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 flight morphology and ecology, but little is known of how this relates to the behavioural algorithms guiding flight. Here we use 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 well modelled by a proportional navigation (PN) guidance law, which commands turning in proportion to the angular rate of the line-of-sight to target, at a guidance gain. However, naïve Gyrfalcons operate at significantly lower values of N than Peregrines, producing slower turning and a longer path to intercept. Gyrfalcons are less manoeuvrable than Peregrines, but physical constraint is insufficient to explain the lower values of N we found, which may reflect either the inexperience of the individual birds or ecological adaptation at the species level. For example, low 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. Likewise, during close pursuit of typical fast evasive prey, PN will be less prone to being thrown off by erratic target manoeuvres at low guidance gain. The fact that low-gain PN successfully models the maiden attack flights of Gyrfalcons suggests that this behavioural algorithm is embedded in a guidance pathway ancestral to the clade containing Gyrfalcons and Peregrines, though perhaps with much deeper evolutionary origins.

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

ABSTRACTThe 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 STATEMENTNaïve 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.

Simulation of 5DOF Model for Arial Grasping

This study presents dynamic modeling and simulation of an air vehicle consisting of a body, gripper and a claw. This model is inspired from birds’ aerial hunting, while considering the extra degree of freedom associated with the claw. For a manipulator like a gripper, additional degree of freedom creates more flexibility for grasping. The main contribution of this paper focuses on the development of a model that is suitable for trajectory optimization in grasping phase. Mathematical representation of the system is developed based on the Newton-Euler approach in MATLAB-Simulink environment, considering the motion in vertical plane. The dynamic behavior of the system is evaluated by simulation in variety situations and sensitivity analysis is carried out to determine and characterize the parameters having the most and least effects on grasping. It is shown that the initial position of the gripper and the claw as well as the additional mass that is added to the system in grasping phase make considerable changes in the dynamics that necessitates the use of the control system. In addition, smooth trajectories and controls are obtained by adding friction to the system in order to avoid dynamic divergence.

Avian-Inspired Grasping for Quadrotor Micro UAVs

Micro Unmanned Aerial Vehicles (MAVs) have been used in a wide range of applications [1, 2, 3]. However, there are few papers addressing high-speed grasping and transportation of pay-loads using MAVs. Drawing inspiration from aerial hunting by birds of prey, we design and equip a quadrotor MAV with an actuated appendage enabling grasping and object retrieval at high speeds. We develop a nonlinear dynamic model of the system, demonstrate that the system is differentially flat, plan dynamic trajectories using the flatness property, and present experimental results with pick-up velocities at 2 m/s (6 body lengths / second) and 3 m/s (9 body lengths / second). Finally, the experimental results are compared with observations derived from video footage of a bald eagle swooping down and snatching a fish out of water.