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.