Humanoid gait generation for walk-to locomotion using single-stage MPC

Aboudonia, Ahmed; Scianca, Nicola; Simone, Daniele De; Lanari, Leonardo; Oriolo, Giuseppe

doi:10.1109/humanoids.2017.8239554

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…Expressing this constraint in double support would involve products of decision variables, thus resulting in a nonlinear constraint. In order to preserve linearity, we employ a moving constraint ( Aboudonia et al, 2017 ): the ZMP must belong to a fixed-shape region (the footprint) which slides between consecutive footsteps during double support and coincides with a footstep during single support. In other words, this region is a horizontal slice of the prism, see Figure 1 .…”

Section: Is-mpc For 3d Walking and Runningmentioning

confidence: 99%

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

et al. 2022

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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 a footstep plan as closely as possible, that is, 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 a 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 into 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 a decision variable. Using the prediction given by the first stage, the horizontal dynamics become linear time-varying. During the 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 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.

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Section: Is-mpc For 3d Walking and Runningmentioning

confidence: 99%

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

et al. 2022

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“…During double support, the support polygon would be the convex hull of the two footsteps, whose boundary is a nonlinear function of their relative position. To preserve linearity, we adopt an approach based on moving constraints [30]. In particular, the admissible region for the ZMP in double support has exactly the same shape and dimensions it has in single support, and it roto-translates (i.e., simultaneously rotates and translates) from one footstep to the other in such a way to always remain in the support polygon (see Fig.…”

Section: B Zmp Constraintsmentioning

confidence: 99%

MPC for Humanoid Gait Generation: Stability and Feasibility

et al. 2020

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We present IS-MPC, an intrinsically stable MPC framework for humanoid gait generation which incorporates an explicit stability constraint in the formulation. The proposed method uses as prediction model a dynamically extended LIP where ZMP velocities are the control inputs, producing in real time a gait (including footsteps with the associated timing) that realizes omnidirectional motion commands coming from an external source. The stability constraint links the future ZMP velocities to the current system state so as to guarantee the essential requirement that the generated CoM trajectory is bounded with respect to the ZMP trajectory. Since the control horizon of the MPC algorithm is finite, only part of the future ZMP velocities are decision variables of the QP problem; the remaining part, called tail, must be either conjectured or anticipated using preview information on the reference motion. Several possible options for the tail are discussed, and each of them is shown to correspond to a specific terminal constraint. A theoretical analysis of the feasibility of the generic MPC iteration is developed and used to obtain sufficient conditions for recursive feasibility. Finally, it is proved that IS-MPC guarantees stability of the CoM/ZMP dynamics if it is recursively feasible. Simulation and experimental results on the NAO and the HRP-4 humanoids are presented to illustrate the performance of the proposed method.

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“…These impose that the ZMP lies at all times within the support polygon of the robot. To avoid nonlinearities, during double support phases the latter is approximated by a moving constraint (Aboudonia et al 2017). • Kinematic constraints.…”

Section: Is-mpc For the Sagittal Motion Ismentioning

confidence: 99%

A behavior-based framework for safe deployment of humanoid robots

et al. 2021

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We present a complete framework for the safe deployment of humanoid robots in environments containing humans. Proceeding from some general guidelines, we propose several safety behaviors, classified in three categories, i.e., override, temporary override, and proactive. Activation and deactivation of these behaviors is triggered by information coming from the robot sensors and is handled by a state machine. The implementation of our safety framework is discussed with respect to a reference control architecture. In particular, it is shown that an MPC-based gait generator is ideal for realizing all behaviors related to locomotion. Simulation and experimental results on the HRP-4 and NAO humanoids, respectively, are presented to confirm the effectiveness of the proposed method.

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Humanoid gait generation for walk-to locomotion using single-stage MPC

Cited by 9 publications

References 13 publications

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

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

MPC for Humanoid Gait Generation: Stability and Feasibility

A behavior-based framework for safe deployment of humanoid robots

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