Microsoft is offering a preview of its new AI-powered simulator for drones, flying taxis, and other advanced aerial mobility (AAM) vehicles. Project AirSim can be used to build, train, and test autonomous drones through hyper-realistic simulations of real-world scenarios. The goal is to help drone makers encode autonomy without the need for deep expertise in AI.Get more news about Drone Simulation Company,you can vist our website!
Project AirSim is a result of five years of research and experimentation into deep learning and AI. While the earlier open-source research project is being retired, Microsoft said learnings from the same have inspired the launch of this new end-to-end platform that would allow AAM customers to test and train AI-powered aircraft in simulated 3D environments more easily. Massive amounts of data are required for training drone AI models on exactly which actions to take at each phase of flight, right from takeoff to cruising and landing. Using Project AirSim’s high-fidelity simulations, drone AI models can run through millions of flights in seconds, learning how to react to countless variables much as they would in the physical world:
How would the drone fly in rain, sleet, or snow? How would strong winds or high temperatures affect battery life? Can the drone’s camera see a turbine’s arms on an overcast day just as well as on a clear one?Using data from Bing Maps and other providers, Microsoft is curating a ready-to-use library of simulated 3D environments of both urban and rural landscapes under Project AirSim. This library would be able to pull up specific locations, like New York City or London, or generic spaces, like an airport.
In addition, the company plans to make pre-trained AI models available for specific scenarios also, such as drone infrastructure inspection, delivery and logistics, and urban air mobility.One of the early testers of Project AirSim is North Dakota-based drone startup Airtonomy. The company used AimSim simulations to train its autonomous drones that are now inspecting wind farms, surveying wildlife, and detecting leaks in oil tanks across the Midwest.As Matt Holvey, director of intelligent systems at aviation pioneer Bell, pointed out, you can’t afford to make millions of mistakes in the real world. “Often, you can’t afford to make even one.”
This is why Bell is using AirSim to train its AI model on thousands of “what if” scenarios in a matter of minutes, helping it practice and perfect a critical maneuver before attempting it in the real world.
More recently, the company used Project AirSim to prepare for NASA’s Systems Integration and Operationalization (SIO) extension project, which aims to accelerate the use of autonomous aircraft in the national airspace system. The company’s Autonomous Pod Transport (APT) aircraft flew through a corridor in the Dallas-Fort Worth area, successfully demonstrating the craft’s ability to maintain contact with ground-based radar monitoring systems.
