There are two ways you can generate training data from AirSim for deep learning. 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. Transfer learning and related research is one of our focus areas.
#Flight simulator 2017 osx code#
This way you can write and test your code in the simulator, and later execute it on the real vehicles. 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. The APIs are exposed through the RPC, and are accessible via a variety of languages, including C++, Python, C# and Java. You can use these APIs to retrieve images, get state, control the vehicle and so on. For cars, you can use arrow keys to drive manually.ĪirSim exposes APIs so you can interact with the vehicle in the simulation programmatically. If you have remote control (RC) as shown below, you can manually control the drone in the simulator.
View our detailed documentation on all aspects of AirSim. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way.įor more details, see the use precompiled binaries document. 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. Similarly, we have an experimental release for a Unity plugin. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. 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. AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release).
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