Enhancing Human Navigation Ability Using Force-Feedback From a Lower-Limb Exoskeleton

Abstract

Humans operating in dynamic environments with limited visibility are susceptible to collisions with moving objects, occupational hazards, and/or other agents, which can result in personal injuries or fatalities. Most existing research has focused on using vibrotactile cues to address this challenge. In this work, we propose a fundamentally new approach that utilizes variable impedance on an active exoskeleton to guide humans away from hazards and towards safe areas. This framework combines artificial potential fields with current impedance-based theories of exoskeleton control to provide a comprehensive navigational system that is intuitive for human operators. First, we present the mathematical framework to encode information about the locations of obstacles and the safest direction in which to move. Next, we optimize controller parameters in a series of human-subject experiments. Finally, we evaluate the framework in virtual reality on a set of randomly generated obstacle fields in environments where vision is either fully or partially occluded. Our results suggest that the exoskeleton provides significant separation from obstacles and reduced collisions compared to vision alone in conditions where visibility was limited to less than 1.3 m. Our work demonstrates that force-feedback in parallel with a human can improve overall navigation ability in low visibility conditions.