How biomechanics, path-planning and sensing enable gliding flight in a natural environment
Hedrick, Tyson; Khandelwal, Pranav (2020), How biomechanics, path-planning and sensing enable gliding flight in a natural environment , v4, Dryad, Dataset, https://doi.org/10.5061/dryad.70rxwdbt6
Gliding animals traverse cluttered aerial environments when performing ecologically relevant behaviours. However, it is unknown how gliders execute collision-free flight over varying distances to reach their intended target. We quantified complete glide trajectories amid obstacles in a naturally behaving population of gliding lizards inhabiting a rainforest reserve. In this cluttered habitat, the lizards used glide paths with fewer obstacles than alternatives of similar distance. Their takeoff direction oriented them away from obstacles in their path and they subsequently made mid-air turns with accelerations of up to 0.5 g to reorient towards the target tree. These manoeuvres agreed well with a vision-based steering model which maximized their bearing angle with the obstacle while minimizing it with the target tree. Nonetheless, negotiating obstacles reduced mid-glide shallowing rates, implying greater loss of altitude. Finally, the lizards initiated a pitch-up landing manoeuvre consistent with a visual trigger model, suggesting that the landing decision was based on the optical size and speed of the target. They subsequently followed a controlled-collision approach towards the target, ending with variable impact speeds. Overall, the visually guided path-planning strategy that enabled collision-free gliding required continuous changes in the gliding kinematics such that the lizards never attained theoretically ideal steady state glide dynamics.
This dataset represents the 3D trajectories of gliding lizards (Draco) flying in their natural habitat, collected at the Agumbe Rainforest Research Station, Karnataka, India. Data were collected by high-speed multi-camera videography and processed as described in the associated manuscript.
National Science Foundation, Award: 1253276
- This dataset is supplement to https://doi.org/10.1098/rspb.2019.2888
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