  Rectilinear forms of snake-like robotic locomotion are anticipated to be an
advantage in obstacle-strewn scenarios characterizing urban disaster zones,
subterranean collapses, and other natural environments. The elongated,
laterally-narrow footprint associated with these motion strategies is
well-suited to traversal of confined spaces and narrow pathways. Navigation and
path planning in the absence of global sensing, however, remains a pivotal
challenge to be addressed prior to practical deployment of these robotic
mechanisms. Several challenges related to visual processing and localization
need to be resolved to to enable navigation. As a first pass in this direction,
we equip a wireless, monocular color camera to the head of a robotic snake.
Visiual odometry and mapping from ORB-SLAM permits self-localization in planar,
obstacle-strewn environments. Ground plane traversability segmentation in
conjunction with perception-space collision detection permits path planning for
navigation. A previously presented dynamical reduction of rectilinear snake
locomotion to a non-holonomic kinematic vehicle informs both SLAM and planning.
The simplified motion model is then applied to track planned trajectories
through an obstacle configuration. This navigational framework enables a
snake-like robotic platform to autonomously navigate and traverse unknown
scenarios with only monocular vision.
