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This paper addresses challenges in agricultural unmanned aerial vehicle (A-UAV) positioning, emphasizing the significance of accurate position estimation for applications like coverage path planning under depended noises. The study introduces a solution involving a PCA-based maximum correntropy Kalman filter (PCA-MCKF) to mitigate issues such as lowaltitude flight control, inaccurate position estimation due to coloured noise, and non-Gaussian distribution, including wind effects. Comparative analysis with traditional methods, such as Kalman filter (KF), PCA-KF, and PCA-MCKF, is conducted using four rotor-wing UAVs with linear and nonlinear dynamical models. The paper employs interval type-2 Fuzzy PID as an intelligent controller method and constant acceleration and constant velocity manoeuvre models for estimation. Root mean square error is used as the accuracy metric, and real-time simulations in Webots demonstrate the superiority of the proposed PCA-MCKF in enhancing agricultural UAV applications.
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Investigating Errors Observed during UAV-Based Vertical Measurements Using Computational Fluid Dynamics
Unmanned Aerial Vehicles (UAVs) are a popular platform for air quality measurements. For vertical measurements, rotary-wing UAVs are particularly well-suited. However, an important concern with rotary-wing UAVs is how the rotor-downwash affects measurement accuracy. Measurements from a recent field campaign showed notable discrepancies between data from ascent and descent, which suggested the UAV downwash may be the cause. To investigate and explain these observed discrepancies, we use high-fidelity computational fluid dynamics (CFD) simulations to simulate a UAV during vertical flight. We use a tracer to model a gaseous pollutant and evaluate the impact of the rotor-downwash on the concentration around the UAV. Our results indicate that, when measuring in a gradient, UAV-based measurements were ∼50% greater than the expected concentration during descent, but they were accurate during ascent, regardless of the location of the sensor. These results provide an explanation for errors encountered during vertical measurements and provide insight for accurate data collection methods in future studies.
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- Award ID(s):
- 2125997
- PAR ID:
- 10392192
- Date Published:
- Journal Name:
- Drones
- Volume:
- 6
- Issue:
- 9
- ISSN:
- 2504-446X
- Page Range / eLocation ID:
- 253
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/drones6090253
