Obstacle avoidance in aerial pursuit

Collision avoidance [1-4] and target pursuit [5-8] are challenging flight behaviors for any animal or autonomous vehicle, but their interaction is even more so [9-11]. For predators adapted to hunting in clutter, the demands of these two tasks may conflict, requiring effective reconciliation to avoid a hazardous collision or loss of target. Technical approaches to obstacle avoidance rely mainly on path-planning algorithms [12], but these are unlikely to be effective during closed-loop pursuit of a maneuvering target, so collision avoidance must instead be implemented reactively during prey pursuit. For example, the pursuit-avoidance behavior of predatory flies has been successfully modelled by combining feedback on target motion with feedback on obstacle looming [13]. It is unclear, however, whether this mechanism will generalize to complex environments with many looming obstacles, and it remains unknown how aerial predators reconcile the conflict between obstacle avoidance and prey pursuit in clutter. Here we use high-speed motion capture data to show how Harris hawks Parabuteo unicinctus avoid collisions by making open-loop steering corrections during closed-loop pursuit. We find that hawks combine continuous feedback on target motion with a discrete feedforward steering correction aimed at clearing an upcoming obstacle as closely as possible at maximum span. By biasing the hawks flight direction, this guidance law provides an effective means of prioritizing obstacle avoidance whilst remaining locked-on to the target. We anticipate that a similar mechanism may be used in terrestrial and aquatic pursuit. The same biased guidance law could be used for obstacle avoidance in drones designed to intercept other drones in clutter, or in drones using closed-loop guidance to navigate between fixed waypoints in urban environments.