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Flying insects capable of navigating in highly cluttered natural environments can withstand in-flight collisions because of the combination of their low inertia 1 and the resilience of their wings 2 , exoskeletons 1 and muscles. Current insect-scale (less than ten centimetres long and weighing less than five grams) aerial robots 3 – 6 use rigid microscale actuators, which are typically fragile under external impact. Biomimetic artificial muscles 7 – 10 that are capable of large deformation offer a promising alternative for actuation because they can endure the stresses caused by such impacts. However, existing soft actuators 11 – 13 have not yet demonstrated sufficient power density to achieve lift-off, and their actuation nonlinearity and limited bandwidth create further challenges for achieving closed-loop (driven by an input control signal that is adjusted based on sensory feedback) flight control. Here we develop heavier-than-air aerial robots powered by soft artificial muscles that demonstrate open-loop (driven by a predetermined signal without feedback), passively stable (upright during flight) ascending flight as well as closed-loop, hovering flight. The robots are driven by multi-layered dielectric elastomer actuators that weigh 100 milligrams each and have a resonance frequency of 500 hertz and power density of 600 watts per kilogram. To increase the mechanical power output of the actuator and to demonstrate flight control, we present ways to overcome challenges unique to soft actuators, such as nonlinear transduction and dynamic buckling. These robots can sense and withstand collisions with surrounding obstacles and can recover from in-flight collisions by exploiting material robustness and vehicle passive stability. We also fly two micro-aerial vehicles simultaneously in a cluttered environment. They collide with the wall and each other without suffering damage. These robots rely on offboard amplifiers and an external motion-capture system to provide power to the dielectric elastomer actuators and to control their flight. Our work demonstrates how soft actuators can achieve sufficient power density and bandwidth to enable controlled flight, illustrating the potential of developing next-generation agile soft robots. Heavier-than-air insect-scale aerial robots powered by soft artificial muscles can hover and also recover from in-flight collisions, illustrating the potential for developing next-generation agile soft robots.

In addition to generating lift by leading-edge vortices (as used by most insects), mosquitoes also employ trailing-edge vortices and a lift mechanism from wing rotation, which enables them to stay airborne despite having a seemingly unlikely airframe. On the wings of a mosquito As anyone who has shared a bedroom with a mosquito will attest, mosquitos beat their wings fast enough for the irritating whine at about 800 Hz to be audible. Strangely, mosquito wings are long and thin, rather than the short and stubby wings expected to support rapid wing beats. The wing beat is also of rather low amplitude; the entire angular sweep of the wing is around 40 degrees, less than half that of the honey bee, whose 91 degree range is regarded as shallow. So how do mosquitoes fly at all? Bomphrey and colleagues show that in addition to generating lift by leading-edge vortices (as used by most insects) mosquitos employ trailing-edge vortices and a lift mechanism caused by the rotation of the wing. This adds to the expanding repertoire of mechanisms that insects use to stay airborne despite a seemingly unlikely airframe. Mosquitoes exhibit unusual wing kinematics; their long, slender wings flap at remarkably high frequencies for their size (>800 Hz)and with lower stroke amplitudes than any other insect group 1 . This shifts weight support away from the translation-dominated, aerodynamic mechanisms used by most insects 2 , as well as by helicopters and aeroplanes, towards poorly understood rotational mechanisms that occur when pitching at the end of each half-stroke. Here we report free-flight mosquito wing kinematics, solve the full Navier–Stokes equations using computational fluid dynamics with overset grids, and validate our results with in vivo flow measurements. We show that, although mosquitoes use familiar separated flow patterns, much of the aerodynamic force that supports their weight is generated in a manner unlike any previously described for a flying animal. There are three key features: leading-edge vortices (a well-known mechanism that appears to be almost ubiquitous in insect flight), trailing-edge vortices caused by a form of wake capture at stroke reversal, and rotational drag. The two new elements are largely independent of the wing velocity, instead relying on rapid changes in the pitch angle (wing rotation) at the end of each half-stroke, and they are therefore relatively immune to the shallow flapping amplitude. Moreover, these mechanisms are particularly well suited to high aspect ratio mosquito wings.

Flight speed is positively correlated with body size in animals 1 . However, miniature featherwing beetles can fly at speeds and accelerations of insects three times their size 2 . Here we show that this performance results from a reduced wing mass and a previously unknown type of wing-motion cycle. Our experiment combines three-dimensional reconstructions of morphology and kinematics in one of the smallest insects, the beetle Paratuposa placentis (body length 395 μm). The flapping bristled wings follow a pronounced figure-of-eight loop that consists of subperpendicular up and down strokes followed by claps at stroke reversals above and below the body. The elytra act as inertial brakes that prevent excessive body oscillation. Computational analyses suggest functional decomposition of the wingbeat cycle into two power half strokes, which produce a large upward force, and two down-dragging recovery half strokes. In contrast to heavier membranous wings, the motion of bristled wings of the same size requires little inertial power. Muscle mechanical power requirements thus remain positive throughout the wingbeat cycle, making elastic energy storage obsolete. These adaptations help to explain how extremely small insects have preserved good aerial performance during miniaturization, one of the factors of their evolutionary success. Three-dimensional reconstructions of morphology and flight mechanics of the beetle Paratuposa placentis reveal adaptations that enable extremely small insects to fly at speeds similar to those of much larger insects.

Many swimming and flying animals are observed to cruise in a narrow range of Strouhal numbers, where the Strouhal number St = 2fA/U is a dimensionless parameter that relates stroke frequency f, amplitude A, and forward speed U. Dolphins, sharks, bony fish, birds, bats, and insects typically cruise in the range 0.2 < St < 0.4, which coincides with the Strouhal number range for maximum efficiency as found by experiments on heaving and pitching airfoils. It has therefore been postulated that natural selection has tuned animals to use this range of Strouhal numbers because it confers high efficiency, but the reason why this is so is still unclear. Here, by using simple scaling arguments, we argue that the Strouhal number for peak efficiency is largely determined by fluid drag on the fins and wings.

Flocking is a typical example of emergent collective behavior, where interactions between individuals produce collective patterns on the large scale. Here we show how a quantitative microscopic theory for directional ordering in a flock can be derived directly from field data. We construct the minimally structured (maximum entropy) model consistent with experimental correlations in large flocks of starlings. The maximum entropy model shows that local, pairwise interactions between birds are sufficient to correctly predict the propagation of order throughout entire flocks of starlings, with no free parameters. We also find that the number of interacting neighbors is independent of flock density, confirming that interactions are ruled by topological rather than metric distance. Finally, by comparing flocks of different sizes, the model correctly accounts for the observed scale invariance of long-range correlations among the fluctuations in flight direction.

Global declines in insects have sparked wide interest among scientists, politicians, and the general public. Loss of insect diversity and abundance is expected to provoke cascading effects on food webs and to jeopardize ecosystem services. Our understanding of the extent and underlying causes of this decline is based on the abundance of single species or taxonomic groups only, rather than changes in insect biomass which is more relevant for ecological functioning. Here, we used a standardized protocol to measure total insect biomass using Malaise traps, deployed over 27 years in 63 nature protection areas in Germany (96 unique location-year combinations) to infer on the status and trend of local entomofauna. Our analysis estimates a seasonal decline of 76%, and mid-summer decline of 82% in flying insect biomass over the 27 years of study. We show that this decline is apparent regardless of habitat type, while changes in weather, land use, and habitat characteristics cannot explain this overall decline. This yet unrecognized loss of insect biomass must be taken into account in evaluating declines in abundance of species depending on insects as a food source, and ecosystem functioning in the European landscape.

To navigate, animals need to represent not only their own position and orientation, but also the location of their goal. Neural representations of an animal’s own position and orientation have been extensively studied. However, it is unknown how navigational goals are encoded in the brain. We recorded from hippocampal CA1 neurons of bats flying in complex trajectories toward a spatial goal. We discovered a subpopulation of neurons with angular tuning to the goal direction. Many of these neurons were tuned to an occluded goal, suggesting that goal-direction representation is memory-based. We also found cells that encoded the distance to the goal, often in conjunction with goal direction. The goal-direction and goal-distance signals make up a vectorial representation of spatial goals, suggesting a previously unrecognized neuronal mechanism for goal-directed navigation.

Many animals keep track of their angular heading over time while navigating through their environment. However, a neural-circuit architecture for computing heading has not been experimentally defined in any species. Here we describe a set of clockwise- and anticlockwise-shifting neurons in the Drosophila central complex whose wiring and physiology provide a means to rotate an angular heading estimate based on the fly’s angular velocity. We show that each class of shifting neurons exists in two subtypes, with spatiotemporal activity profiles that suggest different roles for each subtype at the start and end of tethered-walking turns. Shifting neurons are required for the heading system to properly track the fly’s heading in the dark, and stimulation of these neurons induces predictable shifts in the heading signal. The central features of this biological circuit are analogous to those of computational models proposed for head-direction cells in rodents and may shed light on how neural systems, in general, perform integration. A neural circuit in Drosophila reveals how the fly’s internal sense of heading rotates when it turns. Neurons that turn heads Spatial navigation has been shown to rely in part on 'head-direction neurons', which act like an internal compass in several animal species, but the neuronal circuits that generate and update the persistent activity of these cells are unknown. Now Gaby Maimon and colleagues identify two types of upstream neurons that tell the Drosophila head-direction signal when to rotate, and by how much, as the animal's head turns. The P-EN1 neurons act like a gas pedal at the start of the rotation, whereas P-EN2 neurons act like a brake at the end of it, while the magnitude of an asymmetry between left- and right-hand side P-EN neurons marks the velocity of head rotation. These findings will not only inform computational models proposed for spatial integration in other species, such as ants, bees and rodents, but could also help neuroscientists to understand how brains in general integrate transient inputs into persistent activity.

Explanations of why nocturnal insects fly erratically around fires and lamps have included theories of “lunar navigation” and “escape to the light”. However, without three-dimensional flight data to test them rigorously, the cause for this odd behaviour has remained unsolved. We employed high-resolution motion capture in the laboratory and stereo-videography in the field to reconstruct the 3D kinematics of insect flights around artificial lights. Contrary to the expectation of attraction, insects do not steer directly toward the light. Instead, insects turn their dorsum toward the light, generating flight bouts perpendicular to the source. Under natural sky light, tilting the dorsum towards the brightest visual hemisphere helps maintain proper flight attitude and control. Near artificial sources, however, this highly conserved dorsal-light-response can produce continuous steering around the light and trap an insect. Our guidance model demonstrates that this dorsal tilting is sufficient to create the seemingly erratic flight paths of insects near lights and is the most plausible model for why flying insects gather at artificial lights. It is unclear why flying insects congregate around artificial light sources. Here, the authors use high-speed videography and motion-capture, finding that insects fly perpendicular to light sources due to a disruption of the dorsal light response.

Sensorimotor control in vertebrates relies on internal models. When extending an arm to reach for an object, the brain uses predictive models of both limb dynamics and target properties. Whether invertebrates use such models remains unclear. Here we examine to what extent prey interception by dragonflies ( Plathemis lydia ), a behaviour analogous to targeted reaching, requires internal models. By simultaneously tracking the position and orientation of a dragonfly’s head and body during flight, we provide evidence that interception steering is driven by forward and inverse models of dragonfly body dynamics and by models of prey motion. Predictive rotations of the dragonfly’s head continuously track the prey’s angular position. The head–body angles established by prey tracking appear to guide systematic rotations of the dragonfly’s body to align it with the prey’s flight path. Model-driven control thus underlies the bulk of interception steering manoeuvres, while vision is used for reactions to unexpected prey movements. These findings illuminate the computational sophistication with which insects construct behaviour. This study tracks dragonfly head and body movements during high-velocity and high-precision prey-capture flights, and shows that the dragonfly uses predictive internal models and reactive control to build an interception trajectory that complies with biomechanical constraints. Dragonflies use complex internal models Primates and other vertebrates use internal models to control and predict the consequences of their movements, but invertebrates were thought to rely mostly on reflexes. In a study using motion capture to track the head and body movements of dragonflies catching flying prey, Anthony Leonardo and colleagues demonstrate a complexity of behaviour not previously seen in an insect. Approaching from below, the dragonfly's head locks onto its target while its body manoeuvres to align to the prey's flight path and reduce the distance to the prey. Rather than generating these steering movements reactively, by responding only to changes in prey motion, dragonflies using both reactive and predictive control. These predictions account for the motion of the prey and the dragonfly's own body, and are consistent with generation by internal models. The experimental accessibility of the insect nervous system, combined with the load-carrying capacity of the dragonfly, opens these general principles of motor control to detailed mechanistic dissection.