Using collision cones to assess biological deconfliction methods
Abstract
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 pyrrhonotaandDanio 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2016
- Source ID
- 10.1098/rsif.2016.0502
Entities
People
- Daniel Grunbaum
- Diane H. Theriault
- Julia K. Parrish
- Kristi Morgansen
- Margrit Betke
- Natalie L Brace
- Nathan W. Fuller
- Tyson L. Hedrick
- William Boeing
- Zheng Wu
Organizations
- Air Force Office of Scientific Research
- Boston University
- National Science Foundation
- Office of Naval Research
- University of North Carolina
- University of Washington