Having previously developed and tested insect-inspired visual navigation algorithms for ground-based agents, we here investigate their robustness when applied to agents moving in three dimensions, to assess if they are applicable to both flying insects and robots, focusing on the impact and potential utility of changes in height. We first demonstrate that a robot implementing a route navigation algorithm can successfully navigate a route through an indoor environment at a variety of heights, even using images saved at different heights. We show that that in our environments, the efficacy of route navigation is increased with increasing height and also, for those environments, that there is better transfer of information when using images learnt at a high height to navigate when flying lower, than the other way around. This suggests that there is perhaps an adaptive value to the storing and use of views from increased height. To assess the limits to this result, we show that it is possible for a ground-based robot to recover the correct heading when using goal images stored from the perspective of a quadcopter. Through the robustness of this bio-inspired algorithm, we thus demonstrate the benefits of the ALife approach.

Insect-Inspired models of visual navigation, that operate by scanning for familiar views of the world, have been shown to be capable of robust route navigation in simulation. These familiarity-based navigation algorithms operate by training an artificial neural network (ANN) with views from a training route, so that it can then output a familiarity score for any new view. In this paper we show that such an algorithm – with all computation performed on a small low-power robot – is capable of delivering reliable direction information along real-world outdoor routes, even when scenes contain few local landmarks and have high-levels of noise (from variable lighting and terrain). Indeed, routes can be precisely recapitulated and we show that the required computation and storage does not increase with the number of training views. Thus the ANN provides a compact representation of the knowledge needed to traverse a route. In fact, rather than losing information, there are instances where the use of an ANN ameliorates the problems of sub optimal paths caused by tortuous training routes. Our results suggest the feasibility of familiarity-based navigation for long-range autonomous visual homing.