Articles

Handling qualities assessing of SVO-based eVTOL aircraft through EMG and eye data

  • Yuhan LI ,
  • Shuguang ZHANG ,
  • Yibing WU
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  • 1.School of Aeronautic Science and Engineering,Beihang University,Beijing 100191,China
    2.School of Transportation Science and Engineering,Beihang University,Beijing 100191,China
E-mail: gnahz@buaa.edu.cn

Received date: 2024-09-30

  Revised date: 2024-10-13

  Accepted date: 2024-11-11

  Online published: 2024-11-14

Supported by

National Natural Science Foundation of China(52472353)

Abstract

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.

Cite this article

Yuhan LI , Shuguang ZHANG , Yibing WU . Handling qualities assessing of SVO-based eVTOL aircraft through EMG and eye data[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(11) : 531315 -531315 . DOI: 10.7527/S1000-6893.2024.31315

References

[1] 邓景辉. 电动垂直起降飞行器的技术现状与发展[J]. 航空学报202445(5): 529937.
  DENG J H. Technical status and development of electric vertical take-off and landing aircraft[J]. Acta Aeronautica et Astronautica Sinica202445(5): 529937 (in Chinese).
[2] FURUZAWA J C, FREGNANI J A T G. Simplified Vehicle Operations (SVO)?[C]?∥Proceeding of World Aviation Training Summit. Orlando: Halldale Group, 2022.
[3] SKYRYSE. Robinson R66 vs. Skyryse One helicopter-what’s the difference?[EB/OL]. (2024-8-29)[2024-09-25]. .
[4] LOMBAERTS T, KANESHIGE J, FEARY M. Control concepts for simplified vehicle operations of a quadrotor eVTOL vehicle?[C]?∥AIAA Aviation 2020 Forum. Reston: AIAA, 2020: 3189.
[5] CHAKRABORTY I, COMER A, BHANDARI R, et al. Piloted simulation-based assessment of simplified vehicle operations for urban air mobility?[J]. Journal of Aerospace Information Systems202421(5): 392-421.
[6] FEARY M. Humans, autonomy, and eVTOLs[C]∥Association for Unmanned Vehicle Systems International Conference (AUVSI 2018): From VTOL to eVTOL: Technical Solutions. Arlington: Association for Unmanned Vehicle Systems International (AUVSI), 2018.
[7] European Union Aviation Safety Agency. Special condition for VTOL and means of compliance?[R]. Cologne: EASA, 2024.
[8] GARCíA G D, SEIFERTH D, MEIDINGER V, et al. Conduction of mission task elements within simulator flight tests for handling quality evaluation of an eVTOL aircraft?[C]?∥AIAA Scitech 2021 Forum. Reston: AIAA, 2021: 1897.
[9] 冯传宴, 完颜笑如, 刘双, 等. 负荷条件下注意力分配策略对情境意识的影响[J]. 航空学报202041(3): 123307.
  FENG C Y, WANYAN X R, LIU S, et al. Influence of different attention allocation strategies under workloads on situation awareness[J]. Acta Aeronautica et Astronautica Sinica202041(3): 123307 (in Chinese).
[10] KIM Y W, JI Y G. Designing for trust: How human-machine interface can shape the future of urban air mobility[J]. International Journal of Human-Computer Interaction202541(2): 1190-1203.
[11] WILSON G F. An analysis of mental workload in pilots during flight using multiple psychophysiological measures[J]. The International Journal of Aviation Psychology200212(1): 3-18.
[12] 王崴, 赵敏睿, 高虹霓, 等. 基于脑电和眼动信号的人机交互意图识别[J]. 航空学报202142(2): 324290.
  WANG W, ZHAO M R, GAO H N, et al. Human-computer interaction: Intention recognition based on EEG and eye tracking[J]. Acta Aeronautica et Astronautica Sinica202142(2): 324290 (in Chinese).
[13] 马菲, 张琼, 赖培军, 等. 基于BP神经网络的试飞训练安全性量化模型[J]. 航空学报202445(5): 529957.
  MA F, ZHANG Q, LAI P J, et al. BP neural network-based quantitative classification model for safety in experimental flight training?[J]. Acta Aeronautica et Astronautica Sinica202445(5): 529957 (in Chinese).
[14] LI Y H, LI K, CHEN J A, et al. Pilot stress detection through physiological signals using a transformer-based deep learning model[J]. IEEE Sensors Journal202323(11): 11774-11784.
[15] LEE D H, JEONG J H, KIM K, et al. Continuous EEG decoding of pilots’? mental states using multiple feature block-based convolutional neural network[J]. IEEE Access20208: 121929-121941.
[16] LI H, WANG X P, LIU C C, et al. Integrating multi-domain deep features of electrocardiogram and phonocardiogram for coronary artery disease detection?[J]. Computers in Biology and Medicine2021138: 104914.
[17] BELLO-CEREZO R, BIANCONI F, DI MARIA F, et al. Comparative evaluation of hand-crafted image descriptors vs. off-the-shelf CNN-based features for colour texture classification under ideal and realistic conditions[J]. Applied Sciences20199(4): 738.
[18] LI Y H, ZHANG S G, HE R C, et al. Objective detection of trust in automated urban air mobility: A deep learning-based ERP analysis?[J]. Aerospace202411(3): 174.
[19] DOLLINGER D, REISS P, ANGELOV J, et al. Control inceptor design for onboard piloted transition VTOL aircraft considering simplified vehicle operation?[C]?∥AIAA Scitech 2021 Forum. Reston: AIAA, 2021: 1896.
[20] DOLLINGER D, FRICKE T, HOLZAPFEL F. Control inceptor design for remote control of a transition UAV?[C]?∥AIAA Aviation 2019 Forum. Reston: AIAA, 2019: 3268.
[21] ZINTL M, MARB M M, WECHNER M A, et al. Development of a virtual reality simulator for eVTOL flight testing?[C]?∥AIAA Aviation 2022 Forum. Reston: AIAA, 2022: 3941.
[22] KLYDE D H, SCHULZE P C, MITCHELL D G, et al. Mission task element development process: An approach to FAA handling qualities certification?[C]?∥AIAA Aviation 2020 Forum. Reston: AIAA, 2020: 3285.
[23] WECHNER M A, MARB M M, ZINTL M, et al. Design, conduction and evaluation of piloted simulation mission task element tests for desired behavior validation of an eVTOL flight control system?[C]?∥AIAA Aviation 2022 Forum. Reston: AIAA, 2022: 3790.
[24] HERATH N, HERATH M, THILAKANAYAKE T, et al. An image-based disaggregation study of time series energy data using gramian angular field[C]?∥2023 IEEE PES 15th Asia-Pacific Power and Energy Engineering Conference (APPEEC). Piscataway: IEEE Press, 2023: 1-6.
[25] FOODY G M. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification[J]. Remote Sensing of Environment2020239: 111630.
[26] XU B, WANG X, SUN F C, et al. Intelligent control of flexible hypersonic flight dynamics with input dead zone using singular perturbation decomposition?[J]. IEEE Transactions on Neural Networks and Learning Systems202334(9): 5926-5936.
[27] FAUZI A, SYARIF I, BADRIYAH T. Development of a mobile application for plant disease detection using parameter optimization method in convolutional neural networks algorithm[J]. EMITTER International Journal of Engineering Technology202311(2): 192-213.
[28] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition?[DB/OL]. arXiv preprint:1409.1556, 2014.
[29] SHU X B, ZHANG L Y, SUN Y L, et al. Host-parasite: Graph LSTM-in-LSTM for group activity recognition[J]. IEEE Transactions on Neural Networks and Learning Systems202132(2): 663-674.
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