A Whole-Body Model Predictive Control Scheme Including External Contact Forces and CoM Height Variations

Mirjalili, Reihaneh; Yousefi-Korna, Aghil; Shirazi, Farzad A.; Nikkhah, Arman; Nazemi, Fatemeh; Khadiv, Majid

doi:10.1109/humanoids.2018.8624963

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…However this work did not consider the effect that variable CoM height has on the Zero Moment Point (ZMP) dynamics. The same problem also existed in [17]. Englsberger et al [18] generalized the 3D divergent component of motion to solve the height motion.…”

Section: Introductionmentioning

confidence: 89%

Nonlinear Model Predictive Control for Robust Bipedal Locomotion: Exploring Angular Momentum and CoM Height Changes

Ding¹,

Zhou²,

Xin³

et al. 2019

Preprint

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Human beings can make use of various reactive strategies, e.g. foot location adjustment and upper-body inclination, to keep balance while walking under dynamic disturbances. In this work, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for versatile bipedal gait pattern generation, with the capabilities of footstep adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. These features are realized by constraining the Zero Moment Point motion with considering the variable CoM height and angular momentum change of the Inverted Pendulum plus Flywheel Model. In addition, the NMPC framework also takes into account the constraints of footstep location, CoM vertical motion, upper-body inclination and joint torques, and is finally formulated as a quadratically constrained quadratic program. Therefore, it can be solved efficiently by Sequential Quadratic Programming. Using this unified framework, versatile walking pattern with exploiting time-varying CoM height trajectory and angular momentum changes can be generated based only on the terrain information input. Furthermore, the improved capability for balance recovery under external pushes has been demonstrated through simulation studies.

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Section: Introductionmentioning

confidence: 89%

Nonlinear Model Predictive Control for Robust Bipedal Locomotion: Exploring Angular Momentum and CoM Height Changes

Ding¹,

Zhou²,

Xin³

et al. 2019

Preprint

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“…In this model, the single mass is assumed to move along a horizontal plane and based on this assumption, the motion equations in sagittal and frontal planes are decoupled and independent. Several studies used this model to develop an online walking generator based on optimal control [17] and also linear MPC [3,10,22]. Several extensions of LIPM have been proposed to increase the accuracy of this model while keeping the simplicity level [7,18,21,27].…”

Section: Dynamics Modelsmentioning

confidence: 99%

A modular framework to generate robust biped locomotion: from planning to control

et al. 2021

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Biped robots are inherently unstable because of their complex kinematics as well as dynamics. Despite many research efforts in developing biped locomotion, the performance of biped locomotion is still far from the expectations. This paper proposes a model-based framework to generate stable biped locomotion. The core of this framework is an abstract dynamics model which is composed of three masses to consider the dynamics of stance leg, torso, and swing leg for minimizing the tracking problems. According to this dynamics model, we propose a modular walking reference trajectories planner which takes into account obstacles to plan all the references. Moreover, this dynamics model is used to formulate the controller as a Model Predictive Control (MPC) scheme which can consider some constraints in the states of the system, inputs, outputs, and also mixed input-output. The performance and the robustness of the proposed framework are validated by performing several numerical simulations using MATLAB. Moreover, the framework is deployed on a simulated torque-controlled humanoid to verify its performance and robustness. The simulation results show that the proposed framework is capable of generating biped locomotion robustly.

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“…(3) and (4). Initially, a trajectory optimization problem is solved for the dynamics governing the z direction independently, such that the height of the inverted pendulum moves from its current height to the desired height by the end of the trajectory (duration of one step) while also reaching a zero velocity [21], [22]. The QP problem solved for the z direction trajectory is min .…”

Section: B Trajectory Generation and Control Approachmentioning

confidence: 99%

DeepQ Stepper: A framework for reactive dynamic walking on uneven terrain

Meduri¹,

Khadiv²,

Righetti³

2020

Preprint

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Reactive stepping and push recovery for biped robots is often restricted to flat terrains because of the difficulty in computing capture regions for nonlinear dynamic models. In this paper, we address this limitation by using reinforcement learning to approximately learn the 3D capture region for such systems. We propose a novel 3D reactive stepper, The DeepQ stepper, that computes optimal step locations for walking at different velocities using the 3D capture regions approximated by the action-value function. We demonstrate the ability of the approach to learn stepping with a simplified 3D pendulum model and a full robot dynamics. Further, the stepper achieves a higher performance when it learns approximate capture regions while taking into account the entire dynamics of the robot that are often ignored in existing reactive steppers based on simplified models. The DeepQ stepper can handle non convex terrain with obstacles, walk on restricted surfaces like stepping stones and recover from external disturbances for a constant computational cost.

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A Whole-Body Model Predictive Control Scheme Including External Contact Forces and CoM Height Variations

Cited by 11 publications

References 21 publications

Nonlinear Model Predictive Control for Robust Bipedal Locomotion: Exploring Angular Momentum and CoM Height Changes

Nonlinear Model Predictive Control for Robust Bipedal Locomotion: Exploring Angular Momentum and CoM Height Changes

A modular framework to generate robust biped locomotion: from planning to control

DeepQ Stepper: A framework for reactive dynamic walking on uneven terrain

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