The effect of optic flow cues on honeybee flight control in wind

Bumblebees compensate for the adverse effects of sidewind during visually guided landings

Flying animals often encounter winds during visually guided landings. However, how winds affect their flight control strategy during landing is unknown. Here, we investigated how sidewind affects the landing performance and sensorimotor control of foraging bumblebees (Bombus terrestris). We trained bumblebees to forage in a wind tunnel, and used high-speed stereoscopic videography to record 19,421 landing maneuvers in six sidewind speeds (0 to 3.4 m s-1), which correspond to winds encountered in nature. Bumblebees landed less often in higher windspeeds, but the landing durations from free flight were not increased by wind. By testing how bumblebees adjusted their landing control to compensate for adverse effects of sidewind on landing, we showed that the landing strategy in sidewind resembled that in still air, but with important adaptations. Bumblebees landing in a sidewind tended to drift downwind, which they controlled for by performing more hover maneuvers. Surprisingly, the increased hover prevalence did not increase the duration of free-flight landing maneuvers, as these bumblebees flew faster towards the landing platform outside the hover phases. Hence, by alternating these two flight modes along their flight path, free-flying bumblebees negated the adverse effects of high windspeeds on landing duration. Using control theory, we hypothesize that bumblebees achieve this by integrating a combination of direct aerodynamic feedback and a wind-mediated mechanosensory feedback control, with their vision-based sensorimotor control loop. The revealed landing strategy may be commonly used by insects landing in windy conditions, and may inspire the development of landing control strategies onboard autonomously flying robots.

Visual guidance of honeybees approaching a vertical landing surface

Landing is a critical phase for flying animals, whereby many rely on visual cues to perform controlled touchdown. Foraging honeybees rely on regular landings on flowers to collect food crucial for colony survival and reproduction. Here, we explored how honeybees utilize optical expansion cues to regulate approach flight speed when landing on vertical surfaces. Three sensory-motor control models have been proposed for landings of natural flyers. Landing honeybees maintain a constant optical expansion rate set-point, resulting in a gradual decrease in approach velocity and gentile touchdown. Bumblebees exhibit a similar strategy, but they regularly switch to a new constant optical expansion rate set-point. In contrast, landing birds fly at a constant time to contact to achieve faster landings. Here, we re-examined the landing strategy of honeybees by fitting the three models to individual approach flights of honeybees landing on platforms with varying optical expansion cues. Surprisingly, the landing model identified in bumblebees proved to be the most suitable for these honeybees. This reveals that honeybees adjust their optical expansion rate in a stepwise manner. Bees flying at low optical expansion rates tend to increase their set-point stepwise, while those flying at high optical expansion rates tend to decrease it stepwise. This modular landing control system enables honeybees to land rapidly and reliably under a wide range of initial flight conditions and visual landing platform patterns. The remarkable similarity between the landing strategies of honeybees and bumblebees suggests that this may also be prevalent among other flying insects. Furthermore, these findings hold promising potential for bioinspired guidance systems in flying robots.

Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects

Odor plume tracking is important for many organisms, and flying insects have served as popular model systems for studying this behavior both in field and laboratory settings. The shape and statistics of the airborne odor plumes that insects follow are largely governed by the wind that advects them. Prior atmospheric studies have investigated aspects of microscale wind patterns with an emphasis on characterizing pollution dispersion, enhancing weather prediction models, and for assessing wind energy potential. Here, we aim to characterize microscale wind dynamics through the lens of short-term ecological functions by focusing on spatial and temporal scales most relevant to insects actively searching for odor sources. We collected and compared near-surface wind data across three distinct environments (sage steppe, forest, and urban) in Northern Nevada. Our findings show that near-surface wind direction variability decreases with increasing wind speeds and increases in environments with greater surface complexity. Across environments, there is a strong correlation between the variability in the wind speed (i.e., turbulence intensity) and wind direction (i.e., standard deviation in wind direction). In some environments, the standard deviation in the wind direction varied as much as 15°-75° on time scales of 1-10 min. We draw insight between our findings and previous plume tracking experiments to provide a general intuition for future field research and guidance for wind tunnel design. Our analysis suggests a hypothesis that there may be an ideal range of wind speeds and environment complexity in which insects will be most successful when tracking odor plumes over long distances.

Honeybees Use Multiple Invariants to Control Their Altitude

How do bees perceive altitude changes so as to produce safe displacements within their environment? It has been proved that humans use invariants, but this concept remains little-known within the entomology community. The use of a single invariant, the optical speed rate of change, has been extensively demonstrated in bees in a ground-following task. Recently, it has been demonstrated that another invariant, the splay angle rate of change, could also be used by bees to adjust their altitude. This study aims to understand how bees use these invariants when they are available simultaneously. This issue has been addressed using an experimental setup providing discordant information to bees. We have shown that when the two invariants were available, bees performed ground-following tasks relying primarily on optical speed rate of change. Conversely, when optical speed rate of change was less easily accessible, splay angle rate of change was prioritized, unless the bees perceive danger. Taken together, these results illustrate how the joint use of several invariants allows bees to produce adaptive behaviors.

Through Hawks’ Eyes: Synthetically Reconstructing the Visual Field of a Bird in Flight

Birds of prey rely on vision to execute flight manoeuvres that are key to their survival, such as intercepting fast-moving targets or navigating through clutter. A better understanding of the role played by vision during these manoeuvres is not only relevant within the field of animal behaviour, but could also have applications for autonomous drones. In this paper, we present a novel method that uses computer vision tools to analyse the role of active vision in bird flight, and demonstrate its use to answer behavioural questions. Combining motion capture data from Harris’ hawks with a hybrid 3D model of the environment, we render RGB images, semantic maps, depth information and optic flow outputs that characterise the visual experience of the bird in flight. In contrast with previous approaches, our method allows us to consider different camera models and alternative gaze strategies for the purposes of hypothesis testing, allows us to consider visual input over the complete visual field of the bird, and is not limited by the technical specifications and performance of a head-mounted camera light enough to attach to a bird’s head in flight. We present pilot data from three sample flights: a pursuit flight, in which a hawk intercepts a moving target, and two obstacle avoidance flights. With this approach, we provide a reproducible method that facilitates the collection of large volumes of data across many individuals, opening up new avenues for data-driven models of animal behaviour.

Optic flow based spatial vision in insects

The optic flow, i.e., the displacement of retinal images of objects in the environment induced by self-motion, is an important source of spatial information, especially for fast-flying insects. Spatial information over a wide range of distances, from the animal's immediate surroundings over several hundred metres to kilometres, is necessary for mediating behaviours, such as landing manoeuvres, collision avoidance in spatially complex environments, learning environmental object constellations and path integration in spatial navigation. To facilitate the processing of spatial information, the complexity of the optic flow is often reduced by active vision strategies. These result in translations and rotations being largely separated by a saccadic flight and gaze mode. Only the translational components of the optic flow contain spatial information. In the first step of optic flow processing, an array of local motion detectors provides a retinotopic spatial proximity map of the environment. This local motion information is then processed in parallel neural pathways in a task-specific manner and used to control the different components of spatial behaviour. A particular challenge here is that the distance information extracted from the optic flow does not represent the distances unambiguously, but these are scaled by the animal's speed of locomotion. Possible ways of coping with this ambiguity are discussed.

An experimental setup for decoupling optical invariants in honeybees' altitude control

Bees outperform pilots in navigational tasks, despite having 100,000 times fewer neurons. It is commonly accepted in the literature that optic flow is a key parameter used by flying insects to control their altitude. The ambition of the present work was to design an innovative experimental setup that would make it possible to determine whether bees could rely simultaneously on several optical invariants, as pilots do. We designed a flight tunnel to enable manipulation of an optical invariant, the Splay Angle Rate of Change (SARC) and the restriction of the Optical Speed Rate of Change (OSRC) in the optic flow. It allows us to determine if bees use the SARC to control their altitude and to identify the integration process combining these two optical invariants. Access to the OSRC can be restricted by using different textures. The SARC can be biased thanks to motorized rods. This device allows to record bees' trajectories in different visual configurations, including impoverished conditions and conditions containing contradictory information. The comparative analysis of the recorded trajectories provides first time evidence of SARC use in a ground-following task by a non-human animal. This new tunnel allows a precise experimental control of the visual environment in ecological experimental conditions. Therefore, it could pave the way for a new type of ecologically based studies examining the simultaneous use of several information sources for navigation by flying insects.

Wind and route choice affect performance of bees flying above versus within a cluttered obstacle field

Bees flying through natural landscapes frequently encounter physical challenges, such as wind and cluttered vegetation, but the influence of these factors on flight performance remains unknown. We analyzed 548 videos of wild-caught honeybees (Apis mellifera) flying through an enclosure containing a field of vertical obstacles that bees could choose to fly within (through open corridors, without maneuvering) or above. We varied obstacle field height and wind condition (still, headwinds or tailwinds), and examined how these factors affected bees’ flight altitude, ground speed, and side-to-side casting motions (lateral excursions). When obstacle fields were short, bees flew at altitudes near the midpoint between the tunnel floor and ceiling. When obstacle fields approached or exceeded this midpoint, bees tended to increase their altitude, but they did not always avoid flying through obstacles, despite having the freedom to do so. Bees that flew above the obstacles exhibited 40% faster ground speeds and 36% larger lateral excursions than bees that flew within the obstacle fields. Wind did not affect flight altitude, but bees flew 12–19% faster in tailwinds, and their lateral excursions were 19% larger when flying in headwinds or tailwinds, as compared to still air. Our results show that bees flying through complex environments display flexibility in their route choices (i.e., flying above obstacles in some trials and through them in others), which affects their overall flight performance. Similar choices in natural landscapes could have broad implications for foraging efficiency, pollination, and mortality in wild bees.

Bumblebees display characteristics of active vision during robust obstacle avoidance flight

Insects are remarkable flyers and capable of navigating through highly cluttered environments. We tracked the head and thorax of bumblebees freely flying in a tunnel containing vertically oriented obstacles to uncover the sensorimotor strategies used for obstacle detection and collision avoidance. Bumblebees presented all the characteristics of active vision during flight by stabilizing their head relative to the external environment and maintained close alignment between their gaze and flightpath. Head stabilization increased motion contrast of nearby features against the background to enable obstacle detection. As bees approached obstacles, they appeared to modulate avoidance responses based on the relative retinal expansion velocity (RREV) of obstacles and their maximum evasion acceleration was linearly related to RREVmax. Finally, bees prevented collisions through rapid roll manoeuvres implemented by their thorax. Overall, the combination of visuo-motor strategies of bumblebees highlights elegant solutions developed by insects for visually guided flight through cluttered environments.

Spatial tuning of translational optic flow responses in hawkmoths of varying body size

To safely navigate their environment, flying insects rely on visual cues, such as optic flow. Which cues insects can extract from their environment depends closely on the spatial and temporal response properties of their visual system. These in turn can vary between individuals that differ in body size. How optic flow-based flight control depends on the spatial structure of visual cues, and how this relationship scales with body size, has previously been investigated in insects with apposition compound eyes. Here, we characterised the visual flight control response limits and their relationship to body size in an insect with superposition compound eyes: the hummingbird hawkmoth Macroglossum stellatarum. We used the hawkmoths’ centring response in a flight tunnel as a readout for their reception of translational optic flow stimuli of different spatial frequencies. We show that their responses cut off at different spatial frequencies when translational optic flow was presented on either one, or both tunnel walls. Combined with differences in flight speed, this suggests that their flight control was primarily limited by their temporal rather than spatial resolution. We also observed strong individual differences in flight performance, but no correlation between the spatial response cutoffs and body or eye size.

Oscillations make a self-scaled model for honeybees' visual odometer reliable regardless of flight trajectory

Honeybees foraging and recruiting nest-mates by performing the waggle dance need to be able to gauge the flight distance to the food source regardless of the wind and terrain conditions. Previous authors have hypothesized that the foragers’ visual odometer mathematically integrates the angular velocity of the ground image sweeping backward across their ventral viewfield, known as translational optic flow. The question arises as to how mathematical integration of optic flow (usually expressed in radians/s) can reliably encode distances, regardless of the height and speed of flight. The vertical self-oscillatory movements observed in honeybees trigger expansions and contractions of the optic flow vector field, yielding an additional visual cue called optic flow divergence. We have developed a self-scaled model for the visual odometer in which the translational optic flow is scaled by the visually estimated current clearance from the ground. In simulation, this model, which we have called SOFIa, was found to be reliable in a large range of flight trajectories, terrains and wind conditions. It reduced the statistical dispersion of the estimated flight distances approximately 10-fold in comparison with the mathematically integrated raw optic flow model. The SOFIa model can be directly implemented in robotic applications based on minimalistic visual equipment.

Bumblebees land rapidly and robustly using a sophisticated modular flight control strategy

When approaching a landing surface, many flying animals use visual feedback to control their landing. Here, we studied how foraging bumblebees (Bombus terrestris) use radial optic expansion cues to control in-flight decelerations during landing. By analyzing the flight dynamics of 4,672 landing maneuvers, we showed that landing bumblebees exhibit a series of deceleration bouts, unlike landing honeybees that continuously decelerate. During each bout, the bumblebee keeps its relative rate of optical expansion constant, and from one bout to the next, the bumblebee tends to shift to a higher, constant relative rate of expansion. This modular landing strategy is relatively fast compared to the strategy described for honeybees and results in approach dynamics that is strikingly similar to that of pigeons and hummingbirds. The here discovered modular landing strategy of bumblebees helps explaining why these important pollinators in nature and horticulture can forage effectively in challenging conditions; moreover, it has potential for bio-inspired landing strategies in flying robots.

Distance estimation by Asian honey bees in two visually different landscapes

Honey bees estimate distances to food sources using image motion experienced on the flight path and they use this measure to tune the waggle phase duration in their dance communication. Most studies on the dance-related odometer are based on experiments with Apis mellifera foragers trained into small tunnels with black and white patterns which allowed quantifiable changes in the optic flow. In this study, we determined the calibration curves of two Asian honey bee species, A. florea and A. cerana, in two different natural environments with clear differences in the vegetation conditions and hence visual contrast. We found that the dense vegetation condition (with higher contrast) elicited a more rapid increase in the waggle phase duration with distance than the sparse vegetation in A. florea but not in A. cerana Our findings suggest that contrast sensitivity of the waggle dance odometer might vary among honey bee species.

The effect of optic flow cues on honeybee flight control in wind

To minimize the risk of colliding with the ground or other obstacles, flying animals need to control both their ground speed and ground height. This task is particularly challenging in wind, where head winds require an animal to increase its airspeed to maintain a constant ground speed and tail winds may generate negative airspeeds, rendering flight more difficult to control. In this study, we investigate how head and tail winds affect flight control in the honeybee Apis mellifera, which is known to rely on the pattern of visual motion generated across the eye-known as optic flow-to maintain constant ground speeds and heights. We find that, when provided with both longitudinal and transverse optic flow cues (in or perpendicular to the direction of flight, respectively), honeybees maintain a constant ground speed but fly lower in head winds and higher in tail winds, a response that is also observed when longitudinal optic flow cues are minimized. When the transverse component of optic flow is minimized, or when all optic flow cues are minimized, the effect of wind on ground height is abolished. We propose that the regular sidewards oscillations that the bees make as they fly may be used to extract information about the distance to the ground, independently of the longitudinal optic flow that they use for ground speed control. This computationally simple strategy could have potential uses in the development of lightweight and robust systems for guiding autonomous flying vehicles in natural environments.