Dynamic Bipedal Locomotion over Stochastic Discrete Terrain

Описание: Due to their morphology and mechanical design,
bipedal robots have the ability to traverse over a wide range
of terrain including those with discrete footholds like stepping
stones. This work addresses the challenge of dynamic robotic
walking over stochastically generated stepping stones with
significant variations in step length and step height, and where
the robot has knowledge about the location of the next discrete
foothold only one step ahead. Specifically, our approach utilizes
a 2-step periodic gait optimization technique to build a library
of gaits parametrized by their resulting step lengths and step
heights, as well as the initial configuration of the robot. By doing
so, we address the problems involved during step transition
when switching between the different walking gaits. We then
use gait interpolation in real-time to obtain the desired gait.
The proposed method is successfully validated on ATRIAS, an
underactuated, human-scale bipedal robot, to achieve precise
footstep placement. With no change in step height, step lengths
are varied in the range of [23:78] cm. When both step length
and step height are changed, their variation are within [30:65]
cm and [-22:22] cm respectively. The average walking speed of
both these experiments is 0.6 m/s.