Over the past two years, the research and development team has been developing a method to identify faulty motors on a drone without interrupting a mission (automated or manual). This is of high interest as it will give the pilot more information to ensure the successful recovery of the system in the event of a fault. The team of engineers has developed a Machine learning (using Artificial Neural Networks) framework that superimposes the dynamics on the controls in order to detect and locate a fault in a rotor without compromising the mission. The graph above shows simulate a rotor fault once the drone reaches 9 m/s and the fault identification system (FIS) detects a fault 1-second later. Once the other rotors are analyzed, the Rotor1 is identified as having a fault and this information is sent back to the pilot. It's important to know that even though a major fault has occurred, the drone a capable of flying for some time until instability grows and the drone becomes unc...

Today during lunch time, took the heli out for another spin and consistently in my mind, I was wondering about the numerous time I stopped being careful after the first round of success and lost very expensive aircraft. So having such in mind, I took it slow and easy in a tail-in hover will a slight side hover. Halfway through the flight I decided (or the devil in me) decided to put the heli in a nose-in hover and my pulpitations started all over again. But was surprising was how stable the heli was even in the presence of mild winds. Managed to perform half a circuit and landed the heli once the 4min was up. Good job!

If you want to use my graphic outside Wikipedia, and its resolution or license doesn't satisfy you, write to me: 100px (Photo credit: Wikipedia ) The radian glider took a real beating at the last crash. Although it had a 'soft' landing eventually, going through the trees was not pleasant the foamy airframe . since it wasn't covered, indentations can be seen all over the aircraft.