Safety Systems Analysis in Human-Small Industrial Robot Collaboration: ISO/TS 15066 Specification

Yıl 2025, Cilt: 7 Sayı: 2, 94 - 110, 31.08.2025

Orhan Engin , Gökhan Gökmen , Mustafa Yusuf Anlıaçık , Doğan Turgut

https://doi.org/10.46740/alku.1610614

Öz

During Industry 4.0, collaboration between humans and industrial robots is increasing day by day. Industrial robots carry capacity that could harm humans operate in protected areas and are subject to safety standards. The main purpose of this study is to design experiments, analyze the activities to be done regarding security and monitoring systems in case of collaboration of small industrial robots with power-force limitations with human workers and to examine the studies conducted in recent years in literature. The successful implementation of Physical Human-Robot Interaction in industrial environments depends on ensuring safe collaboration between human operators and robotic devices. This requires an environment that guarantees the safety of human operators without restricting the speed and movement of robots. In this research, the contributions of the specification, which was published by the International Organization for Standardization (ISO) in February 2016 and are an additional specification to ISO/TS (Robots and robotic devices - Collaborative robots) 15066 and EN ISO 10218 (Robots and robotic devices - Safety requirements for industrial robots), were examined. The research has determined that the use of industrial robots in human-robot collaboration applications is becoming increasingly important. It has been concluded that the disadvantages of humans and robots can be minimized by combining their advantages, human abilities (cognition, adaptation or tactile abilities) can be combined with robots' speed, power or precision, but this development requires new safety measures to protect human integrity, especially when working closely with heavy robots.

Anahtar Kelimeler

Physical human-robot interaction , ISO/TS 15066 specification , EN ISO 10218 , Strength and force limitation

İnsan ile Küçük Endüstriyel Robot İşbirliğinde Güvenlik Sistemleri Analizi: ISO/TS 15066 Spesifikasyonu

Öz

Endüstri 4.0 sürecinde, İnsan ve endüstriyel robotların iş birlikleri günden güne artmaktadır. İnsana zarar verebilecek taşıma kapasiteli endüstriyel robotlar, korunaklı alanda çalışır ve güvenlik standartlarına tabii tutulur. Bu çalışmanın temel amacı, deney tasarlama, analiz, güç-kuvvet sınırlamasına sahip küçük endüstriyel robotların, insan çalışanlar ile iş birliği çalışmaları durumunda güvenlik ve izleme sistemleri ile ilgili yapılması gereken faaliyetlerin analiz edilmesi ve literatürde son yıllarda yapılan çalışmaların incelenmesidir. Fiziksel İnsan-Robot Etkileşiminin sanayi ortamlarında başarılı bir şekilde uygulanabilmesi, insan operatörler ile robotik cihazlar arasında güvenli bir iş birliğini sağlamaya bağlıdır. Bu, robotların hızını ve hareketini kısıtlamadan, insan operatörlerin güvenliğini garanti eden bir ortam gerektirir. Bu araştırmada,
Şubat 2016'da Uluslararası Standardizasyon Örgütü (ISO) tarafından yayımlanan, ISO/TS (Robotlar ve robotik cihazlar- İşbirlikçi robotlar) 15066 ve EN ISO 10218 (Robotlar ve robotik cihazlar- Endüstriyel robotlar için güvenlik gereklilikleri) için ek niteliğinde olan spesifikasyonun sağladığı katkılar incelenmiştir. Araştırmada, Endüstriyel robotların, insan-robot iş birliğine yönelik uygulamalarda kullanımının giderek daha önemli hale geldiği belirlenmiştir. İnsan ve robotların avantajları birleştirilerek dezavantajlarının minimize edilebileceği, insan yetenekleri (kognisyon, adaptasyon veya dokunsal yetiler) robotların hız, güç veya hassasiyet gibi özellikleriyle birleştirilebileceği ancak bu gelişim, özellikle ağır robotlarla yakın çalışırken insan bütünlüğünü korumak için yeni güvenlik önlemleri gerektirdiği sonuçlarına varılmıştır.

Anahtar Kelimeler

Fiziksel insan-robot etkileşimi , ISO/TS 15066 spesifikasyonu , EN ISO 10218 , Güç ve kuvvet sınırlama

Kaynakça

• [1] I. Akgul, "Time Series Analysis and Forecasting Models," Suggestion Journal, vol. 1, no. 1, pp. 52–69, 1994, doi: 10.14783/maruoneri.698511.
• [2] F. Alharbi and D. Csala, "A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach," School of Engineering, Lancaster University, vol. 7, pp. 94, 2022.
• [3] D. Aydin, "Demand Forecast Analysis with the Help of Artificial Neural Networks and an Application in Maritime Transportation Sector," M.S. thesis, Inst. of Social Sciences, Marmara Univ., Istanbul, Türkiye, 2012.
• [4] C. Chase, Demand-Driven Forecasting: A Structured Approach to Forecasting. USA: John Wiley & Sons, 2013, doi: 10.1002/9781118691861.
• [5] J. Cheng, S. Tiwari, D. Khaled, M. Mahendru, and U. Shahzad, "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, vol. 198, pp. 1–15, 2024, doi: 10.1016/j.techfore.2023.122938.
• [6] J. Fattah, L. Ezzine, Z. Aman, H. El Moussami, and A. Lachhab, "Forecasting of demand using ARIMA model," International Journal of Engineering Business Management, vol. 10, pp. 1–9, 2018, doi: 10.1177/1847979018808673.
• [7] T. Falatouri, F. Darbanian, P. Brandtner, and C. Udokwu, "Predictive Analytics for Demand Forecasting – A Comparison of SARIMA and LSTM in Retail SCM," Procedia Computer Science, vol. 200, pp. 993–1003, 2022, doi: 10.1016/j.procs.2022.01.298.
• [8] E. Hazır, K. K. Koç, and Ş. Esnaf, "Prediction of Turkish Furniture Sales Values with a Sample Artificial Intelligence Application," National Furniture Congress Journal, pp. 1172–1182, 2015.
• [9] S. Imece and Ö. F. Beyca, "Demand Forecasting with Integration of Time Series and Regression Models in Pharmaceutical Industry," Demand Forecasting in Pharmaceutical Industry, vol. 34, no. 3, pp. 414–423, 2022, doi: 10.7240/jeps.1127844.
• [10] F. R. Jacobs and R. B. Chase, Operations and Supply Management: The Core, 6th ed. New York: McGraw-Hill, 2010.
• [11] W. Jiang, X. Wu, Y. Gong, W. Yu, and X. Zhong, "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, vol. 193, pp. 1–8, 2020, doi: 10.1016/j.energy.2019.116779.
• [12] N. Kaya, Stock Management. Ankara: İksad Publishing House, 2020.
• [13] M. Lin, Z. Zhang, and Y. Cao, "Forecasting Supply and Demand of the Wooden Furniture Industry in China," Forest Products Journal, vol. 69, no. 3, pp. 228–238, 2019, doi: 10.13073/FPJ-D-19-00011.
• [14] S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting: Methods and Applications. New York: John Wiley & Sons, 1997.
• [15] E. E. Nebati, M. Taş, and G. Ertaş, "Demand Forecasting in Electricity Consumption in Turkey: Comparison with Time Series and Regression Analysis," European Journal of Science and Technology, no. 31, pp. 348–357, 2021, doi: 10.31590/ejosat.998277.
• [16] M. Nurtaş, Z. Zhantaev, and A. Altaibek, "Earthquake time-series forecast in Kazakhstan territory: Forecasting accuracy with SARIMAX," Procedia Computer Science, vol. 231, pp. 353–358, 2024, doi: 10.1016/j.procs.2023.12.216.
• [17] M. Karahan, "Statistical Forecasting Methods: Application of Product Demand Forecasting with Artificial Neural Networks Method," M.S. thesis, Dept. of Business Administration, Selcuk Univ., Konya, Türkiye, 2011.
• [18] V. Önen, "Turkey's Airline Cargo Demand Forecast Modeling and Forecasting Using ARIMA Method," Journal of Management and Economics Research, vol. 18, no. 4, pp. 29–53, 2020, doi: 10.11611/yead.677319.
• [19] V. Önen, "Turkish Airline Passenger Demand Forecast Modeling, Forecasting and Comparison with ARIMA-ARIMAX Method," Journal of Transportation and Logistics, vol. 8, no. 2, pp. 242–273, 2023, doi: 10.26650/JTL.2023.1270944.
• [20] E. Polat, "Sales Forecast in White Goods Industry: A Data Mining Application," M.S. thesis, Inst. of Social Sciences, Uludağ Univ., Bursa, Türkiye, 2022.
• [21] G. Rumbe, M. Hamasha, and S. A. Mashaqbeh, "A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry," Results in Engineering, vol. 21, pp. 1–6, 2024, doi: 10.1016/j.rineng.2024.101899.
• [22] B. Salttürk, "Estimation of Product Sales Quantities with Artificial Neural Networks: An Application in Furniture Industry," M.S. thesis, Inst. of Science, Sakarya Univ., Sakarya, Türkiye, 2022.
• [23] R. Siddiqui, M. Azmat, S. Ahmed, and S. Kummer, "A Hybrid Demand Forecasting Model for Greater Forecasting Accuracy: The Case of the Pharmaceutical Industry," Supply Chain Forum: An International Journal, vol. 23, no. 3, pp. 1–11, 2021, doi: 10.1080/16258312.2021.1967081.
• [24] W. V. D. Silva, C. P. D. Veiga, C. R. P. D. Veiga, A. Catapan, and U. Tortato, "Demand forecasting in food retail: a comparison between the Holt-Winters and ARIMA models," WSEAS Transactions on Business and Economics, vol. 11, pp. 608–614, 2014.
• [25] S. Ulutürk, "Refinement Models in Future Forecasting and an Application," M.S. thesis, Inst. of Social Sciences, Istanbul Technical Univ., Istanbul, Türkiye, 1994.
• [26] M. Valipour, "Application of ARIMA model for inflow forecasting of Moghanlo River," Journal of King Saud University – Engineering Sciences, vol. 27, no. 1, pp. 72–84, 2015.
• [27] P. E. Yaneva and H. N. Kulina, "Furniture Market Demand Forecasting Using Machine Learning Approaches," Journal of Physics: Conference Series, vol. 2675, no. 1, pp. 1–11, 2023, doi: 10.1088/1742-6596/2675/1/012008.
• [28] M. Yücesan, "Forecasting Monthly Sales of White Goods Using Hybrid ARIMAX and ANN Models," Atatürk University Journal of Social Sciences Institute, vol. 22, no. 4, pp. 2603–2617, 2018.
• [29] M. Yücesan, M. Gül, and E. Çelik, "Performance comparison between ARIMAX, ANN and ARIMAX-ANN hybridization in sales forecasting for furniture industry," Drvna Industrija, vol. 69, no. 4, pp. 357–370, 2018.
• [30] B. Yüksel, "Demand Prediction in Time Series," YBS Encyclopedia, vol. 11, no. 2, pp. 1–18, 2023.
• [31]"Statsmodels," Accessed: Sep. 21, 2024. [Online]. Available: https://www.statsmodels.org/stable/examples/notebooks/generated/statespace_sarimax_stata.html