline—it’s a business reality, which means there are new needs to be met. We’ve learned a lot in the process, and we want to thank this community for your engagement along the way. \r\n\r\nIn the spirit of forward momentum, we will be releasing a new simulation platform in the coming year and subsequently archiving the original 2017 AirSim. Users will still have access to the original AirSim code beyond that point, but no further updates will be made, effective immediately. Instead, we will focus our efforts on a new product, Microsoft Project AirSim, to meet the growing needs of the aerospace industry. Project AirSim will provide an end-to-end platform for safely developing and testing aerial autonomy through simulation. Users will benefit from the safety, code review, testing, advanced simulation, and AI capabilities that are uniquely available in a commercial product. As we get closer to the release of Project AirSim, there will be learning tools and features available to help you migrate to the new platform and to guide you through the product. To learn more about building aerial autonomy with the new Project AirSim, visit [https:\u002F\u002Faka.ms\u002Fprojectairsim](https:\u002F\u002Faka.ms\u002Fprojectairsim).\r\n\r\n# Welcome to AirSim\r\n\r\nAirSim is a simulator for drones, cars and more, built on [Unreal Engine](https:\u002F\u002Fwww.unrealengine.com\u002F) (we now also have an experimental [Unity](https:\u002F\u002Funity3d.com\u002F) 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.\r\n\r\nOur 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.\r\n\r\n**Check out the quick 1.5 minute demo**\r\n\r\nDrones in AirSim\r\n\r\n[![AirSim Drone Demo Video](docs\u002Fimages\u002Fdemo_video.png)](https:\u002F\u002Fyoutu.be\u002F-WfTr1-OBGQ)\r\n\r\nCars in AirSim\r\n\r\n[![AirSim Car Demo Video](docs\u002Fimages\u002Fcar_demo_video.png)](https:\u002F\u002Fyoutu.be\u002Fgnz1X3UNM5Y)\r\n\r\n\r\n## How to Get It\r\n\r\n### Windows\r\n[![Build Status](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Factions\u002Fworkflows\u002Ftest_windows.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Factions\u002Fworkflows\u002Ftest_windows.yml)\r\n* [Download binaries](https:\u002F\u002Fgithub.com\u002FMicrosoft\u002FAirSim\u002Freleases)\r\n* [Build it](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fbuild_windows)\r\n\r\n### Linux\r\n[![Build Status](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Factions\u002Fworkflows\u002Ftest_ubuntu.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Factions\u002Fworkflows\u002Ftest_ubuntu.yml)\r\n* [Download binaries](https:\u002F\u002Fgithub.com\u002FMicrosoft\u002FAirSim\u002Freleases)\r\n* [Build it](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fbuild_linux)\r\n\r\n### macOS\r\n[![Build Status](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Factions\u002Fworkflows\u002Ftest_macos.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Factions\u002Fworkflows\u002Ftest_macos.yml)\r\n* [Build it](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fbuild_macos)\r\n\r\nFor more details, see the [use precompiled binaries](docs\u002Fuse_precompiled.md) document. \r\n\r\n## How to Use It\r\n\r\n### Documentation\r\n\r\nView our [detailed documentation](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002F) on all aspects of AirSim.\r\n\r\n### Manual drive\r\n\r\nIf 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.\r\n\r\n[More details](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fremote_control)\r\n\r\n![record screenshot](docs\u002Fimages\u002FAirSimDroneManual.gif)\r\n\r\n![record screenshot](docs\u002Fimages\u002FAirSimCarManual.gif)\r\n\r\n\r\n### Programmatic control\r\n\r\nAirSim 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.\r\n\r\nThese 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.\r\n\r\nNote that you can use [SimMode setting](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fsettings#simmode) to specify the default vehicle or the new [ComputerVision mode](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fimage_apis#computer-vision-mode-1) so you don't get prompted each time you start AirSim.\r\n\r\n[More details](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fapis)\r\n\r\n### Gathering training data\r\n\r\nThere 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.\r\n\r\n![record screenshot](docs\u002Fimages\u002Frecord_data.png)\r\n\r\nA 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.\r\n\r\n### Computer Vision mode\r\n\r\nYet 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.\r\n\r\n[More details](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fimage_apis)\r\n\r\n### Weather Effects\r\n\r\nPress F10 to see various options available for weather effects. You can also control the weather using [APIs](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fapis#weather-apis). Press F1 to see other options available.\r\n\r\n![record screenshot](docs\u002Fimages\u002Fweather_menu.png)\r\n\r\n## Tutorials\r\n\r\n- [Video - Setting up AirSim with Pixhawk Tutorial](https:\u002F\u002Fyoutu.be\u002F1oY8Qu5maQQ) by Chris Lovett\r\n- [Video - Using AirSim with Pixhawk Tutorial](https:\u002F\u002Fyoutu.be\u002FHNWdYrtw3f0) by Chris Lovett\r\n- [Video - Using off-the-self environments with AirSim](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=y09VbdQWvQY) by Jim Piavis\r\n- [Webinar - Harnessing high-fidelity simulation for autonomous systems](https:\u002F\u002Fnote.microsoft.com\u002FMSR-Webinar-AirSim-Registration-On-Demand.html) by Sai Vemprala\r\n- [Reinforcement Learning with AirSim](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Freinforcement_learning) by Ashish Kapoor\r\n- [The Autonomous Driving Cookbook](https:\u002F\u002Faka.ms\u002FAutonomousDrivingCookbook) by Microsoft Deep Learning and Robotics Garage Chapter\r\n- [Using TensorFlow for simple collision avoidance](https:\u002F\u002Fgithub.com\u002Fsimondlevy\u002FAirSimTensorFlow) by Simon Levy and WLU team\r\n\r\n## Participate\r\n\r\n### Paper\r\n\r\nMore technical details are available in [AirSim paper (FSR 2017 Conference)](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.05065). Please cite this as:\r\n```\r\n@inproceedings{airsim2017fsr,\r\n  author = {Shital Shah and Debadeepta Dey and Chris Lovett and Ashish Kapoor},\r\n  title = {AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles},\r\n  year = {2017},\r\n  booktitle = {Field and Service Robotics},\r\n  eprint = {arXiv:1705.05065},\r\n  url = {https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.05065}\r\n}\r\n```\r\n\r\n### Contribute\r\n\r\nPlease take a look at [open issues](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fairsim\u002Fissues) if you are looking for areas to contribute to.\r\n\r\n* [More on AirSim design](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fdesign)\r\n* [More on code structure](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fcode_structure)\r\n* [Contribution Guidelines](CONTRIBUTING.md)\r\n\r\n### Who is Using AirSim?\r\n\r\nWe are maintaining a [list](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Fwho_is_using) 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](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fairsim\u002Fissues).\r\n\r\n## Contact\r\n\r\nJoin our [GitHub Discussions group](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fdiscussions) to stay up to date or ask any questions.\r\n\r\nWe also have an AirSim group on [Facebook](https:\u002F\u002Fwww.facebook.com\u002Fgroups\u002F1225832467530667\u002F). \r\n\r\n\r\n## What's New\r\n\r\n* [Cinematographic Camera](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3949)\r\n* [ROS2 wrapper](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3976)\r\n* [API to list all assets](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3940)\r\n* [movetoGPS API](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3746)\r\n* [Optical flow camera](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3938)\r\n* [simSetKinematics API](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F4066)\r\n* [Dynamically set object textures from existing UE material or texture PNG](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3992)\r\n* [Ability to spawn\u002Fdestroy lights and control light parameters](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3991)\r\n* [Support for multiple drones in Unity](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpull\u002F3128)\r\n* [Control manual camera speed through the keyboard](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim\u002Fpulls?page=6&q=is%3Apr+is%3Aclosed+sort%3Aupdated-desc#:~:text=1-,Control%20manual%20camera%20speed%20through%20the%20keyboard,-%233221%20by%20saihv) \r\n\r\nFor complete list of changes, view our [Changelog](docs\u002FCHANGELOG.md)\r\n\r\n## FAQ\r\n\r\nIf you run into problems, check the [FAQ](https:\u002F\u002Fmicrosoft.github.io\u002FAirSim\u002Ffaq) and feel free to post issues in the  [AirSim](https:\u002F\u002Fgithub.com\u002FMicrosoft\u002FAirSim\u002Fissues) repository.\r\n\r\n## Code of Conduct\r\n\r\nThis project has adopted the [Microsoft Open Source Code of Conduct](https:\u002F\u002Fopensource.microsoft.com\u002Fcodeofconduct\u002F). For more information see the [Code of Conduct FAQ](https:\u002F\u002Fopensource.microsoft.com\u002Fcodeofconduct\u002Ffaq\u002F) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\r\n\r\n\r\n## License\r\n\r\nThis project is released under the MIT License. Please review the [License file](LICENSE) for more details.\r\n\r\n\r\n","AirSim是微软AI与研究团队基于Unreal Engine\u002FUnity开发的一款开源自动驾驶车辆模拟器。该项目提供了高保真的物理和视觉仿真环境，支持软件在环和硬件在环的仿真测试，兼容PX4、ArduPilot等主流飞行控制器。AirSim的核心功能包括为无人机、汽车等提供高度可定制的虚拟测试场景，特别适用于深度学习、计算机视觉以及强化学习算法的研发和验证。它非常适合于科研机构、高校及企业，在无需实际硬件的情况下快速迭代和评估自动驾驶技术。尽管原版AirSim即将被新的Project AirSim取代，但其作为实验平台的价值仍然不可忽视。",2,"2026-06-11 03:24:28","top_topic"]