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Rectilinear forms of snake-like robotic locomotion are anticipated to be anadvantage in obstacle-strewn scenarios characterizing urban disaster zones,subterranean collapses, and other natural environments. The elongated,laterally-narrow footprint associated with these motion strategies iswell-suited to traversal of confined spaces and narrow pathways. Navigation andpath planning in the absence of global sensing, however, remains a pivotalchallenge to be addressed prior to practical deployment of these roboticmechanisms. Several challenges related to visual processing and localizationneed 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 inconjunction with perception-space collision detection permits path planning fornavigation. A previously presented dynamical reduction of rectilinear snakelocomotion to a non-holonomic kinematic vehicle informs both SLAM and planning.The simplified motion model is then applied to track planned trajectoriesthrough an obstacle configuration. This navigational framework enables asnake-like robotic platform to autonomously navigate and traverse unknownscenarios with only monocular vision.

Autonomous, Monocular, Vision-Based Snake Robot Navigation and Traversal of Cluttered Environments using Rectilinear Gait Motion