Design of a tool for failures detection in multirrotor systems
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Abstract
Unmanned vehicles (UAVs) with multirotors, have a control system that focuses mainly on seeking vehicle stability during flight, however, it does not allow it to detect or predict when a failure occurs, and often does not respond in the best way. This article initially presents 9 categories of failures that could be found during the flight of a UAV, as well as the design of a recurrent neural network capable of detecting patterns in the data series taken from a set of sensors, mounted on the drone. The comparison of two different networks is presented, the LSTM and the GRU, where it is shown that the GRU network is the most appropriate for this type of problem, obtaining up to 97% success with the training data used. In addition, a response to failures is proposed through a communication protocol called MAVLink, which would inform the user of the presence of the failure, in addition to disconnecting the motor if necessary.
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