Taking Inspiration from Flying Insects to Navigate inside Buildings

These days, flying insects are seen as genuinely agile micro air vehicles fitted with smart sensors and also parsimonious in their use of brain resources. They are able to visually navigate in unpredictable and GPS-denied environments. Understanding how such tiny animals work would help engineers to figure out different issues relating to drone miniaturization and navigation inside buildings. To turn a drone of ~1 kg into a robot, miniaturized conventional avionics can be employed; however, this results in a loss of their flight autonomy. On the other hand, to turn a drone of a mass between ~1 g (or less) and ~500 g into a robot requires an innovative approach taking inspiration from flying insects both with regard to their flapping wing propulsion system and their sensory system based mainly on motion vision in order to avoid obstacles in three dimensions or to navigate on the basis of visual cues. This chapter will provide a snapshot of the current state of the art in the field of bioinspired optic flow sensors and optic flow-based direct feedback loops applied to micro air vehicles flying inside buildings

Perceptual-motor coupling betwwen Helicopter and ship during ship deck landing maneuvers

Helicopter ship landings are challenging operations appealing for further researches and innovations to help pilots safely dealing with a variety of environmental, visual and operational contexts. Indeed, landing on ship not only differs from land-based landings in the extent that the landing area is located on the flight deck, which is most of the time oscillating, but also because the visual environment is often impoverished (e.g., rain, fog, night conditions). In order to improve safety at deck-landing, the French Aerospace Lab (ONERA) and the French Defense Agency (DGA) are interested in understanding pilots’ perceptual-motor strategies involved in a such complex task so as to design ecological interfaces assisting pilots’ landing maneuvers

Fast reproducible identification and large-scale databasing of individual functional cognitive networks

<p>Abstract</p> <p>Background</p> <p>Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.</p> <p>Results</p> <p>81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects.</p> <p>Conclusion</p> <p>This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.</p

A methodology to estimate the cost of transport of a hexapod robot based on single leg performance

International audienceIn this methodological work, we introduce a new approach to evaluate the electric power consumption of a legged robot, by analyzing a single leg power consumption on a dedicated test bench, which doesn’t require any dynamic model of the robot and takes account of the non-linearities of the mechanism. The main idea is to split the measured data in two sets: energy consumption during the propulsion stroke (stance), and energy consumption during the leg return stroke (swing). This approach will be helpful to design and fabricate a low power consumption robotic leg and to evaluate the benefits with respect to a conventional leg design

Design of a Bio-Inspired Optical Compass for Education Purposes

International Conference on Robotics in Education (RIE), ELECTR NETWORK, APR 27-28, 2022International audienceThis study concerns the development of an optical compass inspired by the celestial compass of the desert ant Cataglyphis. This bioinspired navigational instrument opens up new opportunities for navigation in the absence of GNSS or GSM coverage for locating outdoors. This pedagogical activity, through the design of the instrument, aims at understand different physical phenomena involved in optical heading detection: Rayleigh scattering of sunlight in the sky, polarization of light and measurement of heading from photosensors. The design and the fabrication of an experimental teaching device are both described. Experiments were performed to allow students (2nd year of master in robotics & IoT) to become familiar with bio-inspired engineering applied to optical heading detection. Students used an Arduino board (8-bit architecture) to address issues related to the real-time processing of microcontroller with limited computational capabilities. Finally, examples of measurements made by students are presented to demonstrate the pedagogical use of such an experimental device for heading measurement in robotics. Our prototype works in the blue visible light and has only 4 photosensors, each one covered with a different orientation of its polarizing filter

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

International audienceBees 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. 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

Antcar : Tâche simple de suivi d'itinéraire avec une vision et un modèle neuronal inspirés des fourmis

The goal of this project is to develop a new method of Route following for mobile robots in a GNSSdenied environment like urban canyons or indoor. We used a robust biologically constrained neural model inspired by ants developed previously in simulation to assess the familiarity index of a panorama. A visual compass algorithm consists in determining the orientation of the maximum familiarity index with respect to the learned panoramas along a path. A car-like robot was equipped with a 220°fisheye camera. The visual compass algorithm used low resolution images of 44x44 pixels (5°/pixel) indoors and outdoors to determine the direction to follow the previously visually-learned path. Finally, the car-like robot was automated to recall the learned path indoors. The biologically constrained neural model compressed the visual information with a high efficiency so that the visual memory has a very low footprint of a few tens of kilobits that does not depend directly on the path length.Le but de ce projet est de développer une nouvelle méthode de suivi d'itinéraire pour les robots mobiles dans un environnement dépourvu de GNSS comme les canyons urbains ou l'intérieur. Nous avons utilisé un modèle neuronal robuste et biologiquement contraint inspiré des fourmis développé précédemment en simulation pour évaluer l'indice de familiarité d'un panorama. Un algorithme de boussole visuelle consiste à déterminer l'orientation de l'indice de familiarité maximal par rapport aux panoramas appris le long d'un chemin. Un robot de type voiture a été équipé d'une caméra à œil de poisson de 220°. L'algorithme de la boussole visuelle utilise des images basse résolution de 44x44 pixels (5°/pixel) à l'intérieur et à l'extérieur pour déterminer la direction à suivre le long du chemin préalablement appris visuellement. Enfin, le robot de type voiture a été automatisé pour rappeler le chemin appris à l'intérieur. Le modèle neuronal biologiquement contraint a comprimé les informations visuelles avec une grande efficacité, de sorte que la mémoire visuelle a une empreinte très faible de quelques dizaines de kilobits qui ne dépend pas directement de la longueur du chemin

A stand-alone polarimetric acquisition system for producing a long-term skylight dataset

International audienceSkylight polarization phenomenon is at the origin of a recent growing interest for bio-inspired navigation. Skylight based orientation sensors can be simulated on the basis of physical models. In parallel, machine learning algorithms require a large amount of data to be trained. However, while some simulated databases already exists in the literature, a public database composed of real-world color polarimetric images of the sky in various weather conditions does not. In this study, a long-term experimental device is presented, designed to be left in a distant roof to acquire data over several months, using a Division-of-Focal-Plane polarization imager with a fisheye lens mounted on a rotative telescope mount. An open-source mechanical and electrical design is proposed for easy replication at other locations, with an algorithm to get the sensor's orientation and geometrical calibration in the East-North-Up frame. A sample one-month long dataset is provided on a public archive

Antcar : Tâche simple de suivi d'itinéraire avec une vision et un modèle neuronal inspirés des fourmis

The goal of this project is to develop a new method of Route following for mobile robots in a GNSSdenied environment like urban canyons or indoor. We used a robust biologically constrained neural model inspired by ants developed previously in simulation to assess the familiarity index of a panorama. A visual compass algorithm consists in determining the orientation of the maximum familiarity index with respect to the learned panoramas along a path. A car-like robot was equipped with a 220°fisheye camera. The visual compass algorithm used low resolution images of 44x44 pixels (5°/pixel) indoors and outdoors to determine the direction to follow the previously visually-learned path. Finally, the car-like robot was automated to recall the learned path indoors. The biologically constrained neural model compressed the visual information with a high efficiency so that the visual memory has a very low footprint of a few tens of kilobits that does not depend directly on the path length.Le but de ce projet est de développer une nouvelle méthode de suivi d'itinéraire pour les robots mobiles dans un environnement dépourvu de GNSS comme les canyons urbains ou l'intérieur. Nous avons utilisé un modèle neuronal robuste et biologiquement contraint inspiré des fourmis développé précédemment en simulation pour évaluer l'indice de familiarité d'un panorama. Un algorithme de boussole visuelle consiste à déterminer l'orientation de l'indice de familiarité maximal par rapport aux panoramas appris le long d'un chemin. Un robot de type voiture a été équipé d'une caméra à œil de poisson de 220°. L'algorithme de la boussole visuelle utilise des images basse résolution de 44x44 pixels (5°/pixel) à l'intérieur et à l'extérieur pour déterminer la direction à suivre le long du chemin préalablement appris visuellement. Enfin, le robot de type voiture a été automatisé pour rappeler le chemin appris à l'intérieur. Le modèle neuronal biologiquement contraint a comprimé les informations visuelles avec une grande efficacité, de sorte que la mémoire visuelle a une empreinte très faible de quelques dizaines de kilobits qui ne dépend pas directement de la longueur du chemin