Version 1
: Received: 31 October 2016 / Approved: 1 November 2016 / Online: 1 November 2016 (05:22:59 CET)
Version 2
: Received: 17 January 2017 / Approved: 18 January 2017 / Online: 18 January 2017 (09:44:54 CET)
Rodriguez Salazar, L.; Cobano, J.A.; Ollero, A. Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation. Sensors2017, 17, 8.
Rodriguez Salazar, L.; Cobano, J.A.; Ollero, A. Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation. Sensors 2017, 17, 8.
Rodriguez Salazar, L.; Cobano, J.A.; Ollero, A. Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation. Sensors2017, 17, 8.
Rodriguez Salazar, L.; Cobano, J.A.; Ollero, A. Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation. Sensors 2017, 17, 8.
Abstract
This paper presents a novel system for identification of wind features such as gust and wind shear which are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Vehicles (UAVs). The proposed system generates real-time wind estimations and real-time wind predictions as well as different alerts that are triggered when a certain feature is identified. This system uses information from a standard sensor suite to combine it in a big-data approach with stored information. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach, whereas the predictions are the result of a statistical approach using atmospheric models to characterize and identify the features separately. This information is combined using a Genetic Algorithm in order to find the most probable wind speed at a certain position and fitting into a Weibull probability density function which meets the Prandtl’s power law relationship. It also uses data from the ground station to produce an extrapolation in order to generate a full 3D wind map along with the prediction windows for each feature. Since both estimations and predictions are added to a wind database (DB), the information is constantly updated producing more accurate results. The knowledge of the wind features is crucial for computing energy-efficient trajectories, so the system not only provides a solution that does not require any additional sensor but produces enough information for any trajectory generation module. The integration of the system is described and a detailed system architecture is presented. Moreover, preliminary results for Software-In-The-Loop testing of the different modules as well as results utilizing collected data from different real flights that were performed in the Seville Metropolitan Area in Andalusia (Spain) are shown. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95 % confidence interval of approximately 30 s.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
