Take-off is a vital part of powered flight which likely constrains the size of birds, yet extinct pterosaurs are known to have reached far larger sizes. Three different hypothesised take-off motions (bipedal burst launching, bipedal countermotion launching, and quadrupedal launching) have been proposed as explanations for how pterosaurs became airborne and circumvented this proposed morphological limit. We have constructed a computational musculoskeletal model of a 5 m wingspan ornithocheiraean pterosaur, reconstructing thirty-four key muscles to estimate the muscle moment arms throughout the three hypothesised take-off motions. Range of motion constrained hypothetical kinematic sequences for bipedal and quadrupedal take-off motions were modelled after extant flying vertebrates. Across our simulations we did not find higher hindlimb moment arms for bipedal take-off motions or noticeably higher forelimb moment arms in the forelimb for quadrupedal take-off motions. Despite this, in all our models we found the muscles utilised in the quadrupedal take-off have the largest total launch applicable moment arms throughout the entire take-off sequences and for the take-off pose. This indicates the potential availability of higher leverage for a quadrupedal take-off than hypothesised bipedal motions in pterosaurs pending further examination of muscle forces.

Wind-hovering birds exhibit remarkable steadiness in flight, achieved through the morphing of their wings and tail. We analysed the kinematics of two nankeen kestrels (Falco cenchroides) engaged in steady wind-hovering flights in a smooth flow wind tunnel. Motion-tracking cameras were used to capture the movements of the birds as they maintained their position. The motion of the birds\' head and body, and the morphing motions of their wings and tail were tracked and analysed using correlation methods. The results revealed that wing sweep, representing the flexion/extension movement of the wing, played a significant role in wing motion. Additionally, correlations between different independent degrees of freedom (DoF), including wing and tail coupling, were observed. These kinematic couplings indicate balancing of forces and moments necessary for steady wind hovering. Variation in flight behaviour between the two birds highlighted the redundancy of DoF and the versatility of wing morphing in achieving control. This study provides insights into fixed-wing craft flight control from the avian world and may inspire novel flight control strategies for future fixed-wing aircraft.

Odours used by insects for foraging and mating are carried by the air. Insects induce airflows around them by flapping their wings, and the distribution of these airflows may strongly influence odour source localisation. The flightless silkworm moth, Bombyx mori, has been a prominent insect model for olfactory research. However, although there have been numerous studies on antenna morphology and its fluid dynamics, neurophysiology, and localisation algorithms, the airflow manipulation of the B. mori by fanning has not been thoroughly investigated. In this study, we performed computational fluid dynamics (CFD) analyses of flapping B. mori to analyse this mechanism in depth. A three-dimensional simulation using reconstructed wing kinematics was used to investigate the effects of B. mori fanning on locomotion and pheromone capture. The fanning of the B. mori was found to generate an aerodynamic force on the scale of its weight through an aerodynamic mechanism similar to that of flying insects. Our simulations further indicate that the B. mori guides particles from its anterior direction within the ~ 60° horizontally by wing fanning. Hence, if it detects pheromones during fanning, the pheromone can be concluded to originate from the direction the head is pointing. The anisotropy in the sampling volume enables the B. mori to orient to the pheromone plume direction. These results provide new insights into insect behaviour and offer design guidelines for robots for odour source localisation.

Nature abounds with examples of ultra-sensitive perception and agile body transformation for highly efficient predation as well as extraordinary adaptation to complex environments. Flytraps, as a representative example, could effectively detect the most minute physical stimulation of insects and respond instantly, inspiring numerous robotic designs and applications. However, current robotic flytraps face challenges in reproducing the ultra-sensitive insect-touch perception. In addition, fast and fully-covered capture of live insects with robotic flytraps remains elusive. Here we report a novel design of a robotic flytrap with an ultra-sensitive \'trichome\' and bistable fast-response \'lobes\'. Our results show that the \'trichome\' of the proposed robotic flytrap could detect and respond to both the external stimulation of 0.45 mN and a tiny touch of a flying bee with a weight of 0.12 g. Besides, once the \'trichome\' is triggered, the bistable \'lobes\' could instantly close themselves in 0.2 s to form a fully-covered cage to trap the bees, and reopen to set them free after the tests. We introduce the design, modeling, optimization, and verification of the robotic flytrap, and envision broader applications of this technology in ultra-sensitive perception, fast-response grasping, and biomedical engineering studies.

Animals must tune their physical performance to changing environmental conditions, and the breadth of environmental tolerance may contribute to delineating the geographic range of a species. A common environmental challenge that flying animals face is the reduction of air density at high elevation and the reduction in the effectiveness of lift production that accompanies it. As a species, turkey vultures (Cathartes aura) inhabit a >3000 m elevation range, and fly considerably higher, necessitating that they accommodate for a 27% change in air density (0.890 to 1.227 kg m-3) through behavior, physiology or biomechanics. We predicted that birds flying at high elevation would maintain aerodynamic lift performance behaviorally via higher flight speeds, rather than increases in power output or local phenotypic adaptation. We used three-dimensional videography to track turkey vultures flying at three elevations, and data supported the hypothesized negative relationship between median airspeed and air density. Additionally, neither the ratio of horizontal speed to sinking speed nor flapping behavior varied with air density.

Flight plays a crucial role in the fitness of insect pollinators, such as bumblebees. Despite their relatively large body size compared with their wings, bumblebees can fly under difficult ambient conditions, such as cooler temperatures. While their body size is often positively linked to their foraging range and flight ability, the influence of age remains less explored. Here, we studied the flight performance (distance, duration and speed) of ageing bumblebee workers using tethered flight mills. Additionally, we measured their intertegular distance and dry mass as proxies for their body size. We found that the flight distance and duration were predominantly influenced by age, challenging assumptions that age does not play a key role in foraging and task allocation. From the age of 7 to 14 days, flight distance and duration increased sixfold and fivefold, respectively. Conversely, the body size primarily impacted the maximum and average flight speed of workers. Our findings indicate that age substantially influences the flight distance and duration in bumblebee workers, affecting foraging performance and potentially altering task allocation strategies. This underscores the importance of considering individual age and physiological changes alongside body size/mass in experiments involving bumblebee workers.

Bats are the only mammals capable of powered flight and have correspondingly specialized body plans, particularly in their limb morphology. The origin of bat flight is still not fully understood due to an uninformative fossil record but, from the perspective of a functional transition, it is widely hypothesized that bats evolved from gliding ancestors. Here, we test predictions of the gliding-to-flying hypothesis of the origin of bat flight by using phylogenetic comparative methods to model the evolution of forelimb and hindlimb traits on a dataset spanning four extinct bats and 231 extant mammals with diverse locomotor modes. Our results reveal that gliders exhibit adaptive trait optima (1) toward relatively elongate forelimbs that are intermediate between those of bats and non-gliding arborealists, and (2) toward relatively narrower but not longer hindlimbs that are intermediate between those of non-gliders and bats. We propose an adaptive landscape based on limb length and width optimal trends derived from our modeling analyses. Our results support a hypothetical evolutionary pathway wherein glider-like postcranial morphology precedes a bat-like morphology adapted to powered-flight, setting a foundation for future developmental, biomechanical, and evolutionary research to test this idea.

Pigeons\' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levensonet al2015PLoS One10e0141357). Could it be that knowledge transferred from their bird\'s-eye views of the earth\'s surface gleaned during flight contributes to this ability? Employing a simulation-based verification strategy, we recapitulate this biological phenomenon with a machine-learning analog. We model pigeons\' visual experience during flight with the self-supervised pre-training of a deep neural network on BirdsEyeViewNet; our large-scale aerial imagery dataset. As an analog of the differential food reinforcement performed in Levensonet al\'s study 2015PLoS One10e0141357), we apply transfer learning from this pre-trained model to the same Hematoxylin and Eosin (H&E) histopathology and radiology images and tasks that the pigeons were trained and tested on. The study demonstrates that pre-training neural networks with bird\'s-eye view data results in close agreement with pigeons\' performance. These results support transfer learning as a reasonable computational model of pigeon representation learning. This is further validated with six large-scale downstream classification tasks using H&E stained whole slide image datasets representing diverse cancer types.

This paper introduces a method for measuring wing motion, deformation, and inertial forces in bio-inspired aircraft research using a camera motion capture system. The method involves placing markers on the wing surface and fitting rigid planes to determine the wing\'s spatial axis. This allows for describing the wing\'s rigid motion and obtaining deformation characteristics, such as deflection, twist angle, and gap distance of the forewing and hindwing. An image-based method is proposed for determining wing mass distribution, mass blocks, and mass points for inertial force measurement. The study addresses wing motion, deformation, and inertial force measurement in a real butterfly-like flapping wing vehicle and demonstrates the effectiveness of the approach. The results reveal that inertial forces play a negligible role in the generation of lift peaks and contribute minimal lift during the entire flapping cycle. Furthermore, a transitional phase between downstroke and upstroke is found in flexible wing motion, which has high lift production. This measurement approach offers a rapid and effective solution to experimental challenges in bio-inspired aircraft design and optimization.

Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), STMR achieves collective group motion. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.