We present a real-time algorithm for humanoid 3D walking and/or running based on a Model Predictive Control (MPC) approach. The objective is to generate a stable gait that replicates as closely as possible a footstep plan, i.e., a sequence of candidate footstep positions and orientations with associated timings. For each footstep, the plan also specifies an associated reference height for the Center of Mass (CoM) and whether the robot should reach the footstep by walking or running. The scheme makes use of the Variable-Height Inverted Pendulum (VH-IP) as prediction model, generating in real-time both a CoM trajectory and adapted footsteps. The VH-IP model relates the position of the CoM to that of the Zero Moment Point (ZMP); to avoid falling, the ZMP must be inside a properly defined support region (a 3D extension of the 2D support polygon) whenever the robot is in contact with the ground. The nonlinearity of the VH-IP is handled by splitting the gait generation in two consecutive stages, both requiring to solve a quadratic program. Thanks to a particular triangular structure of the VH-IP dynamics, the first stage deals with the vertical dynamics using the Ground Reaction Force (GRF) as decision variable. Using the prediction given by the first stage, the horizontal dynamics becomes linear time-varying. During flight phases the VH-IP collapses to a free-falling mass model. The proposed formulation incorporates constraints in order to maintain physically meaningful values of the GRF, keep the ZMP in the support region during contact phases, and ensure that the adapted footsteps are kinematically realizable. Most importantly, a stability constraint is enforced on the time-varying horizontal dynamics to guarantee a bounded evolution of the CoM with respect to the ZMP. Furthermore, we show how to extend the technique in order to perform running on tilted surfaces. We also describe a simple technique that receives in input high-level velocity commands and generates a footstep plan in the form required by the proposed MPC scheme. The algorithm is validated via dynamic simulations on the full-scale humanoid robot HRP-4, as well as experiments on the small-sized robot OP3.

From Walking to Running: 3D Humanoid Gait Generation via MPC / Smaldone, FILIPPO MARIA; Scianca, Nicola; Lanari, Leonardo; Oriolo, Giuseppe. - In: FRONTIERS IN ROBOTICS AND AI. - ISSN 2296-9144. - 9:(2022). [10.3389/frobt.2022.876613]

From Walking to Running: 3D Humanoid Gait Generation via MPC

Filippo Maria Smaldone;Nicola Scianca
;
Leonardo Lanari;Giuseppe Oriolo
2022

Abstract

We present a real-time algorithm for humanoid 3D walking and/or running based on a Model Predictive Control (MPC) approach. The objective is to generate a stable gait that replicates as closely as possible a footstep plan, i.e., a sequence of candidate footstep positions and orientations with associated timings. For each footstep, the plan also specifies an associated reference height for the Center of Mass (CoM) and whether the robot should reach the footstep by walking or running. The scheme makes use of the Variable-Height Inverted Pendulum (VH-IP) as prediction model, generating in real-time both a CoM trajectory and adapted footsteps. The VH-IP model relates the position of the CoM to that of the Zero Moment Point (ZMP); to avoid falling, the ZMP must be inside a properly defined support region (a 3D extension of the 2D support polygon) whenever the robot is in contact with the ground. The nonlinearity of the VH-IP is handled by splitting the gait generation in two consecutive stages, both requiring to solve a quadratic program. Thanks to a particular triangular structure of the VH-IP dynamics, the first stage deals with the vertical dynamics using the Ground Reaction Force (GRF) as decision variable. Using the prediction given by the first stage, the horizontal dynamics becomes linear time-varying. During flight phases the VH-IP collapses to a free-falling mass model. The proposed formulation incorporates constraints in order to maintain physically meaningful values of the GRF, keep the ZMP in the support region during contact phases, and ensure that the adapted footsteps are kinematically realizable. Most importantly, a stability constraint is enforced on the time-varying horizontal dynamics to guarantee a bounded evolution of the CoM with respect to the ZMP. Furthermore, we show how to extend the technique in order to perform running on tilted surfaces. We also describe a simple technique that receives in input high-level velocity commands and generates a footstep plan in the form required by the proposed MPC scheme. The algorithm is validated via dynamic simulations on the full-scale humanoid robot HRP-4, as well as experiments on the small-sized robot OP3.
2022
humanoid robot; model predictive control; legged locomotion; running; variable-height inverted pendulum model
01 Pubblicazione su rivista::01a Articolo in rivista
From Walking to Running: 3D Humanoid Gait Generation via MPC / Smaldone, FILIPPO MARIA; Scianca, Nicola; Lanari, Leonardo; Oriolo, Giuseppe. - In: FRONTIERS IN ROBOTICS AND AI. - ISSN 2296-9144. - 9:(2022). [10.3389/frobt.2022.876613]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1640208
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