Show simple item record

dc.contributor.advisorWebb, Barbara
dc.contributor.advisorHeinze, Stanley
dc.contributor.authorStone, Thomas Jonathan
dc.date.accessioned2018-03-22T11:00:52Z
dc.date.available2018-03-22T11:00:52Z
dc.date.issued2017-11-30
dc.identifier.urihttp://hdl.handle.net/1842/28909
dc.description.abstractAnimals 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.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionStone, T., Mangan, M., Ardin, P., andWebb, B. (2014). Sky segmentation with ultraviolet images can be used for navigation. In Proceedings of Robotics: Science and Systems, Berkeley, USA.en
dc.relation.hasversionStone, T., Differt, D., Milford, M., and Webb, B. (2016a). Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 5615– 5622. IEEE.en
dc.subjectnavigationen
dc.subjectplace recognitionen
dc.subjectlocalisationen
dc.subjectlocalizationen
dc.subjectultravioleten
dc.subjectpath integrationen
dc.subjectcentral complexen
dc.subjectUVen
dc.subjectspherical harmonicsen
dc.subjectinsecten
dc.subjectbioroboticsen
dc.titleMechanisms of place recognition and path integration based on the insect visual systemen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen
dc.rights.embargodate2018-11-30
dcterms.accessRightsRestricted Accessen


Files in this item

This item appears in the following Collection(s)

Show simple item record