Analysis of Cognitive Load in Mobile Control Teleoperation Systems under Time-delayed Environments

SUN Jiahao, YAN Jiadai, ZHU Shiyuan

Packaging Engineering ›› 2026, Vol. 47 ›› Issue (2) : 37-46.

PDF(971 KB)
PDF(971 KB)
Packaging Engineering ›› 2026, Vol. 47 ›› Issue (2) : 37-46. DOI: 10.19554/j.cnki.1001-3563.2026.02.004
Industrial Design

Analysis of Cognitive Load in Mobile Control Teleoperation Systems under Time-delayed Environments

  • SUN Jiahao, YAN Jiadai, ZHU Shiyuan*
Author information +
History +

Abstract

The work aims to investigate how delay extent and visual preference evolve and impact human-computer interaction in mobile control teleoperation systems. A virtual simulation experiment system for mobile control teleoperation was constructed to test and analyze the mobile control experiment on 24 participants. Subjective measurements were conducted with the Technology Acceptance Model (TAM) and NASA Task Load Index (NASA-TLX) questionnaires to understand the changes in cognitive load experienced by participants. Then, the applicability of predictive display technology in reducing the cognitive load of operators was explored. The Virtual Reality Predictive Display Interface (VR-PGI) was superior to the Image Predictive Display Interface (I-PGI) in enhancing user situational awareness and reducing cognitive burden. Moreover, user preferences were influenced not only by objective efficiency but also by the immersive experience and interaction provided by VR-PGI. Additionally, predictive display techniques were shown to be effective in mitigating the negative effects of time delays on cognitive load. This research provides theoretical guidance and practical suggestions for the design of predictive display interfaces (PGIs) in teleoperation, emphasizing the importance of considering both objective efficiency and user experience in design to balance the relationship between predictive accuracy and user experience and promoting the widespread application of predictive display technology in the field of teleoperation.

Key words

teleoperation / cognitive load / time delay / human-computer interaction

Cite this article

Download Citations
SUN Jiahao, YAN Jiadai, ZHU Shiyuan. Analysis of Cognitive Load in Mobile Control Teleoperation Systems under Time-delayed Environments[J]. Packaging Engineering. 2026, 47(2): 37-46 https://doi.org/10.19554/j.cnki.1001-3563.2026.02.004

References

[1] JUNJIE C, WEIYI H, AIGUO S.Dynamic Model of Human Operator in Force Telepresence System Based on Virtual Reality Technology[J]. Journal of Transcluction Technology, 2002, 15(1): 38-42.
[2] ARCARA P, MELCHIORRI C.Control Schemes for Teleoperation with Time Delay: A Comparative Study[J]. Robotics & Autonomous Systems, 2002, 38(1): 49-64.
[3] GAUVAIN M.Cognitive load theory[J]. Psychology of Learning & Motivation, 2010, 55(4): 37-76.
[4] BEJCZY A, KIM W, VENEMA S.The Phantom Robot: Predictive Displays for Teleoperation with Time Delay[C]. IEEE International Conference on Robotics and Automation. Piscataway, New York: IEEE, 1990.
[5] 丑武胜, 孟偲, 陈建新, 等. 空间科学实验机器人辅助遥操作系统[J]. 中国空间科学技术, 2003(6): 10-16.
CHOU W S, MENG C, CHEN J X, et al.Robot-assisted Remote Operating System for Space Science Experiments[J]. China Space Science and Technology, 2003(6): 10-16.
[6] SHERIDAN T B.Human Supervisory Control of Robot Systems[C]// Proceedings of the 1986 IEEE International Conference, New York: IEEE, 1986.
[7] HIRZINGER G, LANDZETTEL K, DIETRICH J .Sensorbased Space Robotics — ROTEX and Its Telerobotic Features[C]// Proceedings of the IEEE Transactions on Robotics and Automation, Graz: Elsevier, 1993.
[8] FREUND E, ROSSMANN J, BRASCH M.Open Multi- agent Control Architecture to Support Virtual-reality- based Man-machine Interfaces[J]. The International Society for Optical Engineering, 2001(4571): 219-229.
[9] DYBVIK H, LOLAND M, GERSTENBERG A, et al.A Low-cost Predictive Display for Teleoperation: Investigating Effects on Human Performance and Workload[J]. International Journal of Human-computer Studies, 2021, 145(1): 10-15.
[10] 龚德英, 张大均. 多媒体学习中认知负荷的优化控制[D]. 重庆: 西南大学, 2013.
GONG D Y, ZHANG D J.Optimal Control of Cognitive Load in Multimedia Learning[D]. Chongqing: Southwest University, 2013.
[11] KALYUGA S, RENKL A.Expertise Reversal Effect and Its Instructional Implications: Introduction to the Special Issue[J]. Instructional Science, 2010, 38(3): 209-215.
[12] SWELLER J.Cognitive Load during Problem Solving: Effects on Learning[J]. Cognitive Science, 1988, 12(2): 257-285.
[13] GOMBOLAY M, BAIR A, HUANG C, et al.Computational Design of Mixed-initiative Human-Robot Teaming that Considers Human Factors: Situational Awareness, Workload, and Workflow Preferences[J]. The International Journal of Robotics Research, 2017(36): 597-617.
[14] PARASURAMAN R, SHERIDAN T B, WICKENS C D . Situation Awareness, Mental Workload,Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs[J]. Journal of Cognitive Engineering and Decision Making, 2008, 2(2): 140-160.
[15] CACACE F, GERMANI A, MANES C.An Observer for a Class of Nonlinear Systems with Time Varying Observation Delay[J]. Systems & Control Letters, 2010, 59(5): 305-312.
[16] FARZA M, M'SAAD M, MENARD T, et al. Simple Cascade Observer for a Class of Nonlinear Systems with Long Output Delays[J]. IEEE Transactions on Automatic Control, 2015, 60(12): 3338-3343.
[17] LI Y, LIU S, XI N,et al.A Study of the Relationship between Brain States and Skill Level of Teleoperator[C]// Proceedings of the IEEE International Conference on Robotics and Biomimetics(ROBIO). Shenhen: IEEE, 2013.
[18] SCHMIDLIN E A, JONES K S .Do Tele-operators Learn to Better Judge Whether a Robot can Passthrough an Aperture?[J]. Human Factors, 2016, 58(2): 360-369.
[19] LOFT S, SANDERSON P, NEAL A, et al.Modeling and Predicting Mental Workload in En route Air Traffic Control: Critical Review and Broader Implications[J]. Human Factors, 2007, 49(3): 376-399.
[20] BROOKINGS J B, WILSON G F, SWAIN C R.Psychophysiological Responses to Changes in Workload during Simulated Air Traffic Control[J]. Biological Psychology, 1996, 42(3): 361-377.
[21] HART S G, STAVELAND L E.Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research[J]. Advances in Psychology, 1988(52): 139-183.
[22] ABBOOD H, ALNUAIMY W, ALATABY A,et al.Prediction of Driver Fatigue: Approaches and Open Challenges[C]// Proceedings of the 2014 14th UK Workshop on Computational Intelligence (UKCI), Bradford: IEEE, 2014.
[23] STEINFELD A, FONG T, KABER D, et al.Common Metrics for Human-robot Interaction[C]. Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human- robot Interaction. New York: ACM, 2006.
[24] CLARKE M A, SCHUETZLER R M, WINDLE J R, et al.Usability and Cognitive Load in the Design of a Personal Health Record[J]. Health Policy and Technology, 2020, 9(2): 218-224.
[25] 尤乾. 面向虚拟现实的数字界面可用性研究与应用[D]. 贵阳: 贵州大学, 2020.
YOU QIAN.Research and Application of Virtual Reality Oriented Digital Interface Usability[D]. Guiyang: Guizhou University, 2020.
[26] FERRELL W R.Remote Manipulation with Transmission Delay[J]. IEEE Transactions onHuman Factors in Electronics, 1965(1): 24-32.
PDF(971 KB)

Accesses

Citation

Detail

Sections
Recommended

/