İnsansız Hava Araçlarında (İHA) İnsan Faktörlerinin Etkisine Dair Literatürün Sistematik Olarak Analizi ve Sınıflandırılması

Year 2020, , 71 - 81, 28.12.2020

Hüseyin Erbil Özyörük

https://doi.org/10.30518/jav.777483

Cited By: 2

Abstract

Askeri ve sivil alanlarda insansız hava araçlarının (İHA) kullanımı gün geçtikçe artmaktadır. Bu artan kullanım, kaza ve kırımlarla ilgili riskleri ortaya çıkarmaktadır. İnsan faktörleri havacılıktaki kaza ve kırımların en önemli nedenleri arasındadır. Bu faktörlerin insansız hava araçları üzerindeki etkisini anlamak, kaza ve kırımları önlemek açısından hayati öneme sahiptir. Bu çalışmada, insansız hava araçlarında insan faktörleri hakkındaki literatür sistematik olarak gözden geçirilmekte ve sınıflandırılmaktadır. Yapılan sınıflandırma sonucunda hangi konularda çalışmaların eksik veya yetersiz olduğunun anlaşılması amaçlanmaktadır. Bu şekilde, gelecekte yapılabilecek araştırmalar hakkında da önerilerde bulunulmaya çalışılmaktadır

Keywords

İnsansız Hava Araçları (İHA), İnsan Faktörleri, Literatür Taraması, Sınıflandırma

Systematic Analysis and Classification of the Literature Regarding the Impact of Human Factors On Unmanned Aerial Vehicles (UAV)

Abstract

The use of unmanned aerial vehicles (UAV) in military and civilian areas is increasing day by day. This increased use poses risks related to accidents and incidents. Human factors are among the most important causes of accidents and incidents in aviation. Understanding the impact of these factors on unmanned aerial vehicles is vital to prevent the accidents and incidents. In this study, literature on human factors in unmanned aerial vehicles is systematically reviewed and classified. As a result of the classification made, it is aimed to understand which subjects are missing or inadequate. In this way, it is also attempted to make suggestions about future studies.

Keywords

Unmanned Aerial Vehicles (UAV), Human Factors, Literatür Taraması, Classification

References

• [1] A. C. Watts, V. G. Ambrosia, and E. A. Hinkley, “Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use,” Remote. Sens., 2012, doi: 10.3390/rs4061671.
• [2] Unmanned aircraft systems: UAS. Montréal: International Civil Aviation Organization, 2011.
• [3] N. Can and M. Kahveci̇, “İnsansız Hava Araçları: Tarihçesi, Tanımı, Dünyada Ve Türkiye Deki Yasal Durumu,” Scitech, vol. 5, no. 4, pp. 511–535, Dec. 2017, doi: 10.15317/Scitech.2017.109.
• [4] “List of unmanned aerial vehicle applications,” Wikipedia. Jun. 29, 2020, Accessed: Aug. 05, 2020. [Online]. Available: https://en.wikipedia.org/w/index.php?title=List_of_unmanned_aerial_vehicle_applications&oldid=965129299.
• [5] “UAS by the Numbers.” https://www.faa.gov/uas/resources/by_the_numbers/ (accessed Aug. 05, 2020).
• [6] “Military Drones Market Size, Growth, Trend and Forecast to 2025 | MarketsandMarkets.” https://www.marketsandmarkets.com/Market-Reports/military-drone-market-221577711.html (accessed Aug. 05, 2020).
• [7] “Unmanned Aerial Vehicle Market, UAV Size, Share, system and Industry Analysis and Market Forecast to 2024 | MarketsandMarketsTM.” https://www.marketsandmarkets.com/Market-Reports/unmanned-aerial-vehicles-uav-market-662.html (accessed Aug. 05, 2020).
• [8] Federal Aviation Administration, “Investigation of UAS Accidents and Incidents,” Sep. 2017, Accessed: Aug. 05, 2020. [Online]. Available: https://sites.nationalacademies.org/cs/groups/depssite/documents/webpage/deps_183066.pdf.
• [9] X. Zhang, G. Jia, and Z. Chen, “The Literature Review of Human Factors Research on Unmanned Aerial Vehicle – What Chinese Researcher Need to Do Next?,” in Cross-Cultural Design. Methods, Tools, and Users, Cham, 2018, pp. 375–384, doi: 10.1007/978-3-319-92141-9_29.
• [10] E. L. Wiener and D. C. Nagel, Human Factors in Aviation. Academic Press, 2014.
• [11] Y.-H. Chang and Y.-C. Wang, “Significant human risk factors in aircraft maintenance technicians,” Safety Science, vol. 48, no. 1, Jan. 2010, Accessed: Aug. 05, 2020. [Online]. Available: https://trid.trb.org/view/904279.
• [12] S. Shappell, C. Detwiler, K. Holcomb, C. Hackworth, A. Boquet, and D. A. Wiegmann, “Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system,” Hum Factors, vol. 49, no. 2, pp. 227–242, Apr. 2007, doi: 10.1518/001872007X312469.
• [13] R. W. Wohleber et al., “Vigilance and Automation Dependence in Operation of Multiple Unmanned Aerial Systems (UAS): A Simulation Study,” Hum Factors, vol. 61, no. 3, pp. 488–505, May 2019, doi: 10.1177/0018720818799468.
• [14] S. Kim and J. Irizarry, “Framework for Human Performance Analysis in Unmanned Aircraft System (UAS) Operations in Dynamic Construction Environment,” pp. 33–42, Mar. 2018, doi: 10.1061/9780784481264.004.
• [15] C. R. Balog, B. A. Terwilliger, D. A. Vincenzi, and D. C. Ison, “Examining Human Factors Challenges of Sustainable Small Unmanned Aircraft System (sUAS) Operations,” in Advances in Human Factors in Robots and Unmanned Systems, Cham, 2017, pp. 61–73, doi: 10.1007/978-3-319-41959-6_6.
• [16] B. Walters, J. French, and M. J. Barnes, “Modeling the effects of crew size and crew fatigue on the control of tactical unmanned aerial vehicles (TUAVs),” in 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165), Orlando, FL, USA, 2000, vol. 1, pp. 920–924, doi: 10.1109/WSC.2000.899893.
• [17] J. T. Coyne, C. Sibley, S. Sherwood, C. K. Foroughi, T. Olson, and E. Vorm, “Assessing Workload with Low Cost Eye Tracking During a Supervisory Control Task,” in Augmented Cognition. Neurocognition and Machine Learning, vol. 10284, D. D. Schmorrow and C. M. Fidopiastis, Eds. Cham: Springer International Publishing, 2017, pp. 139–147.
• [18] F. Bazzano et al., “Mental Workload Assessment for UAV Traffic Control Using Consumer-Grade BCI Equipment,” in Intelligent Human Computer Interaction, vol. 10688, P. Horain, C. Achard, and M. Mallem, Eds. Cham: Springer International Publishing, 2017, pp. 60–72.
• [19] F. Honecker and A. Schulte, “Automated Online Determination of Pilot Activity Under Uncertainty by Using Evidential Reasoning,” in Engineering Psychology and Cognitive Ergonomics: Cognition and Design, Cham, 2017, pp. 231–250, doi: 10.1007/978-3-319-58475-1_18.
• [20] B. Piuzzi, A. Cont, and M. Balerna, “The workload sensing for the human machine interface of Unmanned Air Systems,” in 2014 IEEE Metrology for Aerospace (MetroAeroSpace), Benevento, Italy, May 2014, pp. 50–55, doi: 10.1109/MetroAeroSpace.2014.6865893.
• [21] Z. Yun, Y. Peiyang, W. Lujun, and Y. Juan, “Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering,” Journal of Systems Engineering and Electronics, vol. 29, no. 3, pp. 530–538, Jun. 2018, doi: 10.21629/JSEE.2018.03.10.
• [22] J. T. Platts, “Autonomy in unmanned air vehicles,” Aeronaut. j., vol. 110, no. 1104, pp. 97–105, Feb. 2006, doi: 10.1017/S0001924000001044.
• [23] T. Shmelova, Y. Sikirda, and Y. Kovalyov, “Decision making by remotely piloted aircraft system’s operator,” in 2017 IEEE 4th International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), Kiev, Oct. 2017, pp. 92–99, doi: 10.1109/APUAVD.2017.8308784.
• [24] O. McAree, J. M. Aitken, and S. M. Veres, “Quantifying situation awareness for small unmanned aircraft: Towards routine Beyond Visual Line of Sight operations,” Aeronaut. j., vol. 122, no. 1251, pp. 733–746, May 2018, doi: 10.1017/aer.2018.14.
• [25] A. P. Tvaryanas and G. D. MacPherson, “Fatigue in pilots of remotely piloted aircraft before and after shift work adjustment,” Aviat Space Environ Med, vol. 80, no. 5, pp. 454–461, May 2009, doi: 10.3357/asem.2455.2009.
• [26] N. J. McNeese, M. Demir, N. J. Cooke, and C. Myers, “Teaming With a Synthetic Teammate: Insights into Human-Autonomy Teaming,” Hum Factors, vol. 60, no. 2, pp. 262–273, Mar. 2018, doi: 10.1177/0018720817743223.
• [27] L. Gong, S. Zhang, P. Tang, and Y. Lu, “An integrated graphic–taxonomic–associative approach to analyze human factors in aviation accidents,” Chinese Journal of Aeronautics, vol. 27, no. 2, pp. 226–240, Apr. 2014, doi: 10.1016/j.cja.2014.02.002.
• [28] A. P. Tvaryanas and W. T. Thompson, “Recurrent error pathways in HFACS data: analysis of 95 mishaps with remotely piloted aircraft,” Aviat Space Environ Med, vol. 79, no. 5, pp. 525–532, May 2008, doi: 10.3357/asem.2002.2008.
• [29] G. Wild, K. Gavin, J. Murray, J. Silva, and G. Baxter, “A Post-Accident Analysis of Civil Remotely-Piloted Aircraft System Accidents and Incidents,” J.Aerosp. Technol. Manag., vol. 9, no. 2, pp. 157–168, Apr. 2017, doi: 10.5028/jatm.v9i2.701.
• [30] G. Wild, J. Murray, and G. Baxter, “Exploring Civil Drone Accidents and Incidents to Help Prevent Potential Air Disasters,” Aerospace, vol. 3, no. 3, p. 22, Jul. 2016, doi: 10.3390/aerospace3030022.
• [31] A. P. Tvaryanas, W. T. Thompson, and S. H. Constable, “Human factors in remotely piloted aircraft operations: HFACS analysis of 221 mishaps over 10 years,” Aviat Space Environ Med, vol. 77, no. 7, pp. 724–732, Jul. 2006.
• [32] Y. Lu, Y. Qian, H. Huangfu, S. Zhang, and S. Fu, “Ensuring the Safety Sustainability of Large UAS: Learning from the Maintenance Risk Dynamics of USAF MQ-1 Predator Fleet in Last Two Decades,” Sustainability, vol. 11, no. 4, p. 1129, Feb. 2019, doi: 10.3390/su11041129.
• [33] L. Castano and H. Xu, “Safe decision making for risk mitigation of UAS,” in 2019 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, Jun. 2019, pp. 1326–1335, doi: 10.1109/ICUAS.2019.8797774.
• [34] O. Fontaine, A. Martinetti, and S. Michaelides-Mateou, “Remote pilot aircraft system (RPAS): Just culture, human factors and learnt lessons,” vol. 53, pp. 205–210, Jan. 2016, doi: 10.3303/CET1653035.
• [35] J. D. Stevenson, S. O’Young, and L. Rolland, “Enhancing the Visibility of Small Unmanned Aerial Vehicles,” Procedia Manufacturing, vol. 3, pp. 944–951, 2015, doi: 10.1016/j.promfg.2015.07.143.
• [36] D. Dores, A. Baltazar, T. Cabral, I. Machado, and P. Gonçalves, “Safety Issues of the Portuguese Military Remotely Piloted Aircraft Systems,” in A World with Robots: International Conference on Robot Ethics: ICRE 2015, M. I. Aldinhas Ferreira, J. Silva Sequeira, M. O. Tokhi, E. E. Kadar, and G. S. Virk, Eds. Cham: Springer International Publishing, 2017, pp. 185–198.
• [37] Y. Lim et al., “A novel simulation environment for cognitive human factors engineering research,” in 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, 2017, pp. 1–8, doi: 10.1109/DASC.2017.8102126.
• [38] T. F. Shmelova and O. V. Shostak, “System for monitoring external pilot emotional state during UAV control,” in 2015 IEEE International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), Kyiv, Ukraine, Oct. 2015, pp. 332–335, doi: 10.1109/APUAVD.2015.7346634.
• [39] B. Stark, T. Patel, and Y. Chen, “HRV monitoring for human factor research in UAS,” Aug. 2013, vol. 4, doi: 10.1115/DETC2013-12746.
• [40] A. Hocraffer and C. S. Nam, “A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management,” Applied Ergonomics, vol. 58, pp. 66–80, Jan. 2017, doi: 10.1016/j.apergo.2016.05.011.
• [41] C. Ruf and P. Stütz, “Model-Driven Payload Sensor Operation Assistance for a Transport Helicopter Crew in Manned–Unmanned Teaming Missions: Assistance Realization, Modelling Experimental Evaluation of Mental Workload,” in Engineering Psychology and Cognitive Ergonomics: Performance, Emotion and Situation Awareness, Cham, 2017, pp. 51–63, doi: 10.1007/978-3-319-58472-0_5.
• [42] D. Donath and A. Schulte, “Behavior Based Task and High Workload Determination of Pilots Guiding Multiple UAVs,” Procedia Manufacturing, vol. 3, pp. 990–997, 2015, doi: 10.1016/j.promfg.2015.07.156.
• [43] F. Fortmann, H. Muller, A. Ludtke, and S. Boll, “Expert-based design and evaluation of an ambient light display to improve monitoring performance during multi-UAV supervisory control,” in 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, Orlando, FL, USA, Mar. 2015, pp. 28–34, doi: 10.1109/COGSIMA.2015.7107971.
• [44] M. Kriegel, C. Meitinger, and A. Schulte, “Operator Assistance and Semi-autonomous Functions as Key Elements of Future Systems for Multiple Uav Guidance,” in Engineering Psychology and Cognitive Ergonomics, Berlin, Heidelberg, 2007, pp. 705–715, doi: 10.1007/978-3-540-73331-7_77.
• [45] S. R. Dixon, C. D. Wickens, and D. Chang, “Mission Control of Multiple Unmanned Aerial Vehicles: A Workload Analysis,” Hum Factors, vol. 47, no. 3, pp. 479–487, Sep. 2005, doi: 10.1518/001872005774860005.
• [46] H. Ruff, S. Narayanan, and M. Draper, “Human Interaction with Levels of Automation and Decision-Aid Fidelity in the Supervisory Control of Multiple Simulated Unmanned Air Vehicles,” Presence, vol. 11, pp. 335–351, Aug. 2002, doi: 10.1162/105474602760204264.
• [47] K.-P. L. Vu, R. C. Rorie, L. Fern, and R. J. Shively, “Human Factors Contributions to the Development of Standards for Displays of Unmanned Aircraft Systems in Support of Detect-and-Avoid,” Hum Factors, vol. 62, no. 4, pp. 505–515, Jun. 2020, doi: 10.1177/0018720820916326.
• [48] W. Zhang, D. Feltner, J. Shirley, D. Kaber, and M. S. Neubert, “Enhancement and Application of a UAV Control Interface Evaluation Technique: Modified GEDIS-UAV,” J. Hum.-Robot Interact., vol. 9, no. 2, pp. 1–20, Feb. 2020, doi: 10.1145/3368943.
• [49] K. J. Monk and Z. Roberts, “Maintain and Regain Well Clear: Maneuver Guidance Designs for Pilots Performing the Detect-and-Avoid Task,” in Advances in Human Factors in Robots and Unmanned Systems, vol. 595, J. Chen, Ed. Cham: Springer International Publishing, 2018, pp. 64–74.
• [50] A. P. Vinod, T. H. Summers, and M. M. K. Oishi, “User-interface design for MIMO LTI human-automation systems through sensor placement,” in 2016 American Control Conference (ACC), Boston, MA, USA, Jul. 2016, pp. 5276–5283, doi: 10.1109/ACC.2016.7526496.
• [51] E. L. Papautsky, C. Dominguez, R. Strouse, and B. Moon, “Integration of Cognitive Task Analysis and Design Thinking for Autonomous Helicopter Displays,” Journal of Cognitive Engineering and Decision Making, vol. 9, no. 4, pp. 283–294, Dec. 2015, doi: 10.1177/1555343415602624.
• [52] D. A. Vincenzi, “Unmanned Aerial System (UAS) Human-machine Interfaces: New Paradigms in Command and Control,” Procedia Manufacturing, vol. 3, pp. 920–927, 2015.
• [53] J. M. Peschel and R. R. Murphy, “On the Human–Machine Interaction of Unmanned Aerial System Mission Specialists,” IEEE Trans. Human-Mach. Syst., vol. 43, no. 1, pp. 53–62, Jan. 2013, doi: 10.1109/TSMCC.2012.2220133.
• [54] D. Gunn, J. Warm, W. Nelson, R. Bolia, D. Schumsky, and K. Corcoran, “Target Acquisition With UAVs: Vigilance Displays and Advanced Cuing Interfaces,” Human factors, vol. 47, pp. 488–97, Feb. 2005, doi: 10.1518/001872005774859971.
• [55] T. H. Kamine and G. A. Bendrick, “Visual display angles of conventional and a remotely piloted aircraft,” Aviat Space Environ Med, vol. 80, no. 4, pp. 409–413, Apr. 2009, doi: 10.3357/asem.2337.2009.
• [56] Y. Lim et al., “Avionics Human-Machine Interfaces and Interactions for Manned and Unmanned Aircraft,” Progress in Aerospace Sciences, vol. 102, pp. 1–46, Oct. 2018, doi: 10.1016/j.paerosci.2018.05.002.
• [57] Y. Lim, S. Ramasamy, A. Gardi, T. Kistan, and R. Sabatini, “Cognitive Human-Machine Interfaces and Interactions for Unmanned Aircraft,” J Intell Robot Syst, vol. 91, no. 3–4, pp. 755–774, Sep. 2018, doi: 10.1007/s10846-017-0648-9.
• [58] A.-Q. V. Dao et al., “Evaluation of Early Ground Control Station Configurations for Interacting with a UAS Traffic Management (UTM) System,” in Advances in Human Factors in Robots and Unmanned Systems, vol. 595, J. Chen, Ed. Cham: Springer International Publishing, 2018, pp. 75–86.
• [59] J. Haber and J. Chung, “Assessment of UAV operator workload in a reconfigurable multi-touch ground control station environment,” J. Unmanned Veh. Sys., vol. 4, no. 3, pp. 203–216, Sep. 2016, doi: 10.1139/juvs-2015-0039.
• [60] A. Hobbs and B. Lyall, “Human Factors Guidelines for Unmanned Aircraft Systems,” Ergonomics in Design, vol. 24, no. 3, pp. 23–28, Jul. 2016, doi: 10.1177/1064804616640632.
• [61] P. Dumas, A. E. F. Seghrouchni, and P. Taillibert, “Aerial: A Framework to Support Human Decision Making in a Constrained Environment,” in 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, Athens, Nov. 2012, pp. 626–633, doi: 10.1109/ICTAI.2012.90.
• [62] P. Oppold, M. Rupp, M. Mouloua, P. A. Hancock, and J. Martin, “Design considerations to improve cognitive ergonomic issues of unmanned vehicle interfaces utilizing video game controllers,” Work, vol. 41, pp. 5609–5611, 2012, doi: 10.3233/WOR-2012-0896-5609.
• [63] L. Damilano, G. Guglieri, F. Quagliotti, and I. Sale, “FMS for Unmanned Aerial Systems: HMI Issues and New Interface Solutions,” J Intell Robot Syst, vol. 65, no. 1–4, pp. 27–42, Jan. 2012, doi: 10.1007/s10846-011-9567-3.
• [64] G. L. Calhoun, M. H. Draper, M. F. Abernathy, M. Patzek, and F. Delgado, “Synthetic vision system for improving unmanned aerial vehicle operator situation awareness,” Orlando, FL, May 2005, pp. 219–230, doi: 10.1117/12.603421.
• [65] V. Rodríguez-Fernández, H. D. Menéndez, and D. Camacho, “Analysing temporal performance profiles of UAV operators using time series clustering,” Expert Systems with Applications, vol. 70, pp. 103–118, Mar. 2017, doi: 10.1016/j.eswa.2016.10.044.
• [66] S. Huber and P. Wellig, “Human factors of target detection tasks within heavily cluttered video scenes,” in Target and Background Signatures, Oct. 2015, vol. 9653, p. 96530R, doi: 10.1117/12.2193148.
• [67] A. P. Tvaryanas, “Human Systems Integration in Remotely Piloted Aircraft Operations,” vol. 77, no. 12, p. 5, 2006.
• [68] I. R. McAndrew, A. Glassman, D. Bourdeau, R. Clint, and E. Navarro, “Unmanned aerial systems operational challenges when used between regions where English is not widely spoken or understood: Human factors communication,” in 2016 International Conference on Robotics and Automation Engineering (ICRAE), Jeju, South Korea, Aug. 2016, pp. 53–57, doi: 10.1109/ICRAE.2016.7738788.
• [69] Z. Dudas, A. Restas, S. Szabó, K. Domján, and D. Pál, “Human Factor Analysis in Unmanned Aerial Vehicle (UAV) Operations,” 2016, pp. 47–58.
• [70] X. Li, H. Pei, F. Sha, X. Zhang, and W. Chen, “Testing Research on the Professional Ability of Multi-axial UAV Operators Based on Eye-movement Technology,” presented at the 2015 International Forum on Energy, Environment Science and Materials, Shenzen, China, 2015, doi: 10.2991/ifeesm-15.2015.308.
• [71] P. McCarthy and G. K. Teo, “Assessing Human-Computer Interaction of Operating Remotely Piloted Aircraft Systems (RPAS) in Attitude (ATTI) Mode,” in Engineering Psychology and Cognitive Ergonomics: Cognition and Design, Cham, 2017, pp. 251–265, doi: 10.1007/978-3-319-58475-1_19.
• [72] T. R. Carretta and R. E. King, “Personnel Selection Influences on Remotely Piloted Aircraft Human-System Integration,” Aerospace Medicine and Human Performance, vol. 86, no. 8, pp. 736–741, Aug. 2015, doi: 10.3357/AMHP.4287.2015.
• [73] J. Shmelev, “Simulator training optimization of UAV external pilots,” in 2014 IEEE 3rd International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), Kiev, Ukraine, Oct. 2014, pp. 75–78, doi: 10.1109/MSNMC.2014.6979734.
• [74] J. T. Hing and P. Y. Oh, “Development of an Unmanned Aerial Vehicle Piloting System with Integrated Motion Cueing for Training and Pilot Evaluation,” J Intell Robot Syst, vol. 54, no. 1–3, pp. 3–19, Mar. 2009, doi: 10.1007/s10846-008-9252-3.
• [75] J. Hing and P. Y. Oh, “Integrating Motion Platforms With Unmanned Aerial Vehicles to Improve Control, Train Pilots and Minimize Accidents,” in Volume 2: 32nd Mechanisms and Robotics Conference, Parts A and B, Brooklyn, New York, USA, Jan. 2008, pp. 867–875, doi: 10.1115/DETC2008-49602.
• [76] H. Fesenko and V. Kharchenko, “Determining the Optimum Number of Single Operator Controlled Unmanned Aerial Vehicles for NPP Monitoring Missions: Human Error Issues,” in 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine, Oct. 2018, pp. 711–714, doi: 10.1109/INFOCOMMST.2018.8632029.
• [77] T. Porat, T. Oron-Gilad, M. Rottem-Hovev, and J. Silbiger, “Supervising and Controlling Unmanned Systems: A Multi-Phase Study with Subject Matter Experts,” Front. Psychol., vol. 7, May 2016, doi: 10.3389/fpsyg.2016.00568.
• [78] A. C. Trujillo et al., “Operator Informational Needs for Multiple Autonomous Small Vehicles,” Procedia Manufacturing, vol. 3, pp. 936–943, 2015, doi: 10.1016/j.promfg.2015.07.141.
• [79] C. C. Murray and W. Park, “Incorporating Human Factor Considerations in Unmanned Aerial Vehicle Routing,” IEEE Trans. Syst. Man Cybern, Syst., vol. 43, no. 4, pp. 860–874, Jul. 2013, doi: 10.1109/TSMCA.2012.2216871.
• [80] C. Kurkcu, H. Erhan, and S. Umut, “Human Factors Concerning Unmanned Aircraft Systems in Future Operations,” J Intell Robot Syst, vol. 65, no. 1–4, pp. 63–72, Jan. 2012, doi: 10.1007/s10846-011-9592-2.
• [81] V. Rodriguez-Fernandez, A. Gonzalez-Pardo, and D. Camacho, “Automatic Procedure Following Evaluation Using Petri Net-Based Workflows,” IEEE Trans. Ind. Inf., vol. 14, no. 6, pp. 2748–2759, Jun. 2018, doi: 10.1109/TII.2017.2779177.