10.6084/m9.figshare.3823686.v1
Natalie L. Brace
Natalie L.
Brace
Tyson L. Hedrick
Tyson
L. Hedrick
Diane H. Theriault
Diane H.
Theriault
Nathan W. Fuller
Nathan W.
Fuller
Zheng Wu
Zheng
Wu
Margrit Betke
Margrit
Betke
Julia K. Parrish
Julia
K. Parrish
Daniel Grünbaum
Daniel
Grünbaum
Kristi A. Morgansen
Kristi A.
Morgansen
Bat Data from Using collision cones to assess biological deconfliction methods
The Royal Society
2016
collision avoidance algorithm
animal behavior
velocity obstacles
collision cones
nonlinear control
multi-species comparison
2016-09-13 07:41:38
Dataset
https://rs.figshare.com/articles/dataset/Bat_Data_from_Using_collision_cones_to_assess_biological_deconfliction_methods/3823686
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 (<i>Myotis velifer</i>, <i>Petrochelidon pyrrhonota</i>, and <i>Danio aequipinnatus</i>). 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.