Cui, D, Peers, C, Wang, G et al. (3 more authors) (2021) Human inspired fall arrest strategy for humanoid robots based on stiffness ellipsoid optimisation. Bioinspiration & Biomimetics. ISSN 1748-3182
Abstract
Falls are a common risk and impose severe threats to both humans and humanoid robots as a product of bipedal locomotion. Inspired by human fall arrest, we present a novel humanoid robot fall prevention strategy by using arms to make contact with environmental objects. Firstly, the capture point (CP) method is used to detect falling. Once the fall is inevitable, the arm of the robot will be actuated to gain contact with an environmental object to prevent falling. We propose a hypothesis that humans naturally favour to select a pose that can generate a suitable Cartesian stiffness of the arm end-effector. Based on this principle, a configuration optimiser is designed to choose a pose of the arm that maximises the value of the stiffness ellipsoid of the endpoint along the impact force direction. During contact, the upper limb acts as an adjustable active spring-damper and absorbs impact shock to steady itself. To validate the proposed strategy, several simulations are performed in MATLAB & Simulink by having the humanoid robot confront a wall as a case study in which the strategy is proved to be effective and feasible. The results show that using the optimised posture can reduce the joint torque during impact when the arms are used to arrest the fall.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Aug 2021 10:15 |
Last Modified: | 10 Aug 2021 10:15 |
Status: | Published online |
Publisher: | IOP Publishing |
Identification Number: | 10.1088/1748-3190/ac1ab9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176842 |