Haptic Guidance on Demand: A Grip-Force Based Scheduling of Guidance Forces

Abstract

In haptic shared control systems (HSC), a fixed strength of guidance force equates to a fixed level of control authority, which can be insufficient for complex tasks. An adaptable control authority based on operator input can allow the HSC system to better assist the operator under varied conditions. In this paper, we experimentally investigate () an adaptable authority HSC system that provides the operator with a direct way to adjust the control authority based on applied grip force. This system can serve as an intuitive 'manual override' function in case of HSC system malfunction. In a position tracking task, we explore two opposite approaches to adapt the control authority: increasing versus decreasing guidance strength with operator grip. These approaches were compared with unassisted control and two levels of fixed-level haptic guidance. Results show that the grip-adaptable approach allowed the operators to increase performance over unassisted control and over a weak guidance. At the same time, the approach substantially reduced the operator physical control effort required to cope with HSC system disturbances. Predictions based on the formalized model of the complete human-in-the-loop system corresponded to the experimental results, implying that such validated formalization can be used for model-based analysis and design of guidance systems.

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior

“…Previous research has suggested a relationship between arm admittance and grip strength on the steering wheel [26] as measured by sEMG signals from forearm muscles [27]. Inspired by research on the gripforce-based scheduling of guidance forces [28], our previous study adjusted the authority of shared steering control according to driver grip strength on the steering wheel to achieve better driver-automation cooperation performance [29]. We found a reduction in both lane departure risk and driver workload to be associated with sEMG-based adaptive authority compared to fixed authority [29].…”