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Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to) 568 - 579
Number of pages12
JournalIEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics
Volume46
Issue number2
Early online date11 Jun 2015
DOIs
Publication statusPublished - Feb 2016

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  • ReviewToBeSubmittedFinalVer

    ReviewToBeSubmittedFinalVer.pdf, 2.78 MB, application/pdf

    21/07/2015

    Accepted author manuscript

    (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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Abstract

This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and the follower such as a) the order of an autoregressive control policy that maps states of the follower to actions of the guider, b) how the guider may modulate the pulling force in response to the confidence level of the follower, and c) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a $3^{\rm rd}$ order auto-regressive predictive control policy and followers tend to adopt $2^{\rm nd}$ order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the confidence level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the confidence level of the follower monitored using a time varying virtual damped inertial model.

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