  Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to run
on smaller robotic platforms such as Micro-Aerial Vehicles. Bug algorithms are
an alternative that use relatively little processing power, and avoid high
memory consumption by not building an explicit map of the environment. Bug
Algorithms achieve relatively good performance in simulated and robotic maze
solving domains. However, because they are hand-designed, a natural question is
whether they are globally optimal control policies. In this work we explore the
performance of Neuroevolution - specifically NEAT - at evolving control
policies for simulated differential drive robots carrying out generalised maze
navigation. We extend NEAT to include Gated Recurrent Units (GRUs) to help deal
with long term dependencies. We show that both NEAT and our NEAT-GRU can
repeatably generate controllers that outperform I-Bug (an algorithm
particularly well-suited for use in real robots) on a test set of 209 indoor
maze like environments. We show that NEAT-GRU is superior to NEAT in this task
but also that out of the 2 systems, only NEAT-GRU can continuously evolve
successful controllers for a much harder task in which no bearing information
about the target is provided to the agent.
