ROJan 7, 2018

Push recovery with stepping strategy based on time-projection control

arXiv:1801.02151v12 citations
Originality Incremental advance
AI Analysis

This work addresses robust locomotion for bipedal robots, particularly in handling perturbations, but it is incremental as it builds on existing control methods like LQR and 3LP models.

The paper tackles push recovery in bipedal robots by developing a control framework that uses a time-projection method to adjust footstep locations online, based on a 3LP linear model, and demonstrates effectiveness in extreme push recovery scenarios and emergent walking gaits under continuous dragging forces.

In this paper, we present a simple control framework for on-line push recovery with dynamic stepping properties. Due to relatively heavy legs in our robot, we need to take swing dynamics into account and thus use a linear model called 3LP which is composed of three pendulums to simulate swing and torso dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use a particular time-projection method to adjust the next footstep location on-line during the motion continuously. This adjustment, which is found based on both pelvis and swing foot tracking errors, naturally takes the swing dynamics into account. Suggested adjustments are added to the Cartesian 3LP gaits and converted to joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies also ensure enough ground clearance in perturbed walking conditions. The proposed structure is robust, yet uses very simple state estimation and basic position tracking. We rely on the physical series elastic actuators to absorb impacts while introducing simple laws to compensate their tracking bias. Extensive experiments demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness due to relatively soft springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our proposed architecture.

Code Implementations1 repo
Foundations

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