Neuromuscular-System-Based Tuning of a Haptic Shared Control Interface for UAV Teleoperation

Haptic guidance is a promising way to support unmanned aerial vehicle (UAV) operators, but the design of haptic guidance forces is often heuristic. This paper describes the design and experimental validation of a systematic neuromuscular analysis-based tuning procedure for haptic guidance, here applied to haptic collision avoidance system for UAV teleoperation. This tuning procedure is hypothesized to reduce operator workload as compared with current heuristic tuning methods. The proposed procedure takes into consideration the estimated mechanical response of the neuromuscular system (NMS) to haptic cues. A “relax-task” setting of the NMS, for which reflexive and muscular activation is minimal, is chosen as the design point for tuning the haptic support, as this setting is expected to yield minimal physical workload. The paper first presents a neuromuscular identification experiment, performed to estimate the “relax task” admittance of an operator's arm. The averaged admittance of a group of subjects (
$n=10$
) was then used for tuning the haptic shared controller, which was subsequently evaluated in its ability to support different operators (
$n=12$
) in a simulated unmanned aerial vehicle surveillance task. Results show that our novel tuning procedure indeed reduces operator workload and also improves situation awareness compared with haptic settings that ignore the NMS. In fact, it is shown that overtuning, which frequently occurs for these heuristically tuned systems, leads to even lower user acceptance scores than interfaces without any haptic support.