Data from: Using collision cones to assess biological deconfliction methods

Biological systems consistently outperform autonomous systems governed by
engineered algorithms in their ability to reactively avoid collisions. To
better understand this discrepancy, a collision avoidance algorithm was
applied to frames of digitized video trajectory data from bats, swallows
and fish (Myotis velifer, Petrochelidon pyrrhonota and Danio
aequipinnatus). Information available from visual cues, specifically
relative position and velocity, was provided to the algorithm which used
this information to define collision cones that allowed the algorithm to
find a safe velocity requiring minimal deviation from the original
velocity. The subset of obstacles provided to the algorithm was determined
by the animal's sensing range in terms of metric and topological
distance. The algorithmic calculated velocities showed good agreement with
observed biological velocities, indicating that the algorithm was an
informative basis for comparison with the three species and could
potentially be improved for engineered applications with further study.