A Robust Biped Locomotion Based on Linear-Quadratic-Gaussian Controller and Divergent Component of Motion

Kasaei, Mohammadreza; Lau, Nuno; Pereira, Artur

doi:10.1109/iros40897.2019.8967778

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…The Online Planners here is based on the work in (Kasaei et al, 2019), here we describe briefly the technical essentials. As shown in Figure 2, Online Planners is composed of a set of sub-planners which are solved separately and connected together hierarchically to reduce the complexity of the planning process.…”

Section: Online Plannersmentioning

confidence: 99%

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Learning hybrid locomotion skills—Learn to exploit residual actions and modulate model-based gait control

et al. 2023

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This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking.

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Section: Online Plannersmentioning

confidence: 99%

“…This trajectory will be fed into PD Controllers to generate closed-loop locomotion. More detail can be found in our previous work (Kasaei et al, 2019).…”

Section: Online Plannersmentioning

confidence: 99%

Learning hybrid locomotion skills—Learn to exploit residual actions and modulate model-based gait control

et al. 2023

Self Cite

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“…Experimental results showed that the robot could walk in various environments by taking time-varying DCM into account. To decrease the reaction time, Kasaei et al [ 42 ] decoupled the control and replanning procedures. They formulated an offline optimal controller based on Linear–Quadratic–Gaussian (LQG) control [ 43 ] to achieve a reference trajectory-tracking process.…”

Section: Related Workmentioning

confidence: 99%

Robust Walking for Humanoid Robot Based on Divergent Component of Motion

Zhang

Shan

et al. 2022

Micromachines

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In order to perform various complex tasks in place of humans, humanoid robots should walk robustly in the presence of interference. In the paper, an improved model predictive control (MPC) method based on the divergent components of motion (DCM) is proposed. Firstly, the humanoid robot model is simplified to a finite-sized foot-pendulum model. Then, the gait of the humanoid robot in the single-support phase (SSP) and double-support phase (DSP) is planned based on DCM. The center of mass (CoM) of the robot will converge to the DCM, which simplifies the feedback control process. Finally, an MPC controller incorporating an extended Kalman filter (EKF) is proposed to realize the tracking of the desired DCM trajectory. By adjusting the step duration, the controller can compensate for CoM trajectory errors caused by disturbances. Simulation results show that—compared with the traditional method—the method we propose achieves improvements in both disturbed walking and uneven-terrain walking.

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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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A Robust Biped Locomotion Based on Linear-Quadratic-Gaussian Controller and Divergent Component of Motion

Cited by 8 publications

References 18 publications

Learning hybrid locomotion skills—Learn to exploit residual actions and modulate model-based gait control

Learning hybrid locomotion skills—Learn to exploit residual actions and modulate model-based gait control

Robust Walking for Humanoid Robot Based on Divergent Component of Motion

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

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