The Department of Computer Science at the University of Cyprus cordially invites you to the Invited Course Lecture entitled:

Insect Visual Homing: Investigations of the Snapshot Model

Speaker: Dr. Andrew Philippides
Affiliation: University of Sussex, UK
Category: Invited Course Lecture
Location: Room 147, Faculty of Pure and Applied Sciences (FST-01), 1 University Avenue, 2109 Nicosia, Cyprus (directions)
Date: Monday, September 21th, 2009
Time: 10:00-13:00 EET
Host: Christos Schizas (schizas AT

Visual homing, the ability to get back to a nest or goal location using visual landmarks, is a vital capability for insects. Insects appear to achieve this behaviour through a process of image-matching in which the direction to nest or goal is recovered from the difference between the view from their current position and a view or 'snapshot' stored at the nest or goal position. Since this type of view-based homing was first proposed, simple, snapshot-type models have demonstrated successful homing performance over a range of environments and robotic platforms. In this talk, I will introduce the field of view-based homing and review the main snapshot-type models. I will then discuss three aspects of visual homing that I am currently working on: how the snapshot is learnt and used; what visual features make up the snapshot; and over what range a single snapshot is sufficient for homing. I will first discuss recent work testing snapshot models in several natural environments. While it is known that insects use image matching to return home, the extent of the area within which insects can navigate in natural environments using a single snapshot has yet to be determined. This information is necessary before we can interpret data coming from the radar tracking of homing bees which some authors have used to conclude that a map-like representation is required for navigation over bees' natural foraging range. I will show that the information for homing can exist in natural environments over large-scales (100s of metres) and where in the world this information is coming from. I will then discuss the results of this work in the light of both the natural visual ecology and natural behaviour of Australian Desert Ants as they navigate between nest and a feeder. Recent work by a colleague at Sussex has shown that the outline of objects against the skyline is sufficient for these ants to recover the direction home from the feeder. Here I will show that the skyline contains enough information for visual homing algorithms to function successfully. I will then discuss how the environment limits the range of a single snapshot, but that this range can be increased by considering the ant's required behaviour - route following - which leads to a simpler model of navigation. I will conclude by discussing bumblebee learning flights and how this remarkable innate behaviour facilitates view-based homing. To enable them to locate their inconspicuous nest entrances using local visual landmarks, bees and wasps perform orientation or learning flights when they leave the nest to forage. This behaviour includes a number of stereotyped flight manoeuvres which appear to be structured to mediate the active acquisition of visual information. We have recorded and analysed bumblebee learning flights in multiple visual environments. Here I will present several flight strategies that bumblebees use to learn, and later find, the nest location during outward and return flights, respectively.

Short Bio:
Andrew Philippides is a Lecturer II within the Department of Informatics in the University of Sussex, where he is a member of the Sussex insect Navigation Group within the Centre for Computational Neuroscience and Robotics. He gained his doctorate in neuroscience at Sussex studying diffusible neuromodulators in real and artificial nervous systems and has been at Sussex ever since. While continuing his research into neuromodulation in networks, he also studies visual navigation in insects using a combination of behavioural experiments, mathematical and robotic modelling.

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