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.