|Abstract:||Accurate positioning of a drone taxi during takeoff and landing is a very important aspect regarding flight safety. However, in an urban environment where the demand of passenger is concentrated, it is difficult to obtain a reliable global navigation satellite system (GNSS) signal. Thus, the positioning result is unreliable. Therefore, we propose an ultra wideband (UWB) network on the vertiport to replace GNSS to obtain accurate positioning. Since the positioning performance of the UWB networks depends on the placement of UWB anchors, it is necessary to optimize the layout of UWB. The optimization process takes into account airborne UWB ranging error characteristics and Fresnel hole effects that depend on the received signal strength (RSS) such that the actual positioning accuracy of a drone taxi can be well predicted. In this paper, the landing path of a drone taxi set as the tag, and the UWB anchors installed in the vertiport were set as the anchor, and we optimized the UWB anchors layout about landing path of the drone taxi. As an optimization algorithm, we propose a binary genetic algorithm using a multi-objective fitness function consisting the number and positioning error of UWB. Then, we conducted hardware-in-the-loop simulation (HILS) to confirm that flight controller that is deployed position estimation algorithm of optimized UWB positioning network complete the flight following the mission path. As a result, positioning error has similar pattern to positioning accuracy of simulation result based on genetic algorithm and flight controller completed the flight along the mission path.|
Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
|Pages:||1392 - 1401|
|Cite this article:||
Jung, Doyeon, Kim, Euiho, "Optimization of UWB Network Placement Using Genetic Algorithm Supporting Drone Taxi Landing in Ertiport," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1392-1401.
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