  Navigating a large-scaled robot in unknown and cluttered height-constrained
environments is challenging. Not only is a fast and reliable planning algorithm
required to go around obstacles, the robot should also be able to change its
intrinsic dimension by crouching in order to travel underneath
height-constrained regions. There are few mobile robots that are capable of
handling such a challenge, and bipedal robots provide a solution. However, as
bipedal robots have nonlinear and hybrid dynamics, trajectory planning while
ensuring dynamic feasibility and safety on these robots is challenging. This
paper presents an end-to-end autonomous navigation framework which leverages
three layers of planners and a variable walking height controller to enable
bipedal robots to safely explore height-constrained environments. A
vertically-actuated Spring-Loaded Inverted Pendulum (vSLIP) model is introduced
to capture the robot's coupled dynamics of planar walking and vertical walking
height. This reduced-order model is utilized to optimize for long-term and
short-term safe trajectory plans. A variable walking height controller is
leveraged to enable the bipedal robot to maintain stable periodic walking gaits
while following the planned trajectory. The entire framework is tested and
experimentally validated using a bipedal robot Cassie. This demonstrates
reliable autonomy to drive the robot to safely avoid obstacles while walking to
the goal location in various kinds of height-constrained cluttered
environments.
