Ricerc@Sapienza

Walking gaits generated using Model Predictive Control (MPC) is widely used due to its capability to handle several constraints that characterize humanoid locomotion. The use of simplified models such as the Linear Inverted Pendulum allows to perform computations in real-time, giving the robot the fundamental capacity to replan its motion to follow external inputs (e.g. reference velocity, footstep plans). However, usually the MPC does not take into account the current state of the robot when computing the reference motion, losing the ability to react to external disturbances.

We introduce a robust gait generation framework for humanoid robots based on our Intrinsically Stable Model Predictive Control (IS-MPC) scheme, which features a stability constraint to guarantee internal stability. With respect to the original version, the new framework adds multiple components addressing the robustness problem from different angles: an observer-based disturbance compensation mechanism; a ZMP constraint restriction that provides robustness with respect to bounded disturbances; and a step timing adaptation module to prevent the loss of feasibility.

We consider a situation in which a humanoid robot must reach a goal region (walk-to task) walking in an environment consisting of horizontal patches located at different heights (world of stairs). To solve this problem, the paper proposes an integrated motion planner/controller working in two stages: off-line footstep planning and on-line gait generation. The planning stage is based on a randomized algorithm that efficiently searches for a feasible footstep sequence.