帳號:guest(216.73.216.146)          離開系統
字體大小: 字級放大   字級縮小   預設字形  

詳目顯示

以作者查詢圖書館館藏以作者查詢臺灣博碩士論文系統以作者查詢全國書目
作者(中文):范育瑄
作者(外文):Fan, Yu-Hsuan
論文名稱(中文):以操作人員為中心的人機協作設計:以組裝作業為例
論文名稱(外文):Operator-Centered Design for Human Robot Collaboration: The Assembly Task as an Example
指導教授(中文):盧俊銘
指導教授(外文):Lu, Jun-Ming
口試委員(中文):黃瀅瑛
許有真
口試委員(外文):Huang, Ying-Yin
Hsu, Yu-Chen
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學號:110034572
出版年(民國):112
畢業學年度:111
語文別:中文
論文頁數:166
中文關鍵詞:人機協作混合實境組裝作業溝通情境察覺心智負荷
外文關鍵詞:Human-Robot CollaborationMixed Realityassembly taskcommunicationsituation awarenessmental workload
相關次數:
  • 推薦推薦:0
  • 點閱點閱:114
  • 評分評分:*****
  • 下載下載:0
  • 收藏收藏:0
當代製造業對於自動化的需求越來越高,在克服技術限制、實現全自動化之前,仍有一個過渡期,也就是人機協作(Human Robot Collaboration, HRC),但目前人機協作仍存在一些待解決的問題。因此,本研究以在人機協作中最常見、需求最高且最常被討論的組裝作業為例,旨在定義人員於人機協作模式下的認知歷程、找出可能面臨的瓶頸步驟,並據以提出改善方案、驗證「以操作人員為中心的人機協作設計」之成效。

為了達成全面性的評估,本研究除了透過人類訊息處理模型分析協作過程,亦設計兩階段實驗,先透過第一階段實驗找出人員在人機協作過程中的瓶頸,並與理論模型分析的結果整合、據以提出基於主動式提示的改善方案,隨後於第二階段實驗評估、確認其成效。考量可用設備資源的限制,本研究使用混合實境(Mixed Reality)執行模擬評估,使參與者在透過頭戴型顯示器看到虛擬場景的同時也能夠在真實世界中接觸、拿取工件,以獲得觸覺回饋。兩階段實驗招募同一批30名年齡介於20歲到37歲之間(25.7 ± 4.1)的男性參與者。

經理論模型分析與第一階段實驗結果發現,在一般的人機協作設計下,可能會因長期記憶不足或線索太相似而無法精準地提取長期記憶、可用注意力資源不足以正確提取長期記憶或接收環境刺激、短期記憶所需的注意力資源被佔用等因素而產生瓶頸步驟,且在溝通、情境察覺、信任與安全等面向皆不及過往較熟悉的雙人協作,因此人機協作確有改善的空間。第二階段的實驗結果顯示,加入主動式提示後的人機協作確實有效地解決協作過程中的瓶頸步驟,在溝通、情境察覺程度、心智負荷、協作有效性、信任與安全感等面皆有顯著改善,且操作人員對於人機協作之使用意願也大幅提昇。

基於以上成果,本研究彙整四點「以操作人員為中心的人機協作設計」之一般性建議,應可直接套用至與組裝作業類似的情境中,若為其他類型的人機協作,則宜參考本研究之方法並針對不同的任務需求自行調整至適用的情境。若欲提供更完善的提示設計方案,未來可以進一步探討不同的提示設計形式、出現時機、持續的時間長短等之可能影響。
Over the past decades, the demand for automation in the manufacturing industry has kept increasing. However, there are still technical limitations to achieve full automation. Human-Robot Collaboration (HRC) is hence considered during the transition period prior to full automation, but there are still unsolved problems. Therefore, by taking the assembly task, the most common and highely demanded case in the field of HRC, as an example, this study aims to define the operator’s cognitive processes in HRC. Based on the possible bottlenecks being identified, improvements will be proposed, as well as verifying the effectiveness of "operator-centered design for HRC."

For a more comprehensive assessment, in addition to the analysis of the collaboration process using the Human Information Processing (HIP) model, two stages of experiment were also devised. In the first-stage experiment, potential bottlenecks within the HRC process were identified. These findings were subsequently merged with the analysis derived from the HIP model to propose proactive prompts. The effectiveness of such countermeasures was then validated in the second-stage experiment. Considering the resource constraints, Mixed Reality (MR) were utilized for the simulated evaluation. This approach allowed participants to physically interact with workpieces within a realworld context for tactile feedback, while being immersed in virtual environments via head-mounted displays (HMD). The same group of 30 male participants aged 20 to 37 (25.7 ± 4.1) years old were recruited for the two stages of experiment.

The results of theoretical analysis and the first-stage experiment revealed that potential bottlenecks associated with the general HRC design may be due to insufficient long-term memory, too similar cues to accurately retrieve long-term memory, insufficient attention resources to correctly retrieve long-term memory or receive environmental stimuli, and occupation of attention resources required for short-term memory. Moreover, in terms of communication, situation awareness, trust and safety, it is not as good as the inter-operator collaboration that people are more familiar with. After adding proactive prompts in HRC scenarios, results of the second-stage experiment showed that improvement was achieved in terms of enhanced communication, better situation awareness, reduced mental workload, improved collaboration effectiveness, increased trust and safety, and stronger willingness to get involved with HRC. Moreover, four general recommendations were summarized for "operator-centered design for HRC" with assembly tasks.
摘要 I
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與範圍 4
1.3 研究架構與流程 4
第二章 文獻回顧 9
2.1 人機協作 9
2.1.1 人機協作的應用領域 10
2.1.2 人機協作的等級 12
2.1.3 人機協作的困難與挑戰 14
2.2 克服人機協作的瓶頸 16
2.2.1 分析人機協作的方法 16
2.2.2 解決人機協作瓶頸的方法 18
2.3 人機協作評估工具與方法 23
2.3.1 評估形式 23
2.3.2 評估指標 26
2.4 小結 29
第三章 研究方法 30
3.1 人機協作之瓶頸分析 30
3.1.1 標的案例 30
3.1.2 理論模型分析與兩階段實驗設計 31
3.2 實驗規劃與準備 33
3.2.1 研究參與者 33
3.2.2 儀器與設備 34
3.2.3 混合實境的場景 36
3.2.4 協作任務內容 37
3.2.5 次要任務與機器故障情境 40
3.3 第一階段實驗設計:分析現況的瓶頸步驟 42
3.3.1 自變數 42
3.3.2 應變數 42
3.3.3 實驗流程 51
3.3.4 資料處理與分析方法 53
3.4 第二階段實驗設計:提示設計的成效驗證 54
3.4.1 自變數 54
3.4.2 應變數 55
3.4.3 實驗流程 55
3.4.4 資料處理與分析方法 57
第四章 分析瓶頸步驟 59
4.1 基於理論的協作瓶頸分析 59
4.2 第一階段實驗結果分析 67
4.2.1 沉浸感衡量 68
4.2.2 資料前處理 69
4.2.3 溝通 71
4.2.4 情境察覺 72
4.2.5 心智負荷 72
4.2.6 協作有效性 76
4.2.7 信任與安全 77
4.3 統整理論模型與第一階段實驗發現的瓶頸 78
4.4 小結 80
第五章 人機協作之設計改善與成效驗證 82
5.1 基於提示設計的改善方案 82
5.1.1 人員和高自主性與低自主性機器協作時,未及時注意到機器狀態或無法正確判斷機器狀態 83
5.1.2 人員自身的組裝順序錯誤、忘記目前任務進度而未執行某些組裝步驟、還未放好工件就先拿螺絲 85
5.1.3 人員和低自主性機器協作時,在放完螺絲後忘記按下對應按鈕以通知機器,或是找不到按鈕、按錯按鈕 86
5.1.4 人員和高自主性與低自主性機器協作時,未注意保持自己與機器間的安全距離 87
5.2 第二階段實驗結果分析 88
5.2.1 沉浸感衡量與對第一階段殘留之印象 88
5.2.2 資料前處理 89
5.2.3 提示有效性 90
5.2.4 溝通 91
5.2.5 情境察覺 94
5.2.6 心智負荷 94
5.2.7 協作有效性 99
5.2.8 信任與安全 101
5.3 小結 103
第六章 討論 104
6.1 實驗結果比較 104
6.1.1 第一階段實驗:分析瓶頸步驟 104
6.1.2 第二階段實驗結果:提示設計的效果 105
6.2 實驗資料有效性 107
6.2.1 以主要任務完成時間比較協作模式的適用性 107
6.2.2 次要任務呈現的方式 107
6.2.3 可能的離群值 108
6.2.4 次要任務的正確率 109
6.3 改善後的人機協作與雙人協作之比較 109
6.3.1 溝通 109
6.3.2 情境察覺 111
6.3.3 心智負荷 111
6.3.4 協作有效性 113
6.3.5 信任與安全 114
6.4 參與者對五種情境的使用意願 115
6.5 不符合假設的結果 116
6.5.1 與第一階段實驗中與理論模型分析不一致的結果 117
6.5.2 第一階段實驗:一般的人機協作與雙人協作間之差異 117
6.5.2.1 心智負荷 117
6.5.2.2 次要任務完成時間 119
6.5.2.3 協作有效性 120
6.5.3 第二階段實驗:「以操作人員為中心的人機協作」之成效 120
6.5.3.1 心智負荷 121
6.5.4 提示改善方向 122
6.6 「以操作人員為中心的人機協作設計」之一般性建議 123
第七章 結論 126
7.1 主要發現 126
7.2 研究貢獻與可能應用 128
7.3 研究限制與未來方向 130
參考文獻 133
附錄一、主觀問卷量表(人機協作)150
附錄三、NASA-TLX 工作負荷評級量表 155
附錄四、研究倫理審查核可證明 157
附錄五、訪談大綱 158
附錄六、對第一階段實驗的印象 159
附錄七、三種協作情境流程圖之比較 160
中文部分:
1. 李佩蓉(2010)。消費者在虛擬實境中的臨場感體驗與沉浸傾向之研究:以商業動感模擬
遊戲機為例。國立交通大學經營管理研究所碩士論文。
2. 謝尚穎(2018)。以沉浸式虛擬實境模擬人機協作之可行性評估:以遞交作業為例。國立
清華大學工業工程與工程管理研究所碩士論文。
3. 經濟部統計處( 2022 )。經濟部工業局所轄各工業區員工性別統計分析。
https://www.moea.gov.tw/Mns/dos/content/SubMenu.aspx?menu_id=6976
4. 道路交通標誌標線號誌設置規則( 2023 )。全國法規資料。
https://law.moj.gov.tw/LawClass/LawParaDeatil.aspx?pcode=K0040014&bp=13

英文部分:
1. Aivaliotis, P., Kaliakatsos-Georgopoulos, D., Papavasileiou, A., & Makris, S. (2021). A design
of Human and overhead Robot Interaction (HoRI) framework for cooperative robotic
applications in copper industry. Procedia CIRP, 104, pp. 1500-1505.
2. Akers, A., Barton, J., Cossey, R., Gainsford, P., Griffin, M., & Micklewright, D. (2012). Visual
color perception in green exercise: Positive effects on mood and perceived exertion.
Environmental Science & Technology, 46(16), pp. 8661-8666.
3. Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001). Recent
advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), pp. 34-47.
4. Babich, N. (2019). Using Red and Green in UI Design. UX Planet. https://uxplanet.org/usingred-
and-green-in-ui-design-66b39e13de91.
5. Baker, M. & Yanco, H. A. (2004). Autonomy mode suggestions for improving human-robot
interaction. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE
Cat. No. 04CH37583) (3), pp. 2948-2953. IEEE.
6. Baraglia, J., Cakmak, M., Nagai, Y., Rao, R. P., & Asada, M. (2017). Efficient human-robot
collaboration: when should a robot take initiative?. The International Journal of Robotics
Research, 36(5-7), pp. 563-579.
7. Baraka, K., Rosenthal, S., & Veloso, M. (2016). Enhancing human understanding of a mobile
robot's state and actions using expressive lights. In: 2016 25th IEEE International Symposium
on Robot and Human Interactive Communication, pp. 652-657. IEEE.
8. Belingardi, G., Heydaryan, S., & Chiabert, P. (2017). Application of speed and separation
monitoring method in human-robot collaboration: industrial case study. In: 17th International
Scientific Conference on Industrial Systems, pp.4-6.
9. Billings, C. E. (1991). Human-centered aircraft automation: A concept and guidelines, Vol.
103885. National Aeronautics and Space Administration, Ames Research Center.
10. Bordegoni, M., Cugini, U., Belluco, P., & Aliverti, M. (2009). Evaluation of a haptic-based
interaction system for virtual manual assembly. In: Virtual and Mixed Reality: Third
International Conference, pp. 303-312. Springer.
11. Bruno, F., Barbieri, L., & Muzzupappa, M. (2020). A Mixed Reality system for the ergonomic
assessment of industrial workstations. International Journal on Interactive Design and
Manufacturing (IJIDeM), 14(3), pp. 805-812.
12. Cain, B. (2007). A Review of The Mental Workload Literature. Defence Research and
Development. Canada: Human System Integration Section, Part II, Vol. 4, pp. 1-34.
13. Castro, B., Roberts, M., Mena, K., & Boerkoel, J. (2017). Who Takes the Lead? Automated
Scheduling for Human-Robot Teams. In: AAAI Fall Symposia, pp. 85-89.
14. Cha, E. & Matarić, M. (2016). Using nonverbal signals to request help during human-robot
collaboration. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), pp. 5070-5076. IEEE.
15. Chan, A. H. & Ng, A. W. (2009). Perceptions of implied hazard for visual and auditory alerting
signals. Safety Science, 47(3), pp. 346-352.
16. Chandan, K., Kudalkar, V., Li, X., & Zhang, S. (2021). ARROCH: Augmented reality for robots
collaborating with a human. In: 2021 IEEE International Conference on Robotics and
Automation (ICRA), pp. 3787-3793. IEEE.
17. Charalambous, G., Fletcher, S., & Webb, P. (2016). The development of a scale to evaluate trust
in industrial human-robot collaboration. International Journal of Social Robotics, 8, pp. 193-
209.
18. Charalambous, G. & Stout, M. (2016). Optimising train axle inspection with the implementation
of human-robot collaboration: A human factors perspective. In: 2016 IEEE International
Conference on Intelligent Rail Transportation (ICIRT), pp. 254-258. IEEE.
19. Chen, S. B., Qiu, T., Lin, T., Wu, L., Tian, J. S., Lv, W. X., & Zhang, Y. (2004). Intelligent
technologies for robotic welding. In: Robotic welding, intelligence and automation, pp. 123-143.
Springer.
20. Chiossi, F. & Mayer, S. (2023). How Can Mixed Reality Benefit From Physiologically-Adaptive
Systems? Challenges and Opportunities for Human Factors Applications. arXiv preprint
arXiv:2303.17978.
21. Coates, G. (1992). Program from Invisible Site-a virtual sho, a multimedia performance work
presented by George Coates Performance Works.
22. Choi, J. J., Kim, Y., & Kwak, S. S. (2014). The autonomy levels and the human intervention
levels of robots: The impact of robot types in human-robot interaction. In: The 23rd IEEE
International Symposium on Robot and Human Interactive Communication, pp. 1069-1074.
IEEE.
23. Choi, S., Eakins, W., Rossano, G., & Fuhlbrigge, T. (2013). Lead-through robot teaching. In:
2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA), pp. 1-4.
IEEE.
24. Colgate, J. E., Wannasuphoprasit, W., & Peshkin, M. A. (1996). Cobots: Robots for collaboration
with human operators. In: Proceedings of the 1996 ASME international mechanical engineering
congress and exposition, 58, pp.433-439.
25. Dennett, D. (2009). Intentional Systems Theory. In A. Beckermann, B. P. McLaughlin, & S.
Walter (Eds.), The Oxford Handbook of Philosophy of Mind, pp. 339-350. Oxford: Oxford
University Press.
26. Donadio, F., Frejaville, J., Larnier, S., & Vetault, S. (2016). Human-robot collaboration to
perform aircraft inspection in working environment. In: Proceedings of 5th International
conference on Machine Control and Guidance (MCG).
27. Drascic, D. & Milgram, P. (1996). Perceptual issues in augmented reality. In: Stereoscopic
displays and virtual reality systems III, Vol. 2653, pp. 123-134.
28. Durso, F. T., & Dattel, A. R. (2004). SPAM: The real-time assessment of SA. In: S. Banbury &
S. Tremblay (Eds.), A Cognitive Approach to Situation Awareness: Theory and Application, pp.
137-154.
29. Elliot, A. J. & Maier, M. A. (2014). Color psychology: Effects of perceiving color on
psychological functioning in humans. Annual Review of Psychology, 65, pp. 95-120.
30. Ende, T., Haddadin, S., Parusel, S., Wüsthoff, T., Hassenzahl, M., & Albu-Schäffer, A. (2011).
A human-centered approach to robot gesture based communication within collaborative working processes. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.
3367-3374. IEEE.
31. Endsley, M. R. (1995). Measurement of situation awareness in dynamic systems. Human Factors,
37(1), pp. 65-84.
32. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human
Factors, 37, pp. 85-104.
33. Endsley, M. R. & Kiris, E. O. (1995). Situation awareness global assessment technique (SAGAT)
TRACON air traffic control version user guide. Lubbock, TX: Texas Tech University.
34. Etzi, R., Huang, S., Scurati, G. W., Lyu, S., Ferrise, F., Gallace, A., & Bordegoni, M. (2019).
Using virtual reality to test human-robot interaction during a collaborative task. In: International
Design Engineering Technical Conferences and Computers and Information in Engineering
Conference. Vol. 59179, p. V001T02A080, American Society of Mechanical Engineers.
35. Faccio, M., Bottin, M., & Rosati, G. (2019). Collaborative and traditional robotic assembly: a
comparison model. The International Journal of Advanced Manufacturing Technology, 102, pp.
1355-1372.
36. Frankfurt (2021). International Federation of Robotics reports. International Federation of
Robotics.
https://ifr.org/ifr-press-releases/news/robot-density-nearly-doubled-globally - downloads
37. Franklin, C. S., Dominguez, E. G., Fryman, J. D., & Lewandowski, M. L. (2020). Collaborative
robotics: New era of human-robot cooperation in the workplace. Journal of Safety Research, 74,
pp. 153-160.
38. Freedy, A., DeVisser, E., Weltman, G., & Coeyman, N. (2007). Measurement of trust in humanrobot
collaboration. In: 2007 International symposium on collaborative technologies and
systems, pp. 106-114. IEEE.
39. Gambao, E., Hernando, M., & Surdilovic, D. (2012). A new generation of collaborative robots
for material handling. In: ISARC. Proceedings of the International Symposium on Automation
and Robotics in Construction, Vol. 29, p. 1. IAARC Publications.
40. Ganesan, R. K. (2017). Mediating human-robot collaboration through mixed reality cues.
Doctoral dissertation, Arizona State University.
41. Ganesan, R. K., Rathore, Y. K., Ross, H. M., & Amor, H. B. (2018). Better teaming through
visual cues: how projecting imagery in a workspace can improve human-robot collaboration.
IEEE Robotics & Automation Magazine, 25(2), pp. 59-71.
42. Global Collaborative Robot (Cobot) Market: Focus on Payload, Application Sales Channel,
Component, and Industry-Analysis & Forecast, 2020-2025. (2020). BIS Research Inc.
43. Goldstein, M., Öquist, G., & Björk, S. (2002). Evaluating Sonified Rapid Serial Visual
Presentation: An immersive reading experience on a mobile device. In: ERCIM Workshop on
User Interfaces for All, pp. 508-523. Springer.
44. Goetz, J., Kiesler, S., & Powers, A. (2003). Matching robot appearance and behavior to tasks to
improve human-robot cooperation. In: The 12th IEEE International Workshop on Robot and
Human Interactive Communication, pp. 55-60. IEEE.
45. Gombolay, M., Bair, A., Huang, C., & Shah, J. (2017). Computational design of mixed-initiative
human-robot teaming that considers human factors: situational awareness, workload, and
workflow preferences. The International Journal of Robotics Research, 36(5-7), pp. 597-617.
46. Hamieh, B. (2020). The Meaning of Red and Green in User Interfaces for the Color Deficient.
University Honors Theses.
47. Hart, S. G. & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results
of empirical and theoretical research. In: Advances in Psychology, Vol. 52, pp. 139-183.
48. Hawkins, J E (2023) Human Ear. Britannica.
https://www.britannica.com/science/ear
49. He, W., Li, Z., & Chen, C. P. (2017). A survey of human-centered intelligent robots: issues and
challenges. IEEE/CAA Journal of Automatica Sinica, 4(4), pp. 602-609.
50. Helms, E., Schraft, R. D., & Hagele, M. (2002). rob@ work: Robot assistant in industrial
environments. In: Proceedings. 11th IEEE International Workshop on Robot and Human
Interactive Communication, pp. 399-404. IEEE.
51. Hoang, K. C., Chan, W. P., Lay, S., Cosgun, A., & Croft, E. (2022). Virtual barriers in augmented
reality for safe and effective human-robot cooperation in manufacturing. In: 2022 31st IEEE
International Conference on Robot and Human Interactive Communication, pp. 1174-1180.
IEEE.
52. Hoenig, W., Milanes, C., Scaria, L., Phan, T., Bolas, M., & Ayanian, N. (2015). Mixed reality
for robotics. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), pp. 5382-5387. IEEE.
53. Hopko, S. K., Khurana, R., Mehta, R. K., & Pagilla, P. R. (2021). Effect of cognitive fatigue,
operator sex, and robot assistance on task performance metrics, workload, and situation
awareness in human-robot collaboration. IEEE Robotics and Automation Letters, 6(2), pp. 3049-
3056.
54. International Organization for Standardization. (2016). ISO TS 15066-Robots and robotic
devices - Collaborative robots.
55. Ivaldi, S., Anzalone, S. M., Rousseau, W., Sigaud, O., & Chetouani, M. (2014). Robot initiative
in a team learning task increases the rhythm of interaction but not the perceived
engagement. Frontiers in Neurorobotics, 8, p.5.
56. Kadir, B. A., Broberg, O., & Souza da Conceição, C. (2018). Designing human-robot
collaborations in industry 4.0: explorative case studies. In: DS 92: Proceedings of the DESIGN
2018 15th International Design Conference, pp. 601-610.
57. Kaber, D. B. & Endsley, M. R. (1997). Out‐of‐the‐loop performance problems and the use of
intermediate levels of automation for improved control system functioning and safety. Process
Safety Progress, 16(3), pp. 126-131.
58. Kassem, K., Ungerböck, T., Wintersberger, P., & Michahelles, F. (2022). What Is Happening
Behind The Wall? Towards a Better Understanding of a Hidden Robot's Intent By Multimodal
Cues. Proceedings of the ACM on Human-Computer Interaction, 6 , pp. 1-19.
59. Kendon, A. (1967). Some functions of gaze-direction in social interaction. Acta Psychologica,
26, pp.22-63.
60. Kim, T., & Hinds, P. (2006). Who should I blame? Effects of autonomy and transparency on
attributions in human-robot interaction. In: ROMAN 2006-The 15th IEEE International
Symposium on Robot and Human Interactive Communication, pp. 80-85. IEEE.
61. Knowles, W. B. (1963). Operator loading tasks. Human Factors, 5(2), pp. 155-161.
62. Kolbeinsson, A., Lagerstedt, E., & Lindblom, J. (2019). Foundation for a classification of
collaboration levels for human-robot cooperation in manufacturing. Production &
Manufacturing Research, 7(1), pp. 448-471.
63. Körner, U., Müller‐Thur, K., Lunau, T., Dragano, N., Angerer, P., & Buchner, A. (2019).
Perceived stress in human-machine interaction in modern manufacturing environments—
Results of a qualitative interview study. Stress and Health, 35(2), pp. 187-199.
64. Krishnakumar K., Stepanyan V., Barlow J., Hardy G., Dorais G., Poolla C., Reardon S. and
Soloway D. (2014) Initial Evaluations of LoC Prediction Algorithms Using the NASA Vertical
Motion Simulator. In: AIAA Guidance, Navigation, and Control Conference, p. 0265.
65. Krüger, M., Wiebel, C. B., & Wersing, H. (2017). From tools towards cooperative assistants. In:
Proceedings of the 5th International Conference on Human Agent Interaction, pp. 287-294.
66. KUKA. (2020). Human-robot collaboration: 3 Case Studies. Wevolver.
https://www.wevolver.com/article/humanrobot.collaboration.3.case.studies.
67. Kumar, S., Savur, C., & Sahin, F. (2020). Survey of human-robot collaboration in industrial
settings: Awareness, intelligence, and compliance. IEEE Transactions on Systems, Man, and
Cybernetics: Systems, 51(1), pp. 280-297.
68. Kuz, S., Petruck, H., Heisterüber, M., Patel, H., Schumann, B., Schlick, C. M., & Binkofski, F.
(2015). Mirror neuronsand human-robot interaction in assembly cells. Procedia manufacturing,
3, pp. 402-408.
69. Ladkin, P. (1996). AA965 Cali accident report. University of Bielefeld.
70. Lam, C. P., Chou, C. T., Chiang, K. H., & Fu, L. C. (2010). Human-centered robot navigation—
towards a harmoniously human-robot coexisting environment. IEEE Transactions on Robotics,
27(1), pp. 99-112.
71. Lee, J. D. & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human
Factors, 46(1), pp. 50-80.
72. Liau, Y. Y. & Ryu, K. (2020). Task allocation in human-robot collaboration (HRC) based on task
characteristics and agent capability for mold assembly. Procedia Manufacturing, 51, pp. 179-
186.
73. Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140),
pp. 1-55.
74. Lin, F., Ye, L., Duffy, V. G., & Su, C. J. (2002). Developing virtual environments for industrial
training. Information Sciences, 140(1-2), pp. 153-170.
75. Lu, L., Megahed, F. M., Sesek, R. F., & Cavuoto, L. A. (2017). A survey of the prevalence of
fatigue, its precursors and individual coping mechanisms among US manufacturing workers.
Applied Ergonomics, 65, pp.139-151.
76. Lu, L., Xie, Z., Wang, H., Li, L., & Xu, X. (2022). Mental stress and safety awareness during
human-robot collaboration - Review. Applied Ergonomics, 105, 103832.
77. Malik, A. A. & Bilberg, A. (2019). Complexity-based task allocation in human-robot
collaborative assembly. Industrial Robot: the International Journal of Robotics Research and
Application, pp. 471-480.
78. Malik, A. A. & Bilberg, A. (2019). Developing a reference model for human-robot interaction.
International Journal on Interactive Design and Manufacturing (IJIDeM), 13, pp. 1541-1547.
79. Matheson, E., Minto, R., Zampieri, E. G., Faccio, M., & Rosati, G. (2019). Human-robot
collaboration in manufacturing applications: A review. Robotics, 8(4), p. 100.
80. Matsas, E., Vosniakos, G. C., & Batras, D. (2017). Effectiveness and acceptability of a virtual
environment for assessing human-robot collaboration in manufacturing. The International
Journal of Advanced Manufacturing Technology, 92(9), pp. 3903-3917.
81. McKeown, D., (2005). Candidates for within-vehicle auditory displays. In: Proceedings of
ICAD 05-11th Meeting of the International Conference on Auditory Display, pp.182-189.
82. McNeill, D. (1992). Hand and mind: What gestures reveal about thought. University of Chicago
Press.
83. Meshkati, N., Hancock, P., & Rahimi, M. (1992). Techniques in mental workload assessment.
In: J. Wilson & E. Corlett (Eds.), Evaluation of Human Work. A Practical Ergonomics
Methodology, pp. 605-627.
84. Mulder, G. & Mulder, L. J. (1981). Information processing and cardiovascular control.
Psychophysiology, 18(4), pp. 392-402.
85. Naceri, A., Chellali, R., Dionnet, F., & Toma, S. (2010). Depth perception within virtual
environments: comparison between two display technologies. International Journ. on Advances
in Intelligent Systems, 3(1-2), pp. 51-64.
86. Naderpour, M., Nazir, S., & Lu, J. (2015). The role of situation awareness in accidents of largescale
technological systems. Process Safety and Environmental Protection, 97, pp. 13-24.
87. Nazir, S., Colombo, S., & Manca, D. (2012). The role of situation awareness for the operators
of process industry. Chemical Engineering Transactions, 26, pp. 303-308.
88. Ogura, Y., Fujii, M., Nishijima, K., Murakami, H., & Sonehara, M. (2012). Applicability of
hand-guided robot for assembly-line work. Journal of Robotics and Mechatronics, 24(3), pp.
547-552.
89. OSHA and ANSI Safety Colors. (2022). Graphic Products. Inc,
https://www.graphicproducts.com/articles/osha-and-ansi-safety-colors/
90. Parasuraman, R. & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse.
Human Factors, 39(2), pp. 230-253.
91. Phillips, E., Ososky, S., Grove, J., & Jentsch, F. (2011). From tools to teammates: Toward the
development of appropriate mental models for intelligent robots. In: Proceedings of the Human
Factors and Ergonomics Society Annual Meeting, 55(1), pp.1481-1495. CA: SAGE Publications.
92. Pörtner, A., Schröder, L., Rasch, R., Sprute, D., Hoffmann, M., & König, M. (2018). The power
of color: A study on the effective use of colored light in human-robot interaction. In: 2018
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3395-3402.
IEEE.
93. Ragan, E. D., Bowman, D. A., Kopper, R., Stinson, C., Scerbo, S., & McMahan, R. P. (2015).
Effects of field of view and visual complexity on virtual reality training effectiveness for a visual
scanning task. IEEE Transactions on Visualization and Computer Graphics, 21(7), pp. 794-807.
94. Reid, G. B. & Nygren, T. E. (1988). The subjective workload assessment technique: A scaling
procedure for measuring mental workload. In: Advances in Psychology, 52, pp. 185-218.
95. Robotic Industries Association. (2012). ANSI/RIA R15. 06: 2012 Safety Requirements for
industrial robots and robot systems. Ann Arbor: Robotic Industries Association.
96. Rosenstrauch, M. J., Pannen, T. J., & Krüger, J. (2018). Human robot collaboration-using kinect
v2 for ISO/TS 15066 speed and separation monitoring. Procedia CIRP, 76, pp. 183-186.
97. Rout, A., Deepak, B. B. V. L., & Biswal, B. B. (2019). Advances in weld seam tracking
techniques for robotic welding: A Review. Robotics and Computer-Integrated Manufacturing,
56, pp. 12-37.
98. Rubio, S., Díaz, E., Martín, J., & Puente, J. M. (2004). Evaluation of subjective mental workload:
A comparison of SWAT, NASA‐TLX, and workload profile methods. Applied Psychology, 53(1),
pp. 61-86.
99. Schlenk, C. (2019). History of the DLR LWR. Institute of Robotics and Mechatronics.
https://www.dlr.de/rm/en/desktopdefault.aspx/tabid-12464/21732_read-44586/.
100. Schrepp, M., Hinderks, A., & Thomaschewski, J. (2017). Design and evaluation of a short
version of the user experience questionnaire (UEQ-S). International Journal of Interactive
Multimedia and Artificial Intelligence, 4 (6), pp. 103-108.
101. Shah, J., Wiken, J., Williams, B., & Breazeal, C. (2011). Improved human-robot team
performance using chaski, a human-inspired plan execution system. In: Proceedings of the 6th
International Conference on Human-Robot Interaction, pp. 29-36.
102. Sheridan, T. B. & Verplank, W. L. (1978). Human and Computer Control of Undersea
Teleoperators. Massachusetts Institute of Technology Man-Machine Systems Laboratory.
103. Sherwani, F., Asad, M. M., & Ibrahim, B. S. K. K. (2020). Collaborative robots and industrial
revolution 4.0 (ir 4.0). In: 2020 International Conference on Emerging Trends in Smart
Technologies (ICETST), pp. 1-5. IEEE.
104. Sneddon, A., Mearns, K., & Flin, R. (2006). Situation awareness and safety in offshore drill
crews. Cognition, Technology & Work, 8(4), pp. 255-267.
105. Song, S. & Yamada, S. (2018). Bioluminescence-inspired human-robot interaction: designing
expressive lights that affect human's willingness to interact with a robot. In: Proceedings of the
2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 224-232.
106. Stefanov, D. & Bien, Z. Z. (2004). Advances in Human-Friendly Robotic Technologies for
Movement Assistance/Movement Restoration for People with Disabilities. In: Advances in
Rehabilitation Robotics, pp. 3-23. Springer.
107. Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., & Goodrich, M. (2006).
Common metrics for human-robot interaction. In: Proceedings of the 1st ACM SIGCHI/SIGART
Conference on Human-robot Interaction, pp. 33-40.
108. Steuer, J., Biocca, F., & Levy, M. R. (1995). Defining virtual reality: Dimensions determining
telepresence. Communication in the Age of Virtual Reality, 33, pp. 37-39.
109. Taylor, R. M. (1990). Situation awareness rating technique (SART): The development of a tool
for aircrew systems design. In: Situational Awareness in Aerospace Operations (Chapter 3).
France: Neuilly sur-Seine, NATO-AGARD-CP-478.
110. Tan, J. T. C., Duan, F., Zhang, Y., & Arai, T. (2009). Extending task analysis in HTA to model
man-machine collaboration in cell production. In: 2008 IEEE International Conference on
Robotics and Biomimetics, pp. 542-547. IEEE.
111. Tang, K. H., Ho, C. F., Mehlich, J., & Chen, S. T. (2020). Assessment of handover prediction
models in estimation of cycle times for manual assembly tasks in a human-robot collaborative
environment. Applied Sciences, 10(2), pp. 556.
112. Tcha-Tokey, K., Christmann, O., Loup-Escande, E., & Richir, S. (2016). Proposition and
validation of a questionnaire to measure the user experience in immersive virtual environments.
International Journal of Virtual Reality, 16(1), pp. 33-48.
113. Treisman, A. & Souther, J. (1985). Search asymmetry: a diagnostic for preattentive processing
of separable features. Journal of Experimental Psychology: General, 114(3), pp. 285.
114. Universal robots (2020). Universal robots reaches industry milestone with 50,000 collaborative
robots sold. Universal Robot. https://www.universal-robots.com/about-universal-robots/newscentre/
universal-robots-reaches-industry-milestone-with-50-000-collaborative-robots-sold/
115. Villani, V., Pini, F., Leali, F., & Secchi, C. (2018). Survey on human-robot collaboration in
industrial settings: Safety, intuitive interfaces and applications. Mechatronics, 55, pp. 248-266.
116. Vojić, S. (2020). Applications of collaborative industrial robots. Machines. Technologies.
Materials., 14(3), pp. 96-99.
117. Vysocky, A. & Novak, P. (2016). Human-robot collaboration in industry. MM Science Journal,
9(2), pp. 903-906.
118. Walker, M., Hedayati, H., Lee, J., & Szafir, D. (2018). Communicating robot motion intent with
augmented reality. In: Proceedings of the 2018 ACM/IEEE International Conference on Human-
Robot Interaction, pp. 316-324.
119. Weiss, A. Wortmeier, A. K., & Kubicek, B. (2021). Cobots in industry 4.0: A roadmap for future
practice studies on human-robot collaboration. IEEE Transactions on Human-Machine Systems,
51(4), pp. 335-345.
120. Weiss, A., Wurhofer, D., Lankes, M., & Tscheligi, M. (2009). Autonomous vs. tele-operated:
How people perceive human-robot collaboration with HRP-2. In: Proceedings of the 4th
ACM/IEEE International Conference on Human Robot Interaction, pp. 257-258.
121. Wickens, C. D. & Carswell, C. M. (1984). Information processing. Handbook of Human Factors
and Ergonomics, pp. 114-158.
122. Wickens, C. D., Helton, W. S., Hollands, J. G., & Banbury, S. (2021). Engineering Psychology
and Human Performance. Routledge.
123. Witmer, B. G., Jerome, C. J., & Singer, M. J. (2005). The factor structure of the presence
questionnaire. Presence: Teleoperators & Virtual Environments, 14(3), pp. 298-312.
124. Xiao, M. (2022). Collaborative market rebounds in 2021. Control Engineering.
https://www.controleng.com/articles/collaborative-market-rebounds-in-2021/.
125. Yang, J., Sasikumar, P., Bai, H., Barde, A., Sörös, G., & Billinghurst, M. (2020). The effects of
spatial auditory and visual cues on mixed reality remote collaboration. Journal on Multimodal
User Interfaces, 14, pp. 337-352.

(此全文20280823後開放外部瀏覽)
電子全文
摘要
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
* *