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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100175
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor黃瀅瑛zh_TW
dc.contributor.advisorYing-Yin Huangen
dc.contributor.author吳皇政zh_TW
dc.contributor.authorHuang-Cheng Wuen
dc.date.accessioned2025-09-24T16:44:46Z-
dc.date.available2025-09-25-
dc.date.copyright2025-09-24-
dc.date.issued2025-
dc.date.submitted2025-08-13-
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[2]M. J. Barnes and C. A. Matz, “Crewstation design for UAV mission effectiveness,” Army Research Laboratory Technical Report ARL-TR-1734, 1998.
[3]T. B. Sheridan, “Humans and automation: System design and research issues,” Human Factors, vol. 49, no. 5, pp. 719–720, 2007.
[4]S. G. Hart and L. E. Staveland, “Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research,” Advances in Psychology, vol. 52, pp. 139–183, 198.
[5]A. R. Tolk, D. Diallo, and C. Turnitsa, “Modeling unexpected events in UAV missions: Simulation-based approaches,” Journal of Defense Modeling and Simulation, vol. 6, no. 2, pp. 69–81, 2009.
[6]T. L. Hooey and R. D. Foyle, “Simulating the impact of automation failure on human performance in UAV missions,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 48, no. 1, pp. 260–264, 2004.
[7]R. K. Jain and A. K. Singhal, “Task reallocation during emergency conditions in multi-operator UAV systems,” International Journal of Advanced Robotic Systems, vol. 15, pp. 1–9, 2018.
[8]T. D. Nielsen, P. M. Kristensen, and A. M. Pedersen, “Real-time task reallocation in UAV control using probabilistic reasoning,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 2, pp. 244–256, 2015.
[9]S. A. Johnson, A. W. Pritchett, and K. R. Comerford, “The importance of modeling and measuring individual operator variability when designing adaptive systems,” Journal of Cognitive Engineering and Decision Making, vol. 8, no. 3, pp. 221–242, 2014.
[10]J. W. Senders and N. H. Moray, Human Error: Cause, Prediction, and Reduction, CRC Press, 1991.
[11]T. D. Ramchurn, P. Vytelingum, A. Rogers, and N. R. Jennings, “Agent-based control for decentralised demand side management in the smart grid,” Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 5–12, 2011.
[12]S. A. Johnson, A. W. Pritchett, and K. R. Comerford, “The importance of modeling and measuring individual operator variability when designing adaptive systems,” Journal of Cognitive Engineering and Decision Making, vol. 8, no. 3, pp. 221–242, 2014.
[13]S. Atashfeshan, F. Taheri, and M. A. Badamchizadeh, “A Bayesian network-based dynamic task allocation approach for human–automation teams,” International Journal of Human–Computer Studies, vol. 150, 102604, 2021.
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[15]T. V. Tvaryanas, “Fatigue and crew rest in remotely piloted aircraft operations,” Crew Systems Directorate Technical Report, 2006.
[16]A. Chandarana, M. Cooke, and R. Parasuraman, “Task allocation in human–machine teams during dynamic and uncertain conditions,” IEEE Trans. Human-Machine Systems, vol. 52, no. 4, pp. 712–723, 2022.
[17]C. A. Nehme, J. Y. Cummings, J. M. Crandall, and M. A. Cummings, “The impact of automation level on situation awareness and workload in a UAV multiple operator setting,” MIT Humans and Automation Lab, 2006.
[18]M. J. Barnes and C. A. Matz, “Crewstation design for UAV mission effectiveness,” Army Research Laboratory Technical Report ARL-TR-1734, 199.
[19]T. B. Sheridan, “Humans and automation: System design and research issues,” Human Factors, vol. 49, no. 5, pp. 719–720, 2007.
[20]S. G. Hart and L. E. Staveland, “Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research,” Advances in Psychology, vol. 52, pp. 139–183, 1988.
[21]M. Cummings and S. Guerlain, “Developing operator capacity estimates for supervisory control of autonomous vehicles,” Human Factors, vol. 49, no. 1, pp. 1–15, 2007.
[22]C. Chen and M. Barnes, “Supervisory control of multiple UAVs: Human–automation interaction modeling,” Journal of Cognitive Engineering and Decision Making, vol. 8, no. 1, pp. 15–39, 2014.
[23]A. R. Tolk, D. Diallo, and C. Turnitsa, “Modeling unexpected events in UAV missions: Simulation-based approaches,” Journal of Defense Modeling and Simulation, vol. 6, no. 2, pp. 69–81, 2009.
[24]T. L. Hooey and R. D. Foyle, “Simulating the impact of automation failure on human performance in UAV missions,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 48, no. 1, pp. 260–264, 2004.
[25]C. D. Wickens, “Multiple resources and mental workload,” Human Factors, vol. 50, no. 3, pp. 449–455, 2008.
[26]C. D. Wickens and J. G. Hollands, Engineering Psychology and Human Performance, 3rd ed., Prentice Hall, 2000.
[27]C. S. Miller, “Cognitive load theory and user interface design: Toward a unified theory of cognitive load,” in Proc. 10th Int. Conf. HCI, 2001.
[28]R. K. Jain and A. K. Singhal, “Task reallocation during emergency conditions in multi-operator UAV systems,” International Journal of Advanced Robotic Systems, vol. 15, pp. 1–9, 2018.
[29]T. D. Nielsen, P. M. Kristensen, and A. M. Pedersen, “Real-time task reallocation in UAV control using probabilistic reasoning,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 2, pp. 244–256, 2015.
[30]S. Atashfeshan, F. Taheri, and M. A. Badamchizadeh, “A Bayesian network-based dynamic task allocation approach for human–automation teams,” International Journal of Human–Computer Studies, vol. 150, 102604, 2021
[31]S. A. Johnson, A. W. Pritchett, and K. R. Comerford, “The importance of modeling and measuring individual operator variability when designing adaptive systems,” Journal of Cognitive Engineering and Decision Making, vol. 8, no. 3, pp. 221–242, 2014.
[32]J. W. Senders and N. H. Moray, Human Error: Cause, Prediction, and Reduction, CRC Press, 1991.
[33]T. D. Ramchurn, P. Vytelingum, A. Rogers, and N. R. Jennings, “Agent-based control for decentralised demand side management in the smart grid,” Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 5–12, 2011.
[34]M. R. Endsley, “Toward a theory of situation awareness in dynamic systems,” Human Factors, vol. 37, no. 1, pp. 32–64, 1995.
[35]J. McCarley and C. Wickens, “Human factors implications of UAV operations,” Aviation Human Factors, FAA Report AHFS-2005, 2005.
[36]M. R. Endsley, “Toward a theory of situation awareness in dynamic systems,” Human Factors, vol. 37, no. 1, pp. 32–64, 1995.
[37]J. McCarley, C. Wickens, M. Alexander, and D. Thomas, “A computational model of attention/situation awareness,” Army Research Laboratory, ARL-TR-3217, 2004.
[38]C. D. Wickens, J. G. Hollands, S. Banbury, and R. Parasuraman, Engineering Psychology and Human Performance, 3rd ed., Prentice Hall, 2003.
[39]M. Yeh and C. D. Wickens, “Visual search and target detection in color displays: The role of color combinations and visual complexity,” Human Factors, vol. 30, pp. 79–91, 1988.
[40]M. A. Cooke et al., “Human factors of shared control for remotely piloted aircraft,” Human Factors, vol. 56, no. 3, pp. 465–475, 2014.
[41]J. Lachter, D. A. Battiste, R. A. McCandless, and L. T. Brandt, “Exploring single pilot operations: A qualitative analysis of pilot–copilot communication during simulated flight,” Human Factors, vol. 56, no. 3, pp. 659–672, 2014.
[42]A. Chandarana, M. Cooke, and R. Parasuraman, “Task allocation in human–machine teams during dynamic and uncertain conditions,” IEEE Trans. Human-Machine Systems, vol. 52, no. 4, pp. 712–723, 2022.
[43]S. G. Hart and L. E. Staveland, “Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research,” Advances in Psychology, vol. 52, pp. 139–183, 1988.
[44]P. A. Hancock and J. S. Warm, “A dynamic model of stress and sustained attention,” Human Factors, vol. 31, no. 5, pp. 519–537, 1989.
[45]M. Mouloua, P. A. Hancock, and J. R. Parasuraman, “Automation and human performance: Theory and applications,” CRC Press, 2001.
[46]S. R. Dixon, C. D. Wickens, and D. Chang, “Comparing quantitative model predictions to experimental data in multiple-UAV flight control,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 47, no. 3, pp. 112–116, 2003.
[47]T. V. Tvaryanas, L. A. Thompson, and M. R. Constable, “Human factors in remotely piloted aircraft operations,” Crew Systems Directorate Technical Report, Brooks City-Base, TX: USAF Research Lab, 2006.
[48]M. R. Endsley, “Toward a theory of situation awareness in dynamic systems,” *Human Factors*, vol. 37, no. 1, pp. 32–64, 1995.
[49]C. D. Wickens, “Multiple resources and performance prediction,” *Theoretical Issues in Ergonomics Science*, vol. 3, no. 2, pp. 159–177, 2002.
[50]C. Chen and M. Barnes, “Human–agent teaming for multi-robot control: A review of human interaction strategies,” *Human Factors*, vol. 56, no. 3, pp. 521–538, 2014.
[51]B. T. Sullivan, M. J. Kochenderfer, and S. E. Young, “Design of a task-based metric for UAV video quality assessment,” *AIAA Modeling and Simulation Technologies Conference*, 2016.
[52]Z. Bylinskii, T. Judd, F. Durand, A. Torralba, and A. Oliva, “What do different evaluation metrics tell us about saliency models?” *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 41, no. 3, pp. 740–757, 2018.
[53]H. Kim and W. Sung, “MTF-based image quality evaluation for UAV surveillance sensors using slanted-edge method,” Remote Sensing, vol. 16, no. 1, pp. 52–65, 2024.
[54]M. Grindley, J. McDermott, and A. O’Neill, “A decade of large UAV mishaps: Human factors analysis and lessons learned,” Journal of Safety Research, vol. 94, pp. 102–113, 2024.
[55]N. B. Sarter, D. D. Woods, and C. E. Billings, “Automation surprises,” Handbook of Human Factors and Ergonomics, 3rd ed., G. Salvendy, Ed. Hoboken, NJ, USA: Wiley, 2007, pp. 1926–1943.
[56]J. Lachter, M. Isaacson, M. K. Battiste, and E. Johnson, “Exploring single pilot operations: An examination of communication challenges and automation support,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 58, no. 1, pp. 6–10, 2014.
[57]A. Pedersen, J. Franke, and S. Warren, “Cognitive resilience in UAV control: A review of automation, workload, and decision-support strategies,” Human Factors and Aerospace Safety, vol. 11, no. 2, pp. 89–110, 2022.
[58]NASA Human Systems Integration Division, Human Factors Design Standard for Ground Control Stations, NASA/TM-2023-000133, 2023.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100175-
dc.description.abstract本研究聚焦於無人機雙人團隊於任務執行過程中,因突發事件(如抬頭顯示器 head-up display, HUD 故障)所引發的任務中斷與再分配需求。當操作任務突遭干擾,團隊成員能否迅速調整任務分工,將直接影響任務完成效率與操作人員負荷。過往相關研究多著重於自動化系統下的資源分配機制,針對人工操作之雙人團隊於突發情境中執行任務再分配之實證研究則相對缺乏,故本研究旨在補足此一缺口,探討各種分工策略對任務績效與人員負荷的影響。
本研究設計三種任務再分配策略(Scenario 1~3),並建構操控無人機飛行之模擬環境,重現雙人團隊在執行無人機偵蒐任務時遇到 HUD 故障的情境,觀察不同應變策略對任務執行結果是否造成影響。實驗評估指標包含任務完成時間、偵蒐任務目標物拍攝品質,以及操作人員於任務執行後所填寫之 NASA Task Load Index, NASA-TLX 主觀負荷評估。參與實驗的 18 組團隊(共 36 名實驗參與者)皆依隨機順序執行三種情境,藉以控制學習與疲勞效應。
研究結果顯示,情境三(角色交錯型策略)在效率與品質間取得最佳平衡,其平均任務完成時間顯著低於其他情境,同時拍攝分數與主觀負荷評分亦維持穩定,證實靈活的角色切換與任務共享機制有助於提升整體表現;而情境一與二雖具有固定分工優勢,卻易造成單一成員負荷過重。整體而言,本研究不僅提供任務再分配策略的實證依據,也對未來無人機團隊訓練、介面設計與操作流程規劃提出具體建議,強調團隊在面對突發事件時需具備足夠的任務彈性與溝通協調能力,方能維持任務安全與效能。
zh_TW
dc.description.abstractThis study focuses on two-person unmanned aerial vehicle (UAV) teams and their need to reallocate tasks in response to unexpected events, such as head-up display (HUD) failures, during mission execution. When operational disruptions occur, the team's ability to promptly adjust task distribution directly affects mission completion efficiency and operator workload. While previous research has primarily addressed resource allocation in automated systems, there remains a lack of empirical studies on manual task reallocation within human-operated two-person teams under sudden disruptions. Therefore, this study aims to address this research gap by examining the effects of different task redistribution strategies on team performance and operator workload.
Three task reallocation strategies (Scenarios 1 to 3) were designed and tested in a simulated UAV reconnaissance environment featuring a mid-mission HUD failure. The experimental metrics included mission completion time, image quality of photographed targets, and subjective workload as measured by the NASA Task Load Index (NASA-TLX). A total of 18 two-person teams (36 participants) completed all three scenarios in randomized order to counterbalance learning and fatigue effects.
The results indicate that Scenario 3 (role-switching strategy) achieved the best balance between efficiency and quality, with significantly shorter mission times and stable image scores and workload ratings. This suggests that flexible role-switching and task-sharing mechanisms enhance overall team performance. In contrast, Scenarios 1 and 2, which maintained fixed roles, were more likely to result in workload imbalance, placing excessive burden on a single team member. Overall, this study provides empirical support for task reallocation strategies and offers practical insights for UAV operator training, interface design, and operational procedures. It underscores the importance of task flexibility and effective communication for ensuring mission safety and performance in the face of unexpected disruptions.
en
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dc.description.tableofcontents口試委員會審定書 i
誌謝 ii
摘要 iii
Abstract iv
目次 vi
圖次 ix
表次 x
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 2
1.3 研究架構 3
第二章 文獻回顧 6
2.1 任務分析與重新分配理論 6
2.1.1 控制權轉移策略 7
2.1.2 飛行員自行排除故障策略 8
2.1.3 角色交換策略 9
2.2 無人機操作團隊與角色分工 11
2.3 團隊績效與工作負荷理論 12
2.4 無人機影像任務品質評估 14
2.5 航空任務中突發事件處理與人因介入 15
第三章 研究方法 18
3.1 實驗設計 18
3.2 研究參與者招募及資格限制 19
3.3 實驗任務說明 19
3.3.1 團隊任務說明與目標 20
3.3.2 三種任務重新分配情境設計 21
3.3.3 HUD黑屏之故障排除流程 24
3.3.4 操作權切換機制 26
3.4 實驗環境及設備 27
3.4.1 實驗儀器及軟體 28
3.4.2 實驗環境 30
3.5 實驗流程 31
3.5.1 準備階段 31
3.5.2 實驗階段 32
3.5.3 任務後評估與問卷填寫 36
3.5.4 數據蒐集與資料分析 36
3.6 研究目標及假設 40
第四章 研究結果 41
4.1 研究參與者 41
4.2 任務完成時間分析 41
4.2.1 三種情境下之平均任務時間比較 42
4.2.2 任務完成時間之Friedman檢定分析 43
4.2.3 故障排除時間之比較 44
4.2.4 小結 44
4.3 拍攝分數結果分析 45
4.3.1 三種情境下目標物拍攝分數分布 45
4.3.2 統計比較與顯著差異分析 46
4.3.3 小結 47
4.4 NASA-TLX 主觀負荷分析 48
4.4.1 各向度分數描述統計 48
不同情境下各角色之總分與子量表比較 50
4.4.2 50
4.4.3 任務角色與情境之交互作用分析 53
4.4.4 小結 55
第五章 結果與討論 56
5.1 主要發現 56
5.1.1 三種情境下之工作分配與團隊表現關係探討 58
5.1.2 任務時長與主觀負荷的交互關係分析 59
5.2 研究限制及未來方向 60
5.3 結論 63
參考文獻 65
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dc.language.isozh_TW-
dc.subject無人機zh_TW
dc.subject任務分工zh_TW
dc.subject雙人團隊zh_TW
dc.subject認知負荷zh_TW
dc.subject績效表現zh_TW
dc.subject突發故障zh_TW
dc.subjecttwo-person teamen
dc.subjectUnmanned Aerial Vehicle (UAV)en
dc.subjectunexpected failureen
dc.subjectperformanceen
dc.subjectcognitive workloaden
dc.subjecttask distributionen
dc.title團隊任務分析與任務重新分配:以無人機操作情境為例zh_TW
dc.titleTeam Task Analysis and Reallocation: An Application in UAV Operational Scenariosen
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee瞿志行;李昀儒zh_TW
dc.contributor.oralexamcommitteeChih-Hsing Chu;Yun-Ju Leeen
dc.subject.keyword無人機,任務分工,雙人團隊,認知負荷,績效表現,突發故障,zh_TW
dc.subject.keywordUnmanned Aerial Vehicle (UAV),task distribution,two-person team,,cognitive workload,performance,unexpected failure,en
dc.relation.page70-
dc.identifier.doi10.6342/NTU202504336-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2025-08-15-
dc.contributor.author-college工學院-
dc.contributor.author-dept機械工程學系-
dc.date.embargo-lift2030-07-30-
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