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An optimal state dependent haptic guidance controller via a hard rein

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An optimal state dependent haptic guidance controller via a hard rein. / Ranasinghe, Anuradha; Althoefer, Kaspar; Nanayakkara, Thrishantha; Penders, Jacques; Dasgupta, Prokar.

Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. IEEE, 2013. p. 2322-2327 6722150.

Research output: Chapter in Book/Report/Conference proceedingConference paper

Harvard

Ranasinghe, A, Althoefer, K, Nanayakkara, T, Penders, J & Dasgupta, P 2013, An optimal state dependent haptic guidance controller via a hard rein. in Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013., 6722150, IEEE, pp. 2322-2327, 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, Manchester, United Kingdom, 13/10/2013. https://doi.org/10.1109/SMC.2013.397

APA

Ranasinghe, A., Althoefer, K., Nanayakkara, T., Penders, J., & Dasgupta, P. (2013). An optimal state dependent haptic guidance controller via a hard rein. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 (pp. 2322-2327). [6722150] IEEE. https://doi.org/10.1109/SMC.2013.397

Vancouver

Ranasinghe A, Althoefer K, Nanayakkara T, Penders J, Dasgupta P. An optimal state dependent haptic guidance controller via a hard rein. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. IEEE. 2013. p. 2322-2327. 6722150 https://doi.org/10.1109/SMC.2013.397

Author

Ranasinghe, Anuradha ; Althoefer, Kaspar ; Nanayakkara, Thrishantha ; Penders, Jacques ; Dasgupta, Prokar. / An optimal state dependent haptic guidance controller via a hard rein. Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. IEEE, 2013. pp. 2322-2327

Bibtex Download

@inbook{d47a700ac4994140a3bb69ff1c500474,
title = "An optimal state dependent haptic guidance controller via a hard rein",
abstract = "The aim of this paper is to improve the optimality and accuracy of techniques to guide a human in limited visibility and auditory conditions such as in fire-fighting in warehouses or similar environments. At present, breathing apparatus (BA) wearing fire-fighters move in teams following walls. Due to limited visibility and high noise in the oxygen masks, they predominantly depend on haptic communication through reins. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments, just like a dog guiding a blind person. This paper proposes an optimal state-dependent control policy to guide a follower with limited environmental perception, by an intelligent and environmentally perceptive agent. Based on experimental systems identification and numerical simulations on human demonstrations from eight pairs of participants, we show that the guiding agent and the follower experience learning for a optimal stable state-dependent novel 3rd and 2nd order auto regressive predictive and reactive control policies respectively. Our findings provide a novel theoretical basis to design advanced human-robot interaction algorithms in a variety of cases that require the assistance of a robot to perceive the environment by a human counterpart.",
keywords = "Haptic, Human robot interaction (hri), Optimal control policy, Predictive and reactive controllers",
author = "Anuradha Ranasinghe and Kaspar Althoefer and Thrishantha Nanayakkara and Jacques Penders and Prokar Dasgupta",
year = "2013",
month = "10",
doi = "10.1109/SMC.2013.397",
language = "English",
isbn = "9780769551548",
pages = "2322--2327",
booktitle = "Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - An optimal state dependent haptic guidance controller via a hard rein

AU - Ranasinghe, Anuradha

AU - Althoefer, Kaspar

AU - Nanayakkara, Thrishantha

AU - Penders, Jacques

AU - Dasgupta, Prokar

PY - 2013/10

Y1 - 2013/10

N2 - The aim of this paper is to improve the optimality and accuracy of techniques to guide a human in limited visibility and auditory conditions such as in fire-fighting in warehouses or similar environments. At present, breathing apparatus (BA) wearing fire-fighters move in teams following walls. Due to limited visibility and high noise in the oxygen masks, they predominantly depend on haptic communication through reins. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments, just like a dog guiding a blind person. This paper proposes an optimal state-dependent control policy to guide a follower with limited environmental perception, by an intelligent and environmentally perceptive agent. Based on experimental systems identification and numerical simulations on human demonstrations from eight pairs of participants, we show that the guiding agent and the follower experience learning for a optimal stable state-dependent novel 3rd and 2nd order auto regressive predictive and reactive control policies respectively. Our findings provide a novel theoretical basis to design advanced human-robot interaction algorithms in a variety of cases that require the assistance of a robot to perceive the environment by a human counterpart.

AB - The aim of this paper is to improve the optimality and accuracy of techniques to guide a human in limited visibility and auditory conditions such as in fire-fighting in warehouses or similar environments. At present, breathing apparatus (BA) wearing fire-fighters move in teams following walls. Due to limited visibility and high noise in the oxygen masks, they predominantly depend on haptic communication through reins. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments, just like a dog guiding a blind person. This paper proposes an optimal state-dependent control policy to guide a follower with limited environmental perception, by an intelligent and environmentally perceptive agent. Based on experimental systems identification and numerical simulations on human demonstrations from eight pairs of participants, we show that the guiding agent and the follower experience learning for a optimal stable state-dependent novel 3rd and 2nd order auto regressive predictive and reactive control policies respectively. Our findings provide a novel theoretical basis to design advanced human-robot interaction algorithms in a variety of cases that require the assistance of a robot to perceive the environment by a human counterpart.

KW - Haptic

KW - Human robot interaction (hri)

KW - Optimal control policy

KW - Predictive and reactive controllers

UR - http://www.scopus.com/inward/record.url?scp=84893520385&partnerID=8YFLogxK

U2 - 10.1109/SMC.2013.397

DO - 10.1109/SMC.2013.397

M3 - Conference paper

AN - SCOPUS:84893520385

SN - 9780769551548

SP - 2322

EP - 2327

BT - Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

PB - IEEE

ER -

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