Implementing an Adaptive Haptic Shared Controller in Pursuit and Preview Tracking Tasks

Abstract

Haptic shared controllers (HSCs) are a promising solution to prevent human over-reliance on automation during tasks such as car driving. However, research has shown that if the HSC is tuned incorrectly, then there is a risk of haptic conflicts between the human and HSC. To address this challenge, this paper presents the design and implementation of a novel adaptive HSC that continuously adjusts its look-ahead time. By estimating the time shift between the reference state of the HSC and the actual state, the HSC adapts to the look-ahead time of the human it is interacting with. Results from a human-in- the-loop experiment show that the novel HSC achieves similar subjective ratings as a fixed preview HSC, as well as a significant improvement over a fixed pursuit HSC. Going from pursuit to preview, objective experiment data shows that as the adaptive HSC adjusts its look-ahead time, haptic conflicts are reduced and tracking performance is increased. The presented findings are a step forward in designing haptic support systems with high chances of user acceptance. The proposed adaptive look-ahead algorithm provides a new method for online estimation of human look-ahead time, with or without a HSC in-the-loop.