Deetjen Marc E, Biewener Andrew A, Lentink David
Department of Mechanical Engineering, Stanford University, Palo Alto, CA 94305, USA
Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, MA 02138, USA.
J Exp Biol. 2017 Jun 1;220(Pt 11):1956-1961. doi: 10.1242/jeb.149708. Epub 2017 Mar 27.
Birds fly effectively and maneuver nimbly by dynamically changing the shape of their wings during each wingbeat. These shape changes have yet to be quantified automatically at high temporal and spatial resolution. Therefore, we developed a custom 3D surface reconstruction method, which uses a high-speed camera to identify spatially encoded binary striped patterns that are projected on a flying bird. This non-invasive structured-light method allows automated 3D reconstruction of each stand-alone frame and can be extended to multiple views. We demonstrate this new technique by automatically reconstructing the dorsal surface of a parrotlet wing at 3200 frames s during flapping flight. From this shape we analyze key parameters such as wing twist and angle of attack distribution. While our binary 'single-shot' algorithm is demonstrated by quantifying dynamic shape changes of a flying bird, it is generally applicable to moving animals, plants and deforming objects.
鸟类通过在每次振翅过程中动态改变翅膀形状来有效地飞行并灵活地操控。这些形状变化尚未在高时间和空间分辨率下自动进行量化。因此，我们开发了一种定制的三维表面重建方法，该方法使用高速摄像机来识别投射在飞行鸟类身上的空间编码二进制条纹图案。这种非侵入性结构光方法允许对每个独立帧进行自动三维重建，并且可以扩展到多个视图。我们通过在拍动飞行过程中以每秒3200帧的速度自动重建一只小鹦鹉翅膀的背面来展示这项新技术。从这个形状中，我们分析了诸如翅膀扭转和攻角分布等关键参数。虽然我们的二进制“单次拍摄”算法通过量化飞行鸟类的动态形状变化得到了证明，但它一般适用于移动的动物、植物和变形物体。
