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A novel method for slip prediction of walking biped robots

Published online by Cambridge University Press:  03 November 2015

Iyad Hashlamon,
Mehmet Mert Gülhan,
Orhan Ayit and
Kemalettin Erbatur
Iyad Hashlamon*
Affiliation:
Palestine Polytechnic University, Hebron, Palestine
Mehmet Mert Gülhan
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey. E-mails: mertgulhan@sabanciuniv.edu, orhanayit@sabanciuniv.edu, erbatur@sabanciuniv.edu
Orhan Ayit
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey. E-mails: mertgulhan@sabanciuniv.edu, orhanayit@sabanciuniv.edu, erbatur@sabanciuniv.edu
Kemalettin Erbatur
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey. E-mails: mertgulhan@sabanciuniv.edu, orhanayit@sabanciuniv.edu, erbatur@sabanciuniv.edu
*
*Corresponding author. E-mail: iyad@ppu.edu, hashlamon@sabanciuniv.edu
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Summary

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This paper proposes a new approach for slip prediction of walking biped robots. The slip prediction is a measurement-based and friction behavior-inspired approach. A measurement-based online algorithm is designed to estimate the Coulomb friction which is regarded as a slip threshold. To predict the slip, a safety margin is introduced in the negative vicinity of the estimated Coulomb friction. The estimation algorithm concludes that if the applied force is outside the safety margin, then the foot tends to slip. The proposed approach depends on the available type of measurements. Three options of measurements are discussed. Among them, the foot acceleration and ankle force measurements scenario is validated by experiments on the humanoid SURALP (Sabanci University Robotics Research Laboratory Platform). The results demonstrate the effectiveness of the proposed approach for slip prediction and detection.

Keywords

BipedSlip predictionSlip detectionFrictionLIPM and CoM states estimation

Information

Type
Articles
Information
Robotica , Volume 35 , Issue 4 , April 2017, pp. 766 - 786
Copyright
Copyright © Cambridge University Press 2015 

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