Walking Stabilization Using Step Timing and Location Adjustment on the Humanoid Robot, Atlas
This work addresses the challenge of robust locomotion for humanoid robots, which is incremental as it builds on existing balancing strategies.
The authors tackled the problem of improving walking stability in humanoid robots by developing algorithms for step timing and location adjustment, enabling the robot to recover from disturbances and maintain balance. They demonstrated the approach in simulation and experiments on the Atlas robot.
While humans are highly capable of recovering from external disturbances and uncertainties that result in large tracking errors, humanoid robots have yet to reliably mimic this level of robustness. Essential to this is the ability to combine traditional "ankle strategy" balancing with step timing and location adjustment techniques. In doing so, the robot is able to step quickly to the necessary location to continue walking. In this work, we present both a new swing speed up algorithm to adjust the step timing, allowing the robot to set the foot down more quickly to recover from errors in the direction of the current capture point dynamics, and a new algorithm to adjust the desired footstep, expanding the base of support to utilize the center of pressure (CoP)-based ankle strategy for balance. We then utilize the desired centroidal moment pivot (CMP) to calculate the momentum rate of change for our inverse-dynamics based whole-body controller. We present simulation and experimental results using this work, and discuss performance limitations and potential improvements.