  As a vital cognitive function of animals, the navigation skill is first built
on the accurate perception of the directional heading in the environment. Head
direction cells (HDCs), found in the limbic system of animals, are proven to
play an important role in identifying the directional heading allocentrically
in the horizontal plane, independent of the animal's location and the ambient
conditions of the environment. However, practical HDC models that can be
implemented in robotic applications are rarely investigated, especially those
that are biologically plausible and yet applicable to the real world. In this
paper, we propose a computational HDC network which is consistent with several
neurophysiological findings concerning biological HDCs, and then implement it
in robotic navigation tasks. The HDC network keeps a representation of the
directional heading only relying on the angular velocity as an input. We
examine the proposed HDC model in extensive simulations and real-world
experiments and demonstrate its excellent performance in terms of accuracy and
real-time capability.
