In a previous blog, we commented on the difficulty of flying a drone in indoor environments, which are almost always dangerous and hostile areas. Until now, the skill and experience of the pilot was the guarantee of a successful operation. But you don’t just have to have these skills. Sometimes we come across circumstances that we have never experienced before and that, due to the risks and their own rarity, are not possible to rehearse previously.

Those of us who have worked in manned aviation on board, know the importance and the total need to carry out practices in flight simulators, which in a quite real way reproduce situations that, if they occurred in real life, could give us a surprise and produce an incident.

To avoid the lack of experience in cases where we cannot rehearse during real piloting, simulators are used, that are not very frequent in the world of drones.

At this time, hyper-realistic simulations are beginning to exist to train autonomous aerial vehicles that can simulate high-risk inspections.

Now, using technologies such as AI and the creation of very precise 3D worlds, trials and tests can be carried out simulating situations of all kinds, preparing pilots to prevent and memorize reactions before events never seen before in the real world.

In the previous blog we commented on the incorporation of the Metaverse as a new technology that can be an ally of drone operations. In hyper-realistic environments, with AI models hundreds of tests can be executed that accumulate learning by correcting manoeuvres and indicating to the pilot where the mistakes he is making are.

Rehearse flights in difficult environments such as strong wind, gusts, rain, high temperatures, battery or communication failures, sudden lack of visibility problems, etc.

An example of these new technologies applied to drone flight simulation is found in the Microsoft AirSim Project located on the AZURE platform, which is used to safely build, train and test, autonomous aircraft through high-fidelity simulation.

Massive amounts of data will be generated by feeding AI models that anticipate decisions about exactly what actions to take in each phase of flight, from takeoff, conducting the test, and landing. It will also be able to make available libraries of simulated 3D environments that represent diverse urban and rural landscapes, as well as a set of sophisticated pre-trained AI models to help rehearse inspection operations of infrastructures, industries both in open environments, goods delivery, urban air mobility, as in indoor operations.

AirSim can be a fundamental tool that allows us to bridge the real world with the simulated one and shows the power of the industrial Metaverse: the virtual worlds where companies will build, test and refine solutions and then bring them to the real world.

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