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Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation.

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  • 1Robotics, Vision and Control Group, Universidad de Sevilla, 41092 Sevilla, Spain. lrodriguez15@us.es.

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Summary
This summary is machine-generated.

This study introduces a system for identifying wind features like gusts and shear for energy-efficient navigation in Small Unmanned Aerial Systems (UAS). It provides real-time wind estimates and predictions without extra sensors, enhancing flight path optimization.

Keywords:
UASgustmulti-platform integrationwind estimationwind predictionwind shear

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Area of Science:

  • Aerospace Engineering
  • Atmospheric Science
  • Robotics

Background:

  • Energy-efficient navigation is crucial for Small Unmanned Aerial Systems (UAS).
  • Accurate identification of wind features like gusts and shear is essential for optimizing flight paths.
  • Existing systems may require additional sensors, increasing complexity and cost.

Purpose of the Study:

  • To develop a system for real-time identification and prediction of wind features for UAS.
  • To enable energy-efficient trajectory computation without additional hardware.
  • To enhance UAS navigation capabilities through advanced wind field characterization.

Main Methods:

  • Integration of navigation systems and airspeed readings for direct wind vector estimation.
  • Utilizing atmospheric models and statistical analyses for wind field prediction, incorporating big data from previous flights.
  • Employing Weibull probability density functions and Genetic Algorithms (GA) for wind speed distribution analysis.
  • Applying Gaussian Process regression for continuous gust characterization.

Main Results:

  • Real-time wind estimation and prediction achieved at 1 Hz, with wind map updates at 0.4 Hz.
  • Prediction convergence within a 95% confidence interval in approximately 30 seconds.
  • Validation through simulations, Software-In-The-Loop testing, and real flight telemetry data.

Conclusions:

  • The developed system effectively identifies and predicts wind features for UAS.
  • The modular, decentralized architecture enhances system upgradeability and maintainability.
  • The solution offers a cost-effective and sensor-independent approach to improving UAS navigation efficiency.