Intelligent embedded and real-time ANN-based motor control for multi-rotor unmanned aircraft systems

Constant technological advancements in commercial multirotor unmanned aerial vehicles (drones) resulted in their deployment in more and more applications, ranging from entertainment to disaster management and many more domains. However, in contrast to their powerful and diverse entrance into our lifestyle and society, they do not yet provide sufficient intrinsic fail-safe mechanisms to prevent accidents that may occur due to technical problems or unforeseen flight incidents such as turbulent winds, inexperienced pilots, and so on. Therefore, in the current study, we propose the use of an integrated intelligent motor controller, which is trained to recognize incidents directly from the on-board sensors (barometer, gyroscope, compass and accelerometer) and react in real-time, adjusting the drone's motors. The goal is to provide a small, intelligent, low-power, real-time, built-in controller for multirotor UAVs that will be able to understand a dangerous scenario right before it happens, start taking counter measures to keep the drone safe, and provide the pilot with a bigger reaction-time window. We propose the use of an artificial neural network, implemented in a lightweight embedded processing board, that is able to recognize and react in real-time to various turbulent situations. Experimental results suggest that our controller is able to respond properly and timely to wind changes (turbulence) allowing the drone to maintain its expected state and path.