Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors

Low cost robots, such as vacuum cleaners or lawn mowers, employ simplistic and often random navigation policies. Although a large number of sophisticated localization and planning approaches exist, they require additional sensors like LIDAR sensors, cameras or time of flight sensors. In this work, we propose a global localization method biologically inspired by simple insects, such as the ant Cataglyphis that is able to return from distant locations to its nest in the desert without any or with limited perceptual cues. Like in Cataglyphis, the underlying idea of our localization approach is to first compute a pose estimate from pro-prioceptual sensors only, using land navigation, and thereafter refine the estimate through a systematic search in a particle filter that integrates the rare visual feedback. In simulation experiments in multiple environments, we demonstrated that this bioinspired principle can be used to compute accurate pose estimates from binary visual cues only. Such intelligent localization strategies can improve the performance of any robot with limited sensing capabilities such as household robots or toys.Comment: Accepted to BIOSIGNALS 201

Mechanisms of place recognition and path integration based on the insect visual system

Animals are often able to solve complex navigational tasks in very challenging terrain, despite using low resolution sensors and minimal computational power, providing inspiration for robots. In particular, many species of insect are known to solve complex navigation problems, often combining an array of different behaviours (Wehner et al., 1996; Collett, 1996). Their nervous system is also comparatively simple, relative to that of mammals and other vertebrates. In the first part of this thesis, the visual input of a navigating desert ant, Cataglyphis velox, was mimicked by capturing images in ultraviolet (UV) at similar wavelengths to the ant’s compound eye. The natural segmentation of ground and sky lead to the hypothesis that skyline contours could be used by ants as features for navigation. As proof of concept, sky-segmented binary images were used as input for an established localisation algorithm SeqSLAM (Milford and Wyeth, 2012), validating the plausibility of this claim (Stone et al., 2014). A follow-up investigation sought to determine whether using the sky as a feature would help overcome image matching problems that the ant often faced, such as variance in tilt and yaw rotation. A robotic localisation study showed that using spherical harmonics (SH), a representation in the frequency domain, combined with extracted sky can greatly help robots localise on uneven terrain. Results showed improved performance to state of the art point feature localisation methods on fast bumpy tracks (Stone et al., 2016a). In the second part, an approach to understand how insects perform a navigational task called path integration was attempted by modelling part of the brain of the sweat bee Megalopta genalis. A recent discovery that two populations of cells act as a celestial compass and visual odometer, respectively, led to the hypothesis that circuitry at their point of convergence in the central complex (CX) could give rise to path integration. A firing rate-based model was developed with connectivity derived from the overlap of observed neural arborisations of individual cells and successfully used to build up a home vector and steer an agent back to the nest (Stone et al., 2016b). This approach has the appeal that neural circuitry is highly conserved across insects, so findings here could have wide implications for insect navigation in general. The developed model is the first functioning path integrator that is based on individual cellular connections

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Holistic methods for visual navigation of mobile robots in outdoor environments

Differt D. Holistic methods for visual navigation of mobile robots in outdoor environments. Bielefeld: Universität Bielefeld; 2017

Combining omnidirectional vision with polarization vision for robot navigation

La polarisation est le phénomène qui décrit les orientations des oscillations des ondes lumineuses qui sont limitées en direction. La lumière polarisée est largement utilisée dans le règne animal,à partir de la recherche de nourriture, la défense et la communication et la navigation. Le chapitre (1) aborde brièvement certains aspects importants de la polarisation et explique notre problématique de recherche. Nous visons à utiliser un capteur polarimétrique-catadioptrique car il existe de nombreuses applications qui peuvent bénéficier d'une telle combinaison en vision par ordinateur et en robotique, en particulier pour l'estimation d'attitude et les applications de navigation. Le chapitre (2) couvre essentiellement l'état de l'art de l'estimation d'attitude basée sur la vision.Quand la lumière non-polarisée du soleil pénètre dans l'atmosphère, l'air entraine une diffusion de Rayleigh, et la lumière devient partiellement linéairement polarisée. Le chapitre (3) présente les motifs de polarisation de la lumière naturelle et couvre l'état de l'art des méthodes d'acquisition des motifs de polarisation de la lumière naturelle utilisant des capteurs omnidirectionnels (par exemple fisheye et capteurs catadioptriques). Nous expliquons également les caractéristiques de polarisation de la lumière naturelle et donnons une nouvelle dérivation théorique de son angle de polarisation.Notre objectif est d'obtenir une vue omnidirectionnelle à 360 associée aux caractéristiques de polarisation. Pour ce faire, ce travail est basé sur des capteurs catadioptriques qui sont composées de surfaces réfléchissantes et de lentilles. Généralement, la surface réfléchissante est métallique et donc l'état de polarisation de la lumière incidente, qui est le plus souvent partiellement linéairement polarisée, est modifiée pour être polarisée elliptiquement après réflexion. A partir de la mesure de l'état de polarisation de la lumière réfléchie, nous voulons obtenir l'état de polarisation incident. Le chapitre (4) propose une nouvelle méthode pour mesurer les paramètres de polarisation de la lumière en utilisant un capteur catadioptrique. La possibilité de mesurer le vecteur de Stokes du rayon incident est démontré à partir de trois composants du vecteur de Stokes du rayon réfléchi sur les quatre existants.Lorsque les motifs de polarisation incidents sont disponibles, les angles zénithal et azimutal du soleil peuvent être directement estimés à l'aide de ces modèles. Le chapitre (5) traite de l'orientation et de la navigation de robot basées sur la polarisation et différents algorithmes sont proposés pour estimer ces angles dans ce chapitre. A notre connaissance, l'angle zénithal du soleil est pour la première fois estimé dans ce travail à partir des schémas de polarisation incidents. Nous proposons également d'estimer l'orientation d'un véhicule à partir de ces motifs de polarisation.Enfin, le travail est conclu et les possibles perspectives de recherche sont discutées dans le chapitre (6). D'autres exemples de schémas de polarisation de la lumière naturelle, leur calibrage et des applications sont proposées en annexe (B).Notre travail pourrait ouvrir un accès au monde de la vision polarimétrique omnidirectionnelle en plus des approches conventionnelles. Cela inclut l'orientation bio-inspirée des robots, des applications de navigation, ou bien la localisation en plein air pour laquelle les motifs de polarisation de la lumière naturelle associés à l'orientation du soleil à une heure précise peuvent aboutir à la localisation géographique d'un véhiculePolarization is the phenomenon that describes the oscillations orientations of the light waves which are restricted in direction. Polarized light has multiple uses in the animal kingdom ranging from foraging, defense and communication to orientation and navigation. Chapter (1) briefly covers some important aspects of polarization and explains our research problem. We are aiming to use a polarimetric-catadioptric sensor since there are many applications which can benefit from such combination in computer vision and robotics specially robot orientation (attitude estimation) and navigation applications. Chapter (2) mainly covers the state of art of visual based attitude estimation.As the unpolarized sunlight enters the Earth s atmosphere, it is Rayleigh-scattered by air, and it becomes partially linearly polarized. This skylight polarization provides a signi cant clue to understanding the environment. Its state conveys the information for obtaining the sun orientation. Robot navigation, sensor planning, and many other applications may bene t from using this navigation clue. Chapter (3) covers the state of art in capturing the skylight polarization patterns using omnidirectional sensors (e.g fisheye and catadioptric sensors). It also explains the skylight polarization characteristics and gives a new theoretical derivation of the skylight angle of polarization pattern. Our aim is to obtain an omnidirectional 360 view combined with polarization characteristics. Hence, this work is based on catadioptric sensors which are composed of reflective surfaces and lenses. Usually the reflective surface is metallic and hence the incident skylight polarization state, which is mostly partially linearly polarized, is changed to be elliptically polarized after reflection. Given the measured reflected polarization state, we want to obtain the incident polarization state. Chapter (4) proposes a method to measure the light polarization parameters using a catadioptric sensor. The possibility to measure the incident Stokes is proved given three Stokes out of the four reflected Stokes. Once the incident polarization patterns are available, the solar angles can be directly estimated using these patterns. Chapter (5) discusses polarization based robot orientation and navigation and proposes new algorithms to estimate these solar angles where, to the best of our knowledge, the sun zenith angle is firstly estimated in this work given these incident polarization patterns. We also propose to estimate any vehicle orientation given these polarization patterns. Finally the work is concluded and possible future research directions are discussed in chapter (6). More examples of skylight polarization patterns, their calibration, and the proposed applications are given in appendix (B). Our work may pave the way to move from the conventional polarization vision world to the omnidirectional one. It enables bio-inspired robot orientation and navigation applications and possible outdoor localization based on the skylight polarization patterns where given the solar angles at a certain date and instant of time may infer the current vehicle geographical location.DIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIOR

Swarm intelligence in multi-robot systems has become an important area of research within collective robotics. Researchers have gained inspiration from biological systems and proposed a variety of industrial, commercial, and military robotics applications. In order to bridge the gap between theory and application, a strong focus is required on robotic implementation of swarm intelligence. To date, theoretical research and computer simulations in the field have dominated, with few successful demonstrations of swarm-intelligent robotic systems. In this thesis, a study of intelligent foraging behavior via indirect communication between simple individual agents is presented. Models of foraging are reviewed and analyzed with respect to the system dynamics and dependence on important parameters. Computer simulations are also conducted to gain an understanding of foraging behavior in systems with large populations. Finally, a novel robotic implementation is presented. The experiment successfully demonstrates cooperative group foraging behavior without direct communication. Trail-laying and trail-following are employed to produce the required stigmergic cooperation. Real robots are shown to achieve increased task efficiency, as a group, resulting from indirect interactions. Experimental results also confirm that trail-based group foraging systems can adapt to dynamic environments

Modeling the Bat Spatial Navigation System: A Neuromorphic VLSI Approach

Autonomously navigating robots have long been a tough challenge facing engineers. The recent push to develop micro-aerial vehicles for practical military, civilian, and industrial use has added a significant power and time constraint to the challenge. In contrast, animals, from insects to humans, have been navigating successfully for millennia using a wide range of variants of the ultra-low-power computational system known as the brain. For this reason, we look to biological systems to inspire a solution suitable for autonomously navigating micro-aerial vehicles. In this dissertation, the focus is on studying the neurobiological structures involved in mammalian spatial navigation. The mammalian brain areas widely believed to contribute directly to navigation tasks are the Head Direction Cells, Grid Cells and Place Cells found in the post-subiculum, the medial entorhinal cortex, and the hippocampus, respectively. In addition to studying the neurobiological structures involved in navigation, we investigate various neural models that seek to explain the operation of these structures and adapt them to neuromorphic VLSI circuits and systems. We choose the neuromorphic approach for our systems because we are interested in understanding the interaction between the real-time, physical implementation of the algorithms and the real-world problem (robot and environment). By utilizing both analog and asynchronous digital circuits to mimic similar computations in neural systems, we envision very low power VLSI implementations suitable for providing practical solutions for spatial navigation in micro-aerial vehicles

On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div