SkyPole-A method for locating the north celestial pole from skylight polarization patterns.

True north can be determined on Earth by three means: magnetic compasses, stars, and via the global navigation satellite systems (GNSS), each of which has its own drawbacks. GNSS are sensitive to jamming and spoofing, magnetic compasses are vulnerable to magnetic interferences, and the stars can be used only at night with a clear sky. As an alternative to these methods, nature-inspired navigational cues are of particular interest. Celestial polarization, which is used by insects such as Cataglyphis ants, can provide useful directional cues. Migrating birds calibrate their magnetic compasses by observing the celestial rotation at night. By combining these cues, we have developed a bioinspired optical method for finding the celestial pole during the daytime. This method, which we have named SkyPole, is based on the rotation of the skylight polarization pattern. A polarimetric camera was used to measure the degree of skylight polarization rotating with the Sun. Image difference processes were then applied to the time-varying measurements in order to determine the north celestial pole's position and thus the observer's latitude and bearing with respect to the true north.

Passive Polarized Vision for Autonomous Vehicles: A Review.

This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

Floor and ceiling mirror configurations to study altitude control in honeybees.

To investigate altitude control in honeybees, an optical configuration was designed to manipulate or cancel the optic flow. It has been widely accepted that honeybees rely on the optic flow generated by the ground to control their altitude. Here, we create an optical configuration enabling a better understanding of the mechanism of altitude control in honeybees. This optical configuration aims to mimic some of the conditions that honeybees experience over a natural water body. An optical manipulation, based on a pair of opposed horizontal mirrors, was designed to remove any visual information coming from the floor and ceiling. Such an optical manipulation allowed us to get closer to the seminal experiment of Heran & Lindauer 1963. Zeitschrift für vergleichende Physiologie47, 39-55. (doi:10.1007/BF00342890). Our results confirmed that a reduction or an absence of ventral optic flow in honeybees leads to a loss in altitude, and eventually a collision with the floor.

Insect-Inspired Robots: Bridging Biological and Artificial Systems.

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

Shaping the energy curves of a servomotor-based hexapod robot.

The advantageous versatility of hexapod robots is often accompanied by high power consumption, while animals have evolved an energy efficient locomotion. However, there are a lack of methods able to compare and apply animals' energetic optimizations to robots. In this study, we applied our method to a full servomotor-based hexapod robot to evaluate its energetic performance. Using an existing framework based on the laws of thermodynamics, we estimated four metrics using a dedicated test bench and a simulated robotic leg. We analyzed the characteristics of a single leg to shape the energetic profile of the full robot to a given task. Energy saving is improved by 10% through continuous duty factor adjustment with a 192% increase in power maximization. Moreover, adjusting the robot's velocity by the step length and associating this with gait switching, reduces the power loss by a further 10% at low-speed locomotion. However, unlike in animals, only one unique optimal operating point has been revealed, which is a disadvantage caused by the low energetic efficiency of servomotor-based hexapods. Thus, these legged robots are severely limited in their capacity to optimally adjust their locomotion to various tasks-a counter-intuitive conclusion for a supposedly versatile robot.

An extended database of annotated skylight polarization images covering a period of two months.

OBJECTIVES: Recent advances in bio-inspired navigation have sparked interest in the phenomenon of skylight polarization. This interest stems from the potential of skylight-based orientation sensors, which performance can be simulated using physical models. However, the effectiveness of machine learning algorithms in this domain relies heavily on access to large datasets for training. Although there are several databases of simulated images in literature, there remains a lack of publicly available annotated real-world color polarimetric images of the sky across various weather conditions. DATA DESCRIPTION: We present here a dataset obtained from a long-term experimental setup designed to collect polarimetric images from a stand-alone camera. The setup utilizes a Division-of-Focal-Plane polarization camera equipped with a fisheye lens mounted on a rotative telescope mount. Furthermore, we obtained the sensor's orientation within the East-North-Up (ENU) frame from a geometrical calibration and an algorithm provided with the database. To facilitate further research in this area, the present sample dataset spanning two months has been made available on a public archive with manual annotations as required by deep learning algorithms. The images were acquired at 10 min intervals and were taken with various exposure times ranging from 33µs to 300ms.

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.

Visual augmentation of deck-landing-ability improves helicopter ship landing decisions.

When attempting to land on a ship deck tossed by the sea, helicopter pilots must make sure that the helicopter can develop sufficient lift to be able to safely touchdown. This reminder of affordance theory led us to model and study the affordance of deck-landing-ability, which defines whether it is possible to land safely on a ship deck depending on the helicopter's available lift and the ship's deck heave movements. Two groups of participants with no piloting experience using a laptop helicopter simulator attempted to land either a low-lifter or a heavy-lifter helicopter on a virtual ship deck by either triggering a pre-programmed lift serving as the descent law if it was deemed possible to land, or aborting the deck-landing maneuver. The deck-landing-ability was manipulated by varying the helicopter's initial altitude and the ship's heave phase between trials. We designed a visual augmentation making visible the deck-landing-ability, and thus enabling participants to maximize the safety of their deck-landing attempts and reduce the number of unsafe deck-landing. The visual augmentation presented here was perceived by participants as a means of facilitating this decision-making process. The benefits were found to have originated from the clear-cut distinction it helped them to make between safe and unsafe deck-landing windows and the display of the optimal time for initiating the landing.

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.

Ecological Entomology: How Is Gibson's Framework Useful?

To date, numerous studies have demonstrated the fundamental role played by optic flow in the control of goal-directed displacement tasks in insects. Optic flow was first introduced by Gibson as part of their ecological approach to perception and action. While this theoretical approach (as a whole) has been demonstrated to be particularly suitable for the study of goal-directed displacements in humans, its usefulness in carrying out entomological field studies remains to be established. In this review we would like to demonstrate that the ecological approach to perception and action could be relevant for the entomologist community in their future investigations. This approach could provide a conceptual and methodological framework for the community in order to: (i) take a critical look at the research carried out to date, (ii) develop rigorous and innovative experimental protocols, and (iii) define scientific issues that push the boundaries of the current scientific field. After a concise literature review about the perceptual control of displacement in insects, we will present the framework proposed by Gibson and suggest its added value for carrying out research in the field of behavioral ecology in insects.

Ecological design of augmentation improves helicopter ship landing maneuvers: An approach in augmented virtuality.

Helicopter landing on a ship is a visually regulated "rendezvous" task during which pilots must use fine control to land a powerful rotorcraft on the deck of a moving ship tossed by the sea while minimizing the energy at impact. Although augmented reality assistance can be hypothesized to improve pilots' performance and the safety of landing maneuvers by guiding action toward optimal behavior in complex and stressful situations, the question of the optimal information to be displayed to feed the pilots' natural information-movement coupling remains to be investigated. Novice participants were instructed to land a simplified helicopter on a ship in a virtual reality simulator while minimizing energy at impact and landing duration. The wave amplitude and related ship heave were manipulated. We compared the benefits of two types of visual augmentation whose design was based on either solving cockpit-induced visual occlusion problems or strengthening the online regulation of the deceleration by keeping the current [Formula: see text] variable around an ideal value of -0.5 to conduct smooth and efficient landing. Our results showed that the second augmentation, ecologically grounded, offers benefits at several levels of analysis. It decreases the landing duration, improves the control of the helicopter displacement, and sharpens the sensitivity to changes in [Formula: see text]. This underlines the importance for designers of augmented reality systems to collaborate with psychologists to identify the relevant perceptual-motor strategy that must be encouraged before designing an augmentation that will enhance it.

AntBot: A six-legged walking robot able to home like desert ants in outdoor environments.

Autonomous outdoor navigation requires reliable multisensory fusion strategies. Desert ants travel widely every day, showing unrivaled navigation performance using only a few thousand neurons. In the desert, pheromones are instantly destroyed by the extreme heat. To navigate safely in this hostile environment, desert ants assess their heading from the polarized pattern of skylight and judge the distance traveled based on both a stride-counting method and the optic flow, i.e., the rate at which the ground moves across the eye. This process is called path integration (PI). Although many methods of endowing mobile robots with outdoor localization have been developed recently, most of them are still prone to considerable drift and uncertainty. We tested several ant-inspired solutions to outdoor homing navigation problems on a legged robot using two optical sensors equipped with just 14 pixels, two of which were dedicated to an insect-inspired compass sensitive to ultraviolet light. When combined with two rotating polarized filters, this compass was equivalent to two costly arrays composed of 374 photosensors, each of which was tuned to a specific polarization angle. The other 12 pixels were dedicated to optic flow measurements. Results show that our ant-inspired methods of navigation give precise performances. The mean homing error recorded during the overall trajectory was as small as 0.67% under lighting conditions similar to those encountered by ants. These findings show that ant-inspired PI strategies can be used to complement classical techniques with a high level of robustness and efficiency.

Polarized skylight-based heading measurements: a bio-inspired approach.

Many insects such as desert ants, crickets, locusts, dung beetles, bees and monarch butterflies have been found to extract their navigation cues from the regular pattern of the linearly polarized skylight. These species are equipped with ommatidia in the dorsal rim area of their compound eyes, which are sensitive to the angle of polarization of the skylight. In the polarization-based robotic vision, most of the sensors used so far comprise high-definition CCD or CMOS cameras topped with linear polarizers. Here, we present a 2-pixel polarization-sensitive visual sensor, which was strongly inspired by the dorsal rim area of desert ants' compound eyes, designed to determine the direction of polarization of the skylight. The spectral sensitivity of this minimalistic sensor, which requires no lenses, is in the ultraviolet range. Five different methods of computing the direction of polarization were implemented and tested here. Our own methods, the extended and AntBot method, outperformed the other three, giving a mean angular error of only 0.62° ± 0.40° (median: 0.24°) and 0.69° ± 0.52° (median: 0.39°), respectively (mean ± standard deviation). The results obtained in outdoor field studies show that our celestial compass gives excellent results at a very low computational cost, which makes it highly suitable for autonomous outdoor navigation purposes.

Optic flow cues help explain altitude control over sea in freely flying gulls.

For studies of how birds control their altitude, seabirds are of particular interest because they forage offshore where the visual environment can be simply modelled by a flat world textured by waves then generating only ventral visual cues. This study suggests that optic flow, i.e. the rate at which the sea moves across the eye's retina, can explain gulls' altitude control over seas. In particular, a new flight model that includes both energy and optical invariants helps explain the gulls' trajectories during offshore takeoff and cruising flight. A linear mixed model applied to 352 flights from 16 individual lesser black backed gulls (Larus fuscus) revealed a statistically significant optic flow set-point of ca 25° s-1. Thereafter, an optic flow-based flight model was applied to 18 offshore takeoff flights from nine individual gulls. By introducing an upper limit in climb rate on the elevation dynamics, coupled with an optic flow set-point, the predicted altitude gives an optimized fit factor value of 63% on average (30-83% in range) with respect to the GPS data. We conclude that the optic flow regulation principle helps gulls to adjust their altitude over sea without having to directly measure their current altitude.

Insect-inspired vision for autonomous vehicles.

Flying insects are being studied these days as if they were agile micro air vehicles fitted with smart sensors, requiring very few brain resources. The findings obtained on these natural fliers have proved to be extremely valuable when it comes to designing compact low-weight artificial optical sensors capable of performing visual processing tasks robustly under various environmental conditions (light, clouds, contrast). Here, we review some outstanding bio-inspired visual sensors, which can be used for either detecting motion in the visible spectrum or controlling celestial navigation in the ultraviolet spectrum and for attitude stabilisation purposes. Biologically inspired visual sensors do not have to comprise a very large number of pixels: they are able to perform both short and long range navigation tasks surprisingly well with just a few pixels and a weak resolution.

Optic flow-based collision-free strategies: From insects to robots.

Flying insects are able to fly smartly in an unpredictable environment. It has been found that flying insects have smart neurons inside their tiny brains that are sensitive to visual motion also called optic flow. Consequently, flying insects rely mainly on visual motion during their flight maneuvers such as: takeoff or landing, terrain following, tunnel crossing, lateral and frontal obstacle avoidance, and adjusting flight speed in a cluttered environment. Optic flow can be defined as the vector field of the apparent motion of objects, surfaces, and edges in a visual scene generated by the relative motion between an observer (an eye or a camera) and the scene. Translational optic flow is particularly interesting for short-range navigation because it depends on the ratio between (i) the relative linear speed of the visual scene with respect to the observer and (ii) the distance of the observer from obstacles in the surrounding environment without any direct measurement of either speed or distance. In flying insects, roll stabilization reflex and yaw saccades attenuate any rotation at the eye level in roll and yaw respectively (i.e. to cancel any rotational optic flow) in order to ensure pure translational optic flow between two successive saccades. Our survey focuses on feedback-loops which use the translational optic flow that insects employ for collision-free navigation. Optic flow is likely, over the next decade to be one of the most important visual cues that can explain flying insects' behaviors for short-range navigation maneuvers in complex tunnels. Conversely, the biorobotic approach can therefore help to develop innovative flight control systems for flying robots with the aim of mimicking flying insects' abilities and better understanding their flight.

Altitude control in honeybees: joint vision-based learning and guidance.

Studies on insects' visual guidance systems have shed little light on how learning contributes to insects' altitude control system. In this study, honeybees were trained to fly along a double-roofed tunnel after entering it near either the ceiling or the floor of the tunnel. The honeybees trained to hug the ceiling therefore encountered a sudden change in the tunnel configuration midways: i.e. a "dorsal ditch". Thus, the trained honeybees met a sudden increase in the distance to the ceiling, corresponding to a sudden strong change in the visual cues available in their dorsal field of view. Honeybees reacted by rising quickly and hugging the new, higher ceiling, keeping a similar forward speed, distance to the ceiling and dorsal optic flow to those observed during the training step; whereas bees trained to follow the floor kept on following the floor regardless of the change in the ceiling height. When trained honeybees entered the tunnel via the other entry (the lower or upper entry) to that used during the training step, they quickly changed their altitude and hugged the surface they had previously learned to follow. These findings clearly show that trained honeybees control their altitude based on visual cues memorized during training. The memorized visual cues generated by the surfaces followed form a complex optic flow pattern: trained honeybees may attempt to match the visual cues they perceive with this memorized optic flow pattern by controlling their altitude.

A biomimetic vision-based hovercraft accounts for bees' complex behaviour in various corridors.

Here we present the first systematic comparison between the visual guidance behaviour of a biomimetic robot and those of honeybees flying in similar environments. We built a miniature hovercraft which can travel safely along corridors with various configurations. For the first time, we implemented on a real physical robot the 'lateral optic flow regulation autopilot', which we previously studied computer simulations. This autopilot inspired by the results of experiments on various species of hymenoptera consists of two intertwined feedback loops, the speed and lateral control loops, each of which has its own optic flow (OF) set-point. A heading-lock system makes the robot move straight ahead as fast as 69 cm s(-1) with a clearance from one wall as small as 31 cm, giving an unusually high translational OF value (125° s(-1)). Our biomimetic robot was found to navigate safely along straight, tapered and bent corridors, and to react appropriately to perturbations such as the lack of texture on one wall, the presence of a tapering or non-stationary section of the corridor and even a sloping terrain equivalent to a wind disturbance. The front end of the visual system consists of only two local motion sensors (LMS), one on each side. This minimalistic visual system measuring the lateral OF suffices to control both the robot's forward speed and its clearance from the walls without ever measuring any speeds or distances. We added two additional LMSs oriented at +/-45° to improve the robot's performances in stiffly tapered corridors. The simple control system accounts for worker bees' ability to navigate safely in six challenging environments: straight corridors, single walls, tapered corridors, straight corridors with part of one wall moving or missing, as well as in the presence of wind.

A bee in the corridor: centering and wall-following.

In an attempt to better understand the mechanism underlying lateral collision avoidance in flying insects, we trained honeybees (Apis mellifera) to fly through a large (95-cm wide) flight tunnel. We found that, depending on the entrance and feeder positions, honeybees would either center along the corridor midline or fly along one wall. Bees kept following one wall even when a major (150-cm long) part of the opposite wall was removed. These findings cannot be accounted for by the "optic flow balance" hypothesis that has been put forward to explain the typical bees' "centering response" observed in narrower corridors. Both centering and wall-following behaviors are well accounted for, however, by a control scheme called the lateral optic flow regulator, i.e., a feedback system that strives to maintain the unilateral optic flow constant. The power of this control scheme is that it would allow the bee to guide itself visually in a corridor without having to measure its speed or distance from the walls.