Towards Self-Aware Multirotor Formations

In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.