An Optimal State Dependent Haptic Guidance Controller via a Hard Rein

RANASINGHE, Anuradha, ALTHOEFER, Kaspar, NANAYAKKARA, Thrishantha, PENDERS, Jacques and DASGUPTA, Prokar (2013). An Optimal State Dependent Haptic Guidance Controller via a Hard Rein. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics. Institute of Electrical and Electronics Engineers, 2322-2327.

Preview

PDF Penders_an_optimal_state_dependent.pdf - Published Version All rights reserved. Download (1MB) | Preview

Abstract

The aim of this paper is to improve the optimality and accuracy of techniques to guide a human in limited visibility & auditory conditions such as in fire-fighting in warehouses or similar environments. At present, teams of 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 a 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.

Item Type:	Book Section
Additional Information:	Conference held 13-16 October 2013 Manchester, United Kingdom
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Materials and Engineering Research Institute > Modelling Research Centre > Microsystems and Machine Vision Laboratory
Identification Number:	https://doi.org/10.1109/SMC.2013.397
Page Range:	2322-2327
Depositing User:	Helen Garner
Date Deposited:	18 Sep 2014 09:59
Last Modified:	18 Mar 2021 13:34
URI:	https://shura.shu.ac.uk/id/eprint/8455

Actions (login required)

View Item

Downloads

Publication Metrics