scholarly journals Experimental Demonstration of the Vibrational Stabilization Phenomenon in Bio-Inspired Flying Robots

2018 ◽  
Vol 3 (2) ◽  
pp. 643-647 ◽  
Author(s):  
Haithem Taha ◽  
Mohammadali Kiani ◽  
Joel Navarro
2020 ◽  
Vol 5 (46) ◽  
pp. eabb1502 ◽  
Author(s):  
Haithem E. Taha ◽  
Mohammadali Kiani ◽  
Tyson L. Hedrick ◽  
Jeremy S. M. Greeter

It is generally accepted among biology and engineering communities that insects are unstable at hover. However, existing approaches that rely on direct averaging do not fully capture the dynamical features and stability characteristics of insect flight. Here, we reveal a passive stabilization mechanism that insects exploit through their natural wing oscillations: vibrational stabilization. This stabilization technique cannot be captured using the averaging approach commonly used in literature. In contrast, it is elucidated using a special type of calculus: the chronological calculus. Our result is supported through experiments on a real hawkmoth subjected to pitch disturbance from hovering. This finding could be particularly useful to biologists because the vibrational stabilization mechanism may also be exploited by many other creatures. Moreover, our results may inspire more optimal designs for bioinspired flying robots by relaxing the feedback control requirements of flight.


Nature ◽  
2008 ◽  
Author(s):  
Katharine Sanderson
Keyword(s):  

2008 ◽  
Vol 128 (4) ◽  
pp. 677-682 ◽  
Author(s):  
Taku Takaku ◽  
Noriyuki Iwamuro ◽  
Yoshiyuki Uchida ◽  
Ryuichi Shimada

Author(s):  
Suresha .M ◽  
. Sandeep

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.


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