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Node Location Optimization for Localizing UAVs in Urban Scenarios

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17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

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Abstract

Unmanned-Aerial-Vehicles (UAV) widespread use has grown significantly in recent years. Their insertion in the civil sector allows their implementation in the agricultural and industrial sectors, as well as their use for surveillance or delivery applications. However, the efficient development of these applications depends on the drone’s ability to position itself autonomously. Although it is common to find drones with satellite positioning systems (GNSS), these systems are insufficient for autonomous navigation in urban or indoor environments. In these scenarios, the implementation of local positioning systems (LPS) is widely extended due to their adaptability capabilities. Through the optimal distribution of the sensors that constitute this system, they can adapt to almost any environment while also improving its performance. However, the complexity of this problem has been characterized as NP-Hard, which complicates its resolution. In this paper, a genetic algorithm is developed to optimize LPS in different environments. This algorithm, pioneer in the design of LPS for UAV localization, is tested on a generated urban environment. The results obtained denote the effectiveness of the methodology by obtaining location uncertainties significantly lower than GNSS.

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Acknowledgements

This work was supported by the Spanish Research Agency (AEI) under grant number PID2019-108277GB-C21/AEI/10.13039/501100011033.

Author information

Authors and Affiliations

  1. Department of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071, León, Spain

    Paula Verde, Rubén Ferrero-Guillén, José-Manuel Alija-Pérez, Alberto Martínez-Gutiérrez, Javier Díez-González & Hilde Perez

Authors
  1. Paula Verde
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  2. Rubén Ferrero-Guillén
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  3. José-Manuel Alija-Pérez
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  4. Alberto Martínez-Gutiérrez
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  5. Javier Díez-González
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  6. Hilde Perez
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Corresponding author

Correspondence to Paula Verde .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Verde, P., Ferrero-Guillén, R., Alija-Pérez, JM., Martínez-Gutiérrez, A., Díez-González, J., Perez, H. (2023). Node Location Optimization for Localizing UAVs in Urban Scenarios. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_60

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