Insect flight speed estimation analysis based on a full-polarization radar

2018 ◽  
Vol 61 (10) ◽  
Author(s):  
Cheng Hu ◽  
Wenqing Li ◽  
Rui Wang ◽  
Yuanhao Li ◽  
Weidong Li ◽  
...  
Keyword(s):  
Insect Flight ◽  
Flight Speed ◽  
Speed Estimation

scholarly journals Downstroke and upstroke conflict during banked turns in butterflies

2021 ◽  
Vol 18 (185) ◽  
Author(s):  
P. Henningsson ◽  
L. C. Johansson
Keyword(s):  
General Feature ◽  
Insect Flight ◽  
Flight Path ◽  
Flight Speed ◽  
Rolling Motion ◽  
Induced Drag ◽  
Turn Radius ◽  
Thrust Production

For all flyers, aeroplanes or animals, making banked turns involve a rolling motion which, due to higher induced drag on the outer than the inner wing, results in a yawing torque opposite to the turn. This adverse yaw torque can be counteracted using a tail, but how animals that lack tail, e.g. all insects, handle this problem is not fully understood. Here, we quantify the performance of turning take-off flights in butterflies and find that they use force vectoring during banked turns without fully compensating for adverse yaw. This lowers their turning performance, increasing turn radius, since thrust becomes misaligned with the flight path. The separation of function between downstroke (lift production) and upstroke (thrust production) in our butterflies, in combination with a more pronounced adverse yaw during the upstroke increases the misalignment of the thrust. This may be a cost the butterflies pay for the efficient thrust-generating upstroke clap, but also other insects fail to rectify adverse yaw during escape manoeuvres, suggesting a general feature in functionally two-winged insect flight. When lacking tail and left with costly approaches to counteract adverse yaw, costs of flying with adverse yaw may be outweighed by the benefits of maintaining thrust and flight speed.

Effects of Air Ions on Duration and Rate of Sustained Flight of the Blowfly, Phoenicia sericata Meigen

1965 ◽  
Vol 97 (5) ◽  
pp. 552-556 ◽  
Author(s):  
M. G. Maw
Keyword(s):  
Insect Flight ◽  
Negative Ions ◽  
Flight Speed ◽  
Ion Currents ◽  
Air Ions ◽  
Positive Ions ◽  
Normal Laboratory

AbstractThat insect flight is influenced by air ions was shown when blowflies, Phoenicia sericata Meigen, were exposed to air ions at ion currents of about 3.4 × 10−11 amp. Positive ions resulted in longer, faster flights than did normal laboratory air, and there were steep increases and decreases in speed. Negative ions resulted in relatively fast, steady flight that usually lasted longer than in positively ionized or in laboratory air. After exposure to positive ions, exposure to alternating polarities resulted in a steady net increase in flight speed but exposure to alternating polarities after exposure to negative ions had no effect on flight.

Position and Speed Estimation Methods of SPMSMs using a Hall Effect Sensor

2008 ◽  
Vol 128 (2) ◽  
pp. 125-130
Author(s):  
Kan Akatsu ◽  
Nobuhiro Mitomo ◽  
Shinji Wakui
Keyword(s):  
Hall Effect ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Hall Effect Sensor

Conflict Avoidance Using Flight Speed Control in a High Density Air Corridor

2013 ◽  
Vol 61 (5) ◽  
pp. 119-124 ◽  
Author(s):  
Keisuke FUKUOKA ◽  
Noboru TAKEICHI ◽  
Yoichi NAKAMURA
Keyword(s):  
Speed Control ◽  
High Density ◽  
Flight Speed ◽  
Conflict Avoidance

Correction: Feedback Control of Flight Speed to Reduce Unmanned Aerial System Noise

2018 ◽  
Author(s):  
Matthew B. Galles ◽  
Noah H. Schiller ◽  
Kasey A. Ackerman ◽  
Brett A. Newman
Keyword(s):  
Feedback Control ◽  
Flight Speed ◽  
Unmanned Aerial System ◽  
System Noise

The Biomechanics of Insect Flight

2018 ◽  
Author(s):  
Robert Dudley
Keyword(s):  
Insect Flight

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

Sign in / Sign up

Export Citation Format

Share Document