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A procedure to control all six DOF (degrees of freedom) of a UAV (unmanned aerial vehicle) without an external reference system and to enable fully autonomous flight is presented here. For 2D positioning the principle of optical flow is used. Together with the output of height estimation, fusing ultrasonic, infrared and inertial and pressure sensor data, the 3D position of the UAV can be computed, controlled and steered. All data processing is done on the UAV. An external computer with a pathway planning interface is for commanding purposes only. The presented system is part of the AQopterI8 project, which aims to develop an autonomous flying quadrocopter for indoor application. The focus of this paper is 2D positioning using an optical flow sensor. As a result of the performed evaluation, it can be concluded that for position hold, the standard deviation of the position error is 10cm and after landing the position error is about 30cm.

Unmanned Aerial Vehicles (UAVs) are playing an increasingly important role in a wide variety of areas and the range of applications increases daily, which can also be seen in the research of the topic. At the University of Wuerzburg drones are to be used in a project, where the aim is to catch possibly dangerous UAVs in mid air using a net, carried by two drones. This very special scenario poses new problems to the control of the drones, so that traditionally used Proportional-Integral-Differential (PID) controllers are no longer sufficient. Therefore a model-based adaption mechanism was chosen to be used to control the altitude of the drones. Though adaption based controllers have been used in the field of drone research before, the existing algorithms had to be modified to work with the special conditions of the altitude control of UAVs. The design and implementation of the modified Model Reference Adaptive Controllers (MRACs) with an updated Massachusetts Institute of Technology (MIT)-rule will be presented in this work. The behavior of the drones with and without the adaption as well as the changes to the original MRAC are then compared in simulation as well as on a real system and show very promising results in further improving the stability of the altitude control of the drones.

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This paper proposes an attitude determination system for small Unmanned Aerial Vehicles (UAV) with a weight limit of 5 kg and a small footprint of 0.5 m×0.5 m. The system is realized by coupling single-frequency Global Positioning System (GPS) code and carrier-phase measurements with the data acquired from a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) using consumer-grade Components-Off-The-Shelf (COTS) only. The sensor fusion is accomplished using two Extended Kalman Filters (EKF) that are coupled by exchanging information about the currently estimated baseline. With a baseline of 48 cm, the static heading accuracy of the proposed system is comparable to the one of a commercial single-frequency GPS heading system with an accuracy of approximately 0.25 • /m. Flight testing shows that the proposed system is able to obtain a reliable and stable GPS heading estimation without an aiding magnetometer.