İnsansız Hava Araçları (İHA) Mürettebat Performansı ve Emniyetin Artırılması: Teknoloji ve İnovasyon Yönetimi Perspektifi

Year 2024, , 130 - 153, 30.07.2024

Ayse Aslı Yılmaz

https://doi.org/10.54733/smar.1512893

Abstract

İnsansız hava araçlarının (İHA) çeşitli sektörlere entegrasyonu, operasyonel verimliliği, emniyeti ve görev başarısını sağlamak için insan faktörlerinin optimize edilmesinin önemini vurgulamaktadır. Bu çalışma, İHA operasyonlarında insan faktörleri üzerine yapılan literatürün kapsamlı bir bibliyometrik analizini sunmakta ve bilişsel iş yükü, durumsal farkındalık, karar verme, ergonomik tasarım ve insan-makine etkileşimi konularına odaklanmaktadır. Analiz, 2000-2023 yılları arasındaki yayınları kapsayacak şekilde Web of Science kullanılarak gerçekleştirilmiştir. Ana bulgular arasında, son on yılda araştırma çıktılarında önemli bir artış olduğu, İHA teknolojisi ve insan faktörlerine olan artan ilgi ve yatırımı vurgulayan sonuçlar yer almaktadır. Rosenstein (2006), Patterson (2010), Reason (1990), Wiegmann (2001) ve Shappell (2007) gibi etkili yazarlar ile Beijing University of Posts and Telecommunications, Southeast University China, Xidian University, ve Nanjing University of Aeronautics and Astronautics gibi kurumlar, bu alanda lider olarak ortaya çıkmış ve ergonomik tasarım ve karar verme süreçlerinde ilerlemelere katkıda bulunmuşlardır. Özellikle, İHA operatörlerinin uzun vadeli bilişsel iş yükü etkilerini ele alan kapsamlı çalışmaların ve İHA operasyon ortamlarına özel olarak uyarlanmış standart ergonomik kılavuzların geliştirilmesinin eksikliği dikkat çekicidir. Gelişmiş insan-makine etkileşimi teknolojilerinin entegrasyonu halen yeterince araştırılmamış olup, bu alanda daha fazla araştırmaya ihtiyaç olduğunu göstermektedir. Bu boşlukları vurgulayan analiz, mevcut araştırma dinamiklerini anlayışlı bir şekilde sunarak İHA operatörleri, düzenleyiciler ve politika yapıcılar için değerli çıkarımlar sağlamaktadır. Bu bulgular, alanın ilerlemesi ve İHA operasyonlarında ekip performansı ve güvenliği artırmaya yönelik gelecekteki araştırma girişimlerine rehberlik etmek açısından önemlidir.

Keywords

insansız hava araçları, inovasyon yönetimi, insan faktörleri, mürettebat performansı, emniyet

Enhancing UAV Crew Performance and Safety: A Technology and Innovation Management Perspective

Abstract

The integration of Unmanned aerial vehicles (UAVs) into various sectors underscores the importance of optimizing human factors to ensure operational efficiency, safety, and mission success. This study presents a comprehensive bibliometric analysis of the literature on human factors in UAV operations, focusing on cognitive workload, situational awareness, decision-making, ergonomic design, and human-machine interaction. The analysis was conducted using the WoS, covering publications from 2000 to 2023. Key findings include a significant increase in research output over the last decade, highlighting the growing interest and investment in UAV technology and human factors. Influential authors such as Rosenstein (2006), Patterson (2010), Reason (1990), Wiegmann (2001), and Shappell (2007), along with institutions like Beijing University of Posts and Telecommunications, Southeast University China, Xidian University, and Nanjing University of Aeronautics and Astronautics, have emerged as leaders in this field, contributing to advancements in ergonomic design and decision-making processes. Notably, there is a lack of comprehensive studies addressing the long-term cognitive workload effects on UAV operators and the development of standardized ergonomic guidelines tailored specifically for UAV operation environments. The integration of advanced human-machine interaction technologies remains underexplored, indicating a need for further research in this area. By highlighting these gaps, the analysis provides a nuanced understanding of current research dynamics, offering valuable implications for UAV operators, regulators, and policymakers. These findings are pivotal for advancing the field and guiding future research initiatives aimed at enhancing crew performance and safety in UAV operations.

Keywords

unmanned aerial vehicles, innovation management, human factors, crew performance, safety

References

• Aksnes, D., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. SAGE Open, 9(1). https://doi.org/10.1177/2158244019829575
• Alqurashi, F. S., Trichili, A., Saeed, N., Ooi, B. S., & Alouini, M. S. (2022). Maritime communications: A survey on enabling technologies, opportunities, and challenges. IEEE Internet of Things Journal, 10(4), 3525-3547. https://doi.org/10.1109/JIOT.2022.3219674
• Chen, P., Pei, J., Lu, W., & Li, M. (2022). A deep reinforcement learning based method for real-time path planning and dynamic obstacle avoidance. Neurocomputing, 497, 64-75. https://doi.org/10.1016/j.neucom.2022.05.006
• De Almeida, D. R. A., Broadbent, E. N., Ferreira, M. P., Meli, P., Zambrano, A. M. A., Gorgens, E. B., Resende, A. F., de Almeida, C. T., do Amaral, C. H., Corte, A. P. D., Silva, C. A., Romanelli, F. P., Prata, G. A., Papa, D. D. A., Stark, S. C., Valbuena, R., Nelson, B. W., Guillemot, J., Féret, J. P., Chazdon, R., & Brancalion, P. H. (2021). Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sensing of Environment, 264, 112582. https://doi.org/10.1016/j.rse.2021.112582
• Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
• Geraci, G., Garcia-Rodriguez, A., Giordano, L. G., López-Pérez, D., & Björnson, E. (2018). Understanding UAV cellular communications: From existing networks to massive MIMO. IEEE Access, 6, 67853-67865. https://doi.org/10.1109/ACCESS.2018.2876700
• Gong, X., Wang, L., Mou, Y., Wang, H., Wei, X., Zheng, W., & Yin, L. (2022). Improved four-channel PBTDPA control strategy using force feedback bilateral teleoperation system. International Journal of Control, Automation and Systems, 20(3), 1002-1017. https://doi.org/10.1007/s12555-021-0096-y
• Grlj, C. G., Krznar, N., & Pranjić, M. (2022). A decade of UAV docking stations: A brief overview of mobile and fixed landing platforms. Drones, 6(1), 17. https://doi.org/10.3390/drones6010017
• Lu, S., Ban, Y., Zhang, X., Yang, B., Liu, S., Yin, L., & Zheng, W. (2022). Adaptive control of time delay teleoperation system with uncertain dynamics. Frontiers in Neurorobotics, 16, 928863. https://doi.org/10.3389/fnbot.2022.928863
• Lv, Z., Li, Y., Feng, H., & Lv, H. (2021). Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 23(9), 16666-16675. https://doi.org/10.1109/TITS.2021.3113779
• McBurney, M. K., & Novak, P. L. (2002). What is bibliometrics and why should you care?. In Proceedings, IEEE international professional communication conference (pp. 108-114). IEEE. https://doi.org/10.1109/IPCC.2002.1049094
• Moed, H. F., Colledge, L., Reedijk, J., Moya-Anegon, F., Guerrero-Bote, V., Plume, A., & Amin, M. (2012). Citation-based metrics are appropriate tools in journal assessment provided that they are accurate and used in an informed way. Scientometrics, 92(2), 367-376. https://doi.org/10.1007/s11192-012-0679-8
• Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336-341. https://doi.org/10.7326/0003-4819-151-4-200908180-00135
• Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1-9. https://doi.org/10.1186/2046-4053-4-1
• Mohsan, S. A. H., Khan, M. A., Alsharif, M. H., Uthansakul, P., & Solyman, A. A. (2022). Intelligent reflecting surfaces assisted UAV communications for massive networks: current trends, challenges, and research directions. Sensors, 22(14), 5278. https://doi.org/10.3390/s22145278
• Mozaffari, M., Saad, W., Bennis, M., Nam, Y. H., & Debbah, M. (2019). A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE Communications Surveys & Tutorials, 21(3), 2334-2360. https://doi.org/10.1109/COMST.2019.2902862
• Page, M., Mckenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., Mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S., Chou, R., Glanville, J., Grimshaw, J., Hróbjartsson, A., Lalu, M., Li, T., Loder, E., Mayo-Wilson, E., Mcdonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, A. C., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10, 8. https://doi.org/10.1186/s13643-021-01626-4
• Ponomariov, B., & Boardman, C. (2016). What is co-authorship?. Scientometrics, 109, 1939-1963. https://doi.org/10.1007/s11192-016-2127-7
• Poudel, S., & Moh, S. (2022). Task assignment algorithms for unmanned aerial vehicle networks: A comprehensive survey. Vehicular Communications, 35, 100469. https://doi.org/10.1016/j.vehcom.2022.100469
• Rovira-Sugranes, A., Razi, A., Afghah, F., & Chakareski, J. (2022). A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook. Ad Hoc Networks, 130, 102790. https://doi.org/10.1016/j.adhoc.2022.102790
• Sharma, M., Gupta, A., Gupta, S. K., Alsamhi, S. H., & Shvetsov, A. V. (2022). Survey on unmanned aerial vehicle for Mars exploration: Deployment use case. Drones, 6(1), 4. https://doi.org/10.3390/drones6010004
• Shoufan, A., Al-Angari, H. M., Sheikh, M. F. A., & Damiani, E. (2018). Drone pilot identification by classifying radio-control signals. IEEE Transactions on Information Forensics and Security, 13(10), 2439-2447. https://doi.org/10.1109/TIFS.2018.2819126
• Swaminathan, N., Reddy, S. R. P., RajaShekara, K., & Haran, K. S. (2022). Flying cars and eVTOLs—Technology advancements, powertrain architectures, and design. IEEE Transactions on Transportation Electrification, 8(4), 4105-4117. https://doi.org/10.1109/TTE.2022.3172960
• Tiansawat, P., & Elliott, S. (2020). Unmanned aerial vehicles for automated forest restoration. In S. Elliott, G. Gale, & M. Robertson (Eds.), Automated forest restoration: Could robots revive rain forests? Proceedings of a brain-storming workshop, Chiang Mai University, Thailand (pp. 28-45). FORRU-CMU.
• Ubina, N. A., & Cheng, S. C. (2022). A review of unmanned system technologies with its application to aquaculture farm monitoring and management. Drones, 6(1), 12. https://doi.org/10.3390/drones6010012
• Van Duijn, M. A. J., & Vermunt, J. K. (2006). What is special about social network analysis? Methodology, 2(1), 2-6. https://doi.org/10.1027/1614-2241.2.1.2
• Wang, J., Tian, J., Zhang, X., Yang, B., Liu, S., Yin, L., & Zheng, W. (2022). Control of time delay force feedback teleoperation system with finite time convergence. Frontiers in Neurorobotics, 16, 877069. https://doi.org/10.3389/fnbot.2022.877069
• Yan, J., Jiao, H., Pu, W., Shi, C., Dai, J., & Liu, H. (2022). Radar sensor network resource allocation for fused target tracking: A brief review. Information Fusion, 86-87, 104-115. https://doi.org/10.1016/j.inffus.2022.06.009
• Zak, Y., Parmet, Y., & Oron-Gilad, T. (2023). Facilitating the work of unmanned aerial vehicle operators using artificial intelligence: An intelligent filter for command-and-control maps to reduce cognitive workload. Human Factors, 65(7), 1345-1360. https://doi.org/10.1177/00187208221081968
• Zhang, L., Gao, T., Cai, G., & Hai, K. L. (2022a). Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm. Journal of Energy Storage, 49, 104092. https://doi.org/10.1016/j.est.2022.104092
• Zhang, L., Zheng, H., Cai, G., Zhang, Z., Wang, X., & Koh, L. H. (2022b). Power‐frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system. IET Renewable Power Generation, 16(8), 1589-1601. https://doi.org/10.1049/rpg2.12461
• Zhu, K., Yang, J., Zhang, Y., Nie, J., Lim, W. Y. B., Zhang, H., & Xiong, Z. (2022). Aerial refueling: Scheduling wireless energy charging for UAV enabled data collection. IEEE Transactions on Green Communications and Networking, 6(3), 1494-1510. https://doi.org/10.1109/TGCN.2022.3164602