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5 results for “gliding flight”
How biomechanics, path-planning and sensing enable gliding flight in a natural environment
<p>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.</p>
Data from: Gliding for a free lunch: biomechanics of foraging flight in Common Swifts (Apus apus)
Although the biomechanics of animal flight have been well studied in laboratory apparatus such as wind tunnels for many years, the applicability of these data to natural flight behaviour has been examined in few instances and mostly in the context of long-distance migration. Here we use rotational stereo-videography to record the free-flight trajectories of foraging common swifts. We find that despite their exquisite manoeuvring capabilities, the swifts only rarely performed high-acceleration turns. More surprisingly, we also found that despite feeding on tiny insects likely moving with ambient flow, the birds adjust their air speed to optimize cost of transport over distance. Finally, swifts spent only 25% of their time flapping; the majority of time (71%) was spent in extended wing gliding during which the average power expended for changes in speed or elevation was 0.84 W kg-1 and not significantly different from 0. Thus, gliding swifts extracted sufficient environmental energy to pay the cost of flight during foraging.
How biomechanics, path-planning and sensing enable gliding flight in a natural environment
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Data from: Gliding for a free lunch: biomechanics of foraging flight in Common Swifts (Apus apus)
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Data from: Efficiency of lift production in flapping and gliding flight of swifts
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.