The inspection of large infrastructures is a labor and cost intensive task. Huge machines and highly specialized teams work on such tasks. In last few years UAVs or drones have been used to inspect large infrastructures. This can save huge costs and increases the safety of such inspection. The operation of such drones is mostly conducted via a controller. In this study the possibility of operating a drone via gesture control systems is elaborated and a show case is build. There for the Myo Armband and the Leap Motion sensor are connected with to AR Parrot 2.0 drone.

After a literature review which covers the gesture control systems, the drone and the inspec-tion industry, an implementation phase follows. The gesture control systems and the drone are tested on their functionalities and multiple software components are analyzed. After this phase a connection between the Leap Motion sensor and the drone is implemented and tested on the showcase.

The results of this study are that drones will take an important role in the inspection industry. The maneuverability of the AR Parrot 2.0 via the open source application Autoflight works very well for the tasks in the inspection industry. The Myo armband proves not to be applica-ble for the showcase as the steering of the drone and the gesture based software of the Myo armband are not optimal for the piloting of the AR Parrot 2.0. The Leap motion sensor proves to be useful for piloting a drone as it uses a 3D space which not only captures gestures but also movements. An full implementation of an optimally working Leap Motion piloting software could not be realized in this study, but the proof was delivered, that it is possible to build an application which will support the piloting of a drone via Leap Motion sensor. Also the show case has been completed and captured on video. With further research on this topic a system could be developed, which will match all the requirements of the inspection industry and enable operators to fly drones with the Leap Motion sensor.