Indoor drone or Unmanned Aerial Vehicle (UAV) operations, automated or with pilot control, are an upcoming and exciting subset of drone use cases. Automated indoor flights tighten the requirements of stability and localization accuracy in comparison with the classic outdoor use cases which rely primarily on (RTK) GNSS for localization. In this paper the effect of multiple sensors on 3D indoor position accuracy is investigated using the flexible sensor fusion platform OASE. This evaluation is based on real-life drone flights in an industrial lab with mmaccurate ground truth measurements provided by motion capture cameras, allowing the evaluation of the sensors based on their deviation from the ground truth in 2D and 3D. The sensors under consideration for this research are: IMU, sonar, SLAM camera, ArUco markers and Ultra-Wideband (UWB) positioning with up to 6 anchors. The paper demonstrates that using this setup, the achievable 2D (3D) indoor localization error varies between 4.4 cm and 21 cm (4.9 cm and 67.2 cm) depending on the selected set of sensors. Furthermore, cost/accuracy tradeoffs are included to indicate the relative importance of different sensor combinations depending on the (engineering) budget and use case. These lab results were validated in a Proof of Concept deployment of an inventory scanning drone with more than 10 flight hours in a 65 000 m 2 warehouse. By combining lab results and real-life deployment experiences, different subsets of sensors are presented as a minimal viable solution for three different indoor use cases considering accuracy and cost: a large drone with little weight-and cost restrictions, one or more medium sized drones, and a swarm of weight and cost restricted nano drones.
This article presents sensor fusion techniques for ultra-wideband-based localization to achieve sufficient accuracy and robustness for the control of vehicles in an industrial environment. We propose two outlier detection methods in combination with an extended Kalman Filter, and present experimental validation where 10 cm accuracy is achieved even in difficult NLOS conditions.
International audienceThis chapter gives a detailed description of a test setup developed at KU Leuven for the launch and recovery of unpropelled tethered airplanes. The airplanes are launched by bringing them up to flying speed while attached by a tether to the end of a rotating arm. In the development of the setup, particular care was taken to allow experimental validation of advanced estimation and control techniques such as moving horizon estimation and model predictive control. A detailed overview of the hardware, sensors and software used on this setup is given in this chapter. The applied estimation and control techniques are outlined in this chapter as well, and an analysis of the closed loop performance is given
Airborne wind energy systems like power-generating kites promise to become a sustainable and safe alternative for today's fossil fuel based energy production. Controlling these systems is still a challenge due to the fast and highly nonlinear dynamics. A real-world prototype, which has been build at the K.U. Leuven, consists of a carousel which drives an airplane being attached at one of its arms. In the first part of this paper, we propose a nonlinear grey box model which is developed for controlling this kite carousel. In the second part, we present a code generation tool for nonlinear real-time MPC algorithms which exports plain C-code tailored to particular model dynamics. Numerical closed-loop simulations of the kite carousel show that auto-generated code allows to solve the resulting dynamic optimization problems within less than 900 microseconds.
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