基于肌电和眼动信号的简化操纵 eVTOL 操纵品质评估

With the advent of commercial transportation in Urban Air Mobility（UAM），the concept of Simplified Vehicle Operations（SVO）has been integrated into aircraft design，aiming to streamline operational procedures to meet future transport demands. However，there is uncertainty regarding whether electric Vertical Take-Off and Landing（eVTOL）aircraft，which are designed based on SVO，meet airworthiness criteria and whether their control interfaces adhere to ergonomic standards for user-friendliness. To address this issue，an experiment on Mission Task Elements （MTE）was conducted to assess the handling qualities of SVO-based eVTOL aircraft. 20 participants were recruited for the experiment，during which their subjective ratings of handling qualities using Cooper-Harper Rating Scale，qualitative comments through semi-structured interviews，and electromyography（EMG）data and eye-tracking data were recorded. Additionally，a handling qualities assessment model based on Gramian Angular Field （GAF） and 2Ｄ-Convolutional Neural Networks（2Ｄ-CNN）was proposed. The results indicate that poor control interface design significantly affected the participants’EMG and eye-tracking signals. Benefiting from the spatio-temporal information provided by GAF images，the proposed 2Ｄ-CNN achieved an accuracy of 93. 6% in predicting eVTOL handing qualities levels. This study provides a new perspective for the objective assessment of eVTOL handling qualities and offers significant guidance for the future design of SVO.