Biology subjects

Brighton, C. H.

Publications and source records attributed to Brighton, C. H..

4 recordsLinked to original sources

bioRxiv2023

Hawks pursuing targets through clutter avoid obstacles by applying open loop steering corrections during closed loop 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.

zoology↗

bioRxiv2022

Visual versus visual-inertial guidance in hawks pursuing terrestrial targets

The flight behaviour of predatory birds is well modelled by a guidance law called proportional navigation, which commands steering in proportion to the angular rate of the line-of-sight from predator to prey. The line-of-sight rate is defined with respect to an inertial frame of reference, so proportional navigation is necessarily implemented using visual-inertial sensor fusion. In Harris hawks, pursuit of terrestrial targets may be even better modelled by assuming that visual-inertial information on the line-of-sight rate is combined with visual information on the deviation angle between the attackers velocity and the line-of-sight. Here we ask whether a new variant of this mixed guidance law can model Harris hawk pursuit behaviour successfully using visual information alone. We use high-speed motion capture to record n=228 attack fights from N=4 Harris hawks, and confirm that proportional navigation and mixed guidance using visual-inertial information both model the trajectory data well. Moreover, the mixed guidance law still models the data closely if visual-inertial information on the line-of-sight rate is replaced with purely visual information on the apparent motion of the target relative to the background. Although the original form of the mixed guidance law provides the closest fit, all three models provide an adequate phenomenological model of the behavioural data, whilst each making different predictions on the physiological pathways involved.

animal behavior and cognition↗

bioRxiv2022

Steering clear of the confusion effect: aerial predators target fixed points in dense prey aggregations

Collective behaviours are widely assumed to confuse predators, but empirical support for a confusion effect is often lacking, and its importance must depend on the predators targeting mechanism. Here we show that raptors steer attacks on swarming bats by turning towards a fixed point in the swarm, rather than by using closed-loop pursuit of an individual. Any prey that the predator is on a collision course with will appear on a constant bearing, so target selection emerges naturally from the geometry of a collision. Our results show how predators can simplify the demands on their sensory system by decoupling steering from targeting when grabbing prey from a dense swarm. We anticipate that the same tactic will be used against flocks and schools across a wide range of taxa, in which case a confusion effect is paradoxically more likely to occur in attacks on sparse groups, for which steering and targeting cannot be decoupled.

animal behavior and cognition↗

bioRxiv2020

Attack behaviour in naïve Gyrfalcons is modelled by the same guidance law as in Peregrines, but at a lower guidance gain

The aerial hunting behaviours of birds are strongly influenced by their flight morphology and ecology, but little is known of how this variation relates to the behavioural algorithms guiding flight. Here we use onboard GPS loggers to record the attack trajectories of captive-bred Gyrfalcons (Falco rusticolus) during their maiden flights against robotic aerial targets, which we compare to existing flight data from Peregrines (Falco peregrinus) The attack trajectories of both species are modelled most economically by a proportional navigation guidance law, which commands turning in proportion to the angular rate of the line-of-sight to target, at a guidance gain N. However, Gyrfalcons operate at significantly lower values of N than Peregrines, producing slower turning and a longer path to intercept. Gyrfalcons are less agile and less manoeuvrable than Peregrines, but this physical constraint is insufficient to explain their lower guidance gain. On the other hand, lower values of N promote the tail-chasing behaviour that is typical of wild Gyrfalcons, and which apparently serves to tire their prey in a prolonged high-speed pursuit. Moreover, during close pursuit of fast evasive prey such as Ptarmigan (Lagopus spp.), proportional navigation will be less prone to being thrown off by erratic target manoeuvres if N is low. The fact that low-gain proportional navigation successfully models the maiden attack flights of Gyrfalcons suggests that this behavioural algorithm is embedded in a hardwired guidance loop, which we hypothesise is ancestral to the clade containing Gyrfalcons and Peregrines. SUMMARY STATEMENTNaive Gyrfalcons attacking aerial targets are modelled by the same proportional navigation guidance law as Peregrines, but with a lower navigation constant that promotes tail-chasing rather than efficient interception.

zoology↗