UAV navigation and management system based on the spectral portrait of terrain

Authors

  • F. Glugan National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • V. Lysenko National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • S. Shvorov National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • N. Pasichnyk National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • O. Opryshko National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • I. Tsygulyov National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • V. Teplyuk National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • A. Rudenskyi National University of Life and Environmental Sciences of Ukraine image/svg+xml

DOI:

https://doi.org/10.31548/energiya3(67).2023.054

Abstract

This paper focuses on investigating the navigation of unmanned aerial vehicles (UAVs) using spatial-spectral representations of the terrain. This type of navigation is valuable in situations where GPS receivers and other navigation devices fail, but its implementation requires resolving several methodological challenges. One crucial aspect to consider is the impact of changes in illumination on the spectral characteristics of objects. The adoption of satellite-based solutions relying on optical patterns is not suitable for low-flying UAVs, and instead, utilizing service data from the spectral sensors on the UAV's meter display holds greater promise. This study examines an existing method that addresses illumination changes by utilizing the LightValue parameter for different cameras, both in laboratory and field conditions. Through experimentation, it has been established that the relationship between LightValue and the intensities of color components varies individually across different cameras. To correct for natural light variations, it is proposed to employ experimentally derived relationships specific to the sensory equipment brands. When designing navigation systems based on spectral representations of the terrain, it is advisable to select objects that exhibit the most consistent optical changes with respect to illumination.

Key words: UAV, spectral portraits, correction of illumination

References

Jie Su, Jianping He, Peng Cheng, Jiming Chen (2016). A Stealthy GPS Spoofing Strategy for Manipulating the Trajectory of an Unmanned Aerial Vehicle. IFAC-Papers On Line,. 49 (22), 291-296.

Alberto Petrillo, Antonio Pescapé, Stefania Santini (2018). A collaborative approach for improving the security of vehicular scenarios: The case of platooning. Computer Communications, 122, 59-75.

Ángel Manuel Guerrero-Higueras, Noemí DeCastro-García, Vicente Matellán (2018). Detection of Cyber-attacks to indoor real time localization systems for autonomous robots. Robotics and Autonomous Systems, 99, 75-83.

Ángel Manuel Guerrero-Higueras, Noemí DeCastro-García, Vicente Matellán (2011). Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat. Computers and Electronics in Agriculture, 77, 69-73.

Bernstein, L.S., Adler-Golden, S.M., Sundberg, R.L., Levine, R.Y., Perkins, T.C., Berk, A., et al. (2005). Validation of the QUick Atmospheric Correction (QUAC) Algorithm for VNIR-SWIR Multi- and Hyperspectral Imagery. Proceedings of SPIE, 5806,. 668–678.

K. Soudani, G. Hmimina, N. Delpierre, J.-Y. Pontailler, M. Aubinet, D. Bonal, B. Caquet, A. de Grandcourt, B. Burban, C. Flechard, D. Guyon, A. Granier, P. Gross, B. Heinesh, B. Longdoz, D. Loustau, C. Moureaux, J.-M. Ourcival, S. Rambal, L. Saint André, E. Dufrêne, et al (2012). Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sensing of Environment, 123, 234-245.

Haitao Xiang, Lei Tian (2011). An automated stand-alone in-field remote sensing system (SIRSS) for in-season crop monitoring. Computers and Electronics in Agriculture, 78 (1), 1-8.

Mónica Herrero-Huerta, David Hernández-López, Pablo Rodriguez-Gonzalvez, Diego González-Aguilera, José González-Piqueras (2014). Vicarious radiometric calibration of a multispectral sensor from an aerial trike applied to precision agriculture. Computers and Electronics in Agriculture, 108, 28-38.

T. Duan, S. C. Chapman, Y. Guo, B. Zheng (2017). Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research, 210, 71-80.

Jyun-Ping Jhan, Jiann-Yeou Rau (2018). Robust and adaptive band-to-band image transform of UAS miniature multi-lens multispectral camera. Norbert Haala. Journal of Photogrammetry and Remote Sensing, 137, 47–60.

M. M.Saberioona, M.S.M. Amina, A.R. Anuarb, A. Gholizadehc, A. Wayayokd, S. Khairunniza-Bejoda Smart (2014), Assessment of rice leaf chlorophyll content using visible bands atdifferent growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation, 32, 35–45.

I. Korobiichuk, V. Lysenko, O. Opryshko. D. Komarchyk, N. Pasichnyk, A. Juś (2018). Crop Monitoring for Nitrogen Nutrition Level by Digital Camera. Automation 2018, AISC, 743. 595-603 (https://link.springer.com/chapter/10.1007/978-3-319-77179-3_56).

Vitalii Lysenko, Oleksiy Opryshko, Dmytro Komarchuk, Nadiia Pasichnyk, Nataliia Zaets, Alla Dudnyk (2017). Usage of Flying Robots for Monitoring Nitrogen in Wheat Crops. The 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 21-23 September, 2017, Bucharest, Romania, 1, 30-34.

Fangning He, Ayman Habib (2016). Automated Relative Orientation of UAV-Based Imagery in the Presence of Prior Information for the Flight Trajectory. Photogrammetric Engineering & Remote Sensing, 82 (11), 879-891.

Milton C.P. Santos, Lucas V .Santana, Alexandre S. Brandão, Mário Sarcinelli-Filho, Ricardo Carelli (2017). Indoor low-cost localization system for controlling aerial robots. Control Engineering Practice, 61, 93-111.

V. Lysenko, O. Opryshko, D. Komarchyk, N. Pasichnyk (2016). Drones camera calibration for the leaf research. Scientific Journal NUBiP, 252, 61-65.

Published

2023-09-07

Issue

Section

Статті