The Utilization of Wingtip Vortices in Formation Flight Aerodynamics for Unmanned Arial Vehicles

Observations of white pelicans commuting between nesting colonies and foraging areas revealed transitions from small, simple linear flock formations to larger, more complex vee and jay formations during departures, and the reverse during the return approach. Large, less-organized types of formations were relatively uncommon and short lived.Formation angles measured for filmed flocks ranged from 24° to 122° and were highly correlated with mean relative interbird distances within flocks. The number of wingbeats per hour, calculated from wingbeat frequency (beats per minute) and percent time flapping, was lowest in vee formation, progressively greater in jay, echelon, and column formation, and greatest for single birds. Wingbeats per hour decreased behind the lead bird, which usually had the highest rate, within each type of formation.Shifts between flapping and gliding were usually initiated by lead birds. Response times for these shifts were negatively related to flock size, and were shorter in vee and jay formations than in column and echelon formations.Our data suggests formation flight provides both aerodynamic–energetic and communication advantages over solitary flight.

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Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton

Applied Sciences ◽

10.3390/app11041373 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1373

Author(s):

Jingyu Zhang ◽

Zhen Liu ◽

Guangjun Zhang

Keyword(s):

Measurement Errors ◽

Structural Characteristics ◽

Geometric Model ◽

Formation Flight ◽

Depth Information ◽

Feature Points ◽

Feature Point Extraction ◽

Aerial Vehicle ◽

Uav Navigation ◽

Rigid Skeleton

Pose measurement is a necessary technology for UAV navigation. Accurate pose measurement is the most important guarantee for a UAV stable flight. UAV pose measurement methods mostly use image matching with aircraft models or 2D points corresponding with 3D points. These methods will lead to pose measurement errors due to inaccurate contour and key feature point extraction. In order to solve these problems, a pose measurement method based on the structural characteristics of aircraft rigid skeleton is proposed in this paper. The depth information is introduced to guide and label the 2D feature points to eliminate the feature mismatch and segment the region. The space points obtained from the marked feature points fit the space linear equation of the rigid skeleton, and the UAV attitude is calculated by combining with the geometric model. This method does not need cooperative identification of the aircraft model, and can stably measure the position and attitude of short-range UAV in various environments. The effectiveness and reliability of the proposed method are verified by experiments on a visual simulation platform. The method proposed can prevent aircraft collision and ensure the safety of UAV navigation in autonomous refueling or formation flight.

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