Recursive Bayesian Estimation of Bat Flapping Flight Using Kinematic Trees

Bender, Matt; McClelland, Hunter G.; Kurdila, Andrew J.; Mueller, Rolf

doi:10.2514/6.2016-0945

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…The tunnel was illuminated using six lights mounted in the upper corners to mitigate motion blur while filming at high frame rates. The motion tracking system used in the present work is described in more detail in Bender et al ., 2015 [44] and Bender et al ., 2016 [45].…”

Section: Methodsmentioning

confidence: 99%

A computational investigation of lift generation and power expenditure of Pratt’s roundleaf bat (Hipposideros pratti) in forward flight

et al. 2018

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The aerodynamic mechanisms of bat flight have been studied using a numerical approach. Kinematic data acquired using a high resolution motion capture system was employed to simulate the unsteady air flow around a bat’s wings. A flapping bat wing contains many degrees of freedom, which make 3D motion tracking challenging. In order to overcome this challenge, an optical motion capture system of 21 cameras was used to reduce wing self-occlusion. Over the course of a meter-long flight, 108 discrete marker points on the bat’s wings (Pratt’s roundleaf bat, Hipposideros pratti) were tracked. The time evolution of the surface of each wing was computationally reconstructed in 3D space. The resulting kinematic model was interfaced with an unsteady incompressible flow solver using the immersed boundary method (IBM) and large eddy simulation (LES). Verification and validation of the flow simulation were conducted to establish accuracy. The aerodynamic forces calculated from the simulation compared well to the forces theoretically needed to sustain the observed flight trajectory. The transient flow field generated by the simulation allowed for the direct calculation of lift, drag, and power output of the bat during flight. The mean lift coefficient was found to be 3.21, and the flap cycle averaged aerodynamic power output was 1.05 W. Throughout the flap cycle, the planform area of the wings varied up to 46% between the largest and smallest values. During the upstroke, wing rotation was found to mitigate negative lift thereby improving overall flight efficiency. The high resolution motion capture and flow simulation framework presented here has the potential to facilitate the understanding of complex bat flight aerodynamics for both straight and maneuvering flight modes.

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Section: Methodsmentioning

confidence: 99%

A computational investigation of lift generation and power expenditure of Pratt’s roundleaf bat (Hipposideros pratti) in forward flight

et al. 2018

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“…To learn joint geometry, only the markers on the joints of the bat skeleton are used. Extensive discussions about distortion removal and reconstruction of the motion is reported in [2][3][4].…”

Section: Experimental Methodsmentioning

confidence: 99%

“…In [3,4] the authors use the well-known Devanit-Hartenberg (DH) convention to assign rotational degrees of freedom to each of the joints on the wing. This process defines an open kinematic chain which approximates the bat skeleton.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…The resulting point trajectories are shown in figure 1. Furthermore, we abandon the kinematic models of [3,4] and assign body fixed frames by using relative orientations of points. This relaxes the constraints between coordinate frames which are imposed by the Denavit-Hartenberg convention.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…. × SO(n l ) where l is the number of links in the kinematic chain, SE (3) accounts for the body translation and rotation, and SO(n i ) is the special orthogonal group for a joint with n i mutually orthogonal rotations. The motion of the system is typically restricted to a submanifold of this configuration space by enforcing box constraints on joint motion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning bioinspired joint geometry from motion capture data of bat flight

Bender¹,

Guo²,

Powell³

et al. 2019

Bioinspir. Biomim.

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Bioinspired robotic systems are often designed by assuming that the kinematics of the biological system of interest are well approximated by an open kinematic chain subject to box constraints. These box-constrains are typically generated by designer interpretation of biomotion and anatomical studies or pragmatic fabrication constraints. In contrast to this standard design paradigm, this paper presents a methodology for learning joint geometry which restricts the range of motion of generic ball joints to only the reachable set observed during biomotion experiments. This reachable set is identified by constructing an analytical-empirical potential energy function over the experimental observations. The energy function effectively 'pushes' configurations close to the set of observations. This energy function is then thresholded to identify the zero-potential (ZP) configuration set, and joint geometry is constructed using the resulting contour. We construct an entire bat wing using this method, and demonstrate through motion capture experiments that the learned geometry successfully restricts motions to the ZP set.

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