Wake analysis of drag components in gliding flight of a jackdaw (Corvus monedula) during moult

To maintain the quality of the feathers, birds regularly undergo moult. It is widely accepted that moult affects flight performance, but the specific aerodynamic consequences have received relatively little attention. Here we measured the components of aerodynamic drag from the wake behind a gliding jackdaw (Corvus monedula) at different stages of its natural wing moult. We found that span efficiency was reduced (lift induced drag increased) and the wing profile drag coefficient was increased. Both effects best correlated with the corresponding reduction in spanwise camber. The negative effects are partially mitigated by adjustments of wing posture to minimize gaps in the wing, and by weight loss to reduce wing loading. By studying the aerodynamic consequences of moult, we can refine our understanding of the emergence of various moulting strategies found among birds

Compiled dataset of aerodynamic forces and measures of posture from Wake analysis of drag components in gliding flight of a jackdaw (<i>Corvus monedula</i>) during moult

MoultStage: categorical moult stage; AirSpeed (m/s); Weight (N); Lift (N); LiftCoef (-); DragWeightSupport (-); WeightSupport (-); Dideal (N); DragInduced (N); SpanEfficiency (-); DragCoef_ind (-); DragProfile (N); DragCoef_pro (-); DragBody (N); DragCoef_bod (-); WingSpan (m); WingArea (m2); TailSpan (m); TailArea (m2); TailHeight (-); PrimSep 7-10 (-); BodyAngle (degrees); SpanRatio (-); SpanCamber (-)

Wake analysis of aerodynamic components for the glide envelope of a jackdaw (Corvus monedula)

Gliding flight is a relatively inexpensive mode of flight used by many larger bird species, where potential energy is used to cover the cost of aerodynamic drag. Birds have great flexibility in their flight configuration, allowing them to control their flight speed and glide angle. However, relatively little is known about how this flexibility affects aerodynamic drag. We measured the wake of a jackdaw (Corvus monedula) gliding in a wind tunnel, and computed the components of aerodynamic drag from the wake. We found that induced drag was mainly affected by wingspan, but also that the use of the tail has a negative influence on span efficiency. Contrary to previous work, we found no support for the separated primaries being used in controlling the induced drag. Profile drag was of similar magnitude to that reported in other studies, and our results suggest that profile drag is affected by variation in wing shape. For a folded tail, the body drag coefficient had a value of 0.2, rising to above 0.4 with the tail fully spread, which we conclude is due to tail profile drag

Multi-cored vortices support function of slotted wing tips of birds in gliding and flapping flight

Slotted wing tips of birds are commonly considered an adaptation to improve soaring performance, despite their presence in species that neither soar nor glide. We used particle image velocimetry to measure the airflow around the slotted wing tip of a jackdaw ( Corvus monedula ) as well as in its wake during unrestrained flight in a wind tunnel. The separated primary feathers produce individual wakes, confirming a multi-slotted function, in both gliding and flapping flight. The resulting multi-cored wingtip vortex represents a spreading of vorticity, which has previously been suggested as indicative of increased aerodynamic efficiency. Considering benefits of the slotted wing tips that are specific to flapping flight combined with the wide phylogenetic occurrence of this configuration, we propose the hypothesis that slotted wings evolved initially to improve performance in powered flight. </jats:p

Optimization of avian perching manoeuvres

Perching at speed is amongst the most challenging flight behaviours that birds perform1,2, and beyond the capability of current autonomous vehicles. Smaller birds may touchdown by hovering3-8, but larger birds typically swoop upward to perch1,2 – presumably because the adverse scaling of their power margin prohibits slow flapping flight9, and because swooping transfers excess kinetic to potential energy1,2,10. Perching is risky in larger birds6,11, demanding precise control of velocity and pose12-15, but it is unknown how they optimize this challenging manoeuvre. More generally, whereas cruising flight behaviours such as migration and commuting are adapted to minimize cost-of-transport or time-of-flight16, the optimization of unsteady flight manoeuvres remains largely unexplored7,17. Here we show that swooping minimizes neither the time nor energy required to perch safely in Harris’ hawks Parabuteo unicinctus, but instead minimizes the distance flown under hazardous post-stall conditions. By combining motion capture data from 1,563 flights with flight dynamics modelling, we found that the birds’ choice of where to transition from powered dive to unpowered climb minimizes the distance from the landing perch over which very high lift coefficients are required. Time and energy are therefore invested to maintain the control authority needed to execute a safe landing, rather than being minimized continuously as in technical applications of autonomous perching under nonlinear feedback control13 and deep reinforcement learning18,19. Naïve birds learn this behaviour on-the-fly, so our findings suggest an alternative reward function for reinforcement learning of autonomous perching in air vehicles.</jats:p

Optimization of avian perching manoeuvres

AbstractPerching at speed is among the most demanding flight behaviours that birds perform1,2 and is beyond the capability of most autonomous vehicles. Smaller birds may touch down by hovering3–8, but larger birds typically swoop up to perch1,2—presumably because the adverse scaling of their power margin prohibits hovering9 and because swooping upwards transfers kinetic to potential energy before collision1,2,10. Perching demands precise control of velocity and pose11–14, particularly in larger birds for which scale effects make collisions especially hazardous6,15. However, whereas cruising behaviours such as migration and commuting typically minimize the cost of transport or time of flight16, the optimization of such unsteady flight manoeuvres remains largely unexplored7,17. Here we show that the swooping trajectories of perching Harris’ hawks (Parabuteo unicinctus) minimize neither time nor energy alone, but rather minimize the distance flown after stalling. By combining motion capture data from 1,576 flights with flight dynamics modelling, we find that the birds’ choice of where to transition from powered dive to unpowered climb minimizes the distance over which high lift coefficients are required. Time and energy are therefore invested to provide the control authority needed to glide safely to the perch, rather than being minimized directly as in technical implementations of autonomous perching under nonlinear feedback control12 and deep reinforcement learning18,19. Naive birds learn this behaviour on the fly, so our findings suggest a heuristic principle that could guide reinforcement learning of autonomous perching.</jats:p

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’ hawksParabuteo unicinctusavoid 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 hawk’s 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.</jats:p

Supplementary Data supporting: "Obstacle avoidance in aerial pursuit".

Supplementary Data supporting "Obstacle avoidance in aerial pursuit" by Caroline H. Brighton, James A. Kempton, Lydia A. France, Marco KleinHeerenbrink, Sofía Miñano, and Graham K. Taylor. Published in Current Biology (2023). https://doi.org/10.1016/j.cub.2023.06.047. The zipped folder SupplementaryData.zip contains the following data files, which are readable in MATLAB (The Mathworks Inc., Natick, MA, USA) and Excel (Microsoft Inc. Redmond, WA, USA). Please see the paper for definitions and descriptions.flightsOF.matThis MATLAB workspace contains data on n=231 flights without obstacles comprising the n=128 obstacle-free training flights (flights OF001 to OF128), and the n=103 obstacle-free test flights (flights OF129 to OF231). The data are contained in the following arrays:birdPosUpsampled - [X, Y, 0] horizontal position of bird; 20 kHz spline fitbirdVelupsampled - [U, V, 0] horizontal velocity of bird; 20 kHz spline fitlurePosUpsampled - [X, Y, 0] horizontal position of lure; 20 kHz spline fitlureVelupsampled - [U, V, 0] horizontal velocity of lure; 20 kHz spline fitflightsWO.matThis MATLAB workspace contains data on n=155 flights with obstacles, including the n=154 obstacle test flights (flights WO001 to WO155 excluding flight WO119). The data are contained in the following arrays:birdPosUpsampled - [X, Y, 0] horizontal position of bird; 20 kHz spline fitbirdVelupsampled - [U, V, 0] horizontal velocity of bird; 20 kHz spline fitlurePosUpsampled - [X, Y, 0] horizontal position of lure; 20 kHz spline fitlureVelupsampled - [U, V, 0] horizontal velocity of lure; 20 kHz spline fitobstaclesPos - [f, X, Y, Z] XYZ positions of all obstacle markers identified in each frame (f)modelFitsSummary.xlsxThis Excel spreadsheet contains summaries of the fitted models referred to in the main text, with flights numbered according to the scheme above. The tab names refer to the relevant sections of the Results.</p

Visual versus visual-inertial guidance in hawks pursuing terrestrial targets

AbstractThe 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 attacker’s 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.</jats:p