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