Using Collision Cones to Asses Biological Deconfliction Methods
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For autonomous vehicles to navigate the world as efficiently and effectively as biological species, improvements are needed in terms of control strategies and estimation algorithms. Reactive collision avoidance is one specific area where biological systems outperform engineered algorithms. To better understand the discrepancy between engineered and biological systems, a collision avoidance algorithm was applied to frames of trajectory data from three biological species (Myotis velifer, Hirundo rustica, and Danio aequipinnatus). The algorithm uses information that can be sensed through visual cues (relative position and velocity) to define collision cones which are used to determine if agents are on a collision course and if so, to find a safe velocity that requires minimal deviation from the original velocity for each individual agent. Two- and three-dimensional versions of the algorithm with constant speed and maximum speed velocity requirements were considered. The obstacles provided to the algorithm were determined by the sensing range in terms of either metric or topological distance. The calculated velocities showed good correlation with observed velocities over the range of sensing parameters, indicating that the algorithm is a good basis for comparison and could potentially be improved with further study.