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Motion generation for walking exoskeleton robot using multiple dynamic movement primitives sequences combined with reinforcement learning

Published online by Cambridge University Press:  07 January 2022

Peng Zhang  and
Junxia Zhang Open the ORCID record for Junxia Zhang [Opens in a new window]
Peng Zhang
Affiliation:
Tianjin University of Science and Technology, Dagunan Road, Tianjin, China Tianjin Key Laboratory for Integrated Design and Online Monitor Center of Light Design and Food Engineering Machinery Equipment, Tianjin, China
Junxia Zhang*
Affiliation:
Tianjin University of Science and Technology, Dagunan Road, Tianjin, China Tianjin Key Laboratory for Integrated Design and Online Monitor Center of Light Design and Food Engineering Machinery Equipment, Tianjin, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

In order to assist patients with lower limb disabilities in normal walking, a new trajectory learning scheme of limb exoskeleton robot based on dynamic movement primitives (DMP) combined with reinforcement learning (RL) was proposed. The developed exoskeleton robot has six degrees of freedom (DOFs). The hip and knee of each artificial leg can provide two electric-powered DOFs for flexion/extension. And two passive-installed DOFs of the ankle were used to achieve the motion of inversion/eversion and plantarflexion/dorsiflexion. The five-point segmented gait planning strategy is proposed to generate gait trajectories. The gait Zero Moment Point stability margin is used as a parameter to construct a stability criteria to ensure the stability of human-exoskeleton system. Based on the segmented gait trajectory planning formation strategy, the multiple-DMP sequences were proposed to model the generation trajectories. Meanwhile, in order to eliminate the effect of uncertainties in joint space, the RL was adopted to learn the trajectories. The experiment demonstrated that the proposed scheme can effectively remove interferences and uncertainties.

Keywords

dynamic movement primitiveswalking exoskeleton robotgait planning strategyreinforcement learning
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2732 - 2747
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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