An Egret Swarm Optimization Based BiRNN Method Approach to Determine the Protective Stopping Time of UR3 Robot Arm

Year 2025, Volume: 29 Issue: 6, 717 - 725, 23.12.2025

Göksu Taş , Cafer Bal

https://doi.org/10.16984/saufenbilder.1755797

Abstract

The emergency stop moment is of critical importance for robots to meet legal and industrial safety requirements, protect human health and equipment, and respond quickly in times of crisis. In this study, the emergency stop time of the UR3 robot arm, which is widely used in the industry, was determined according to the axis speed, current value, and temperature values. The emergency stop moment of the robot was determined successfully with the state-of-the-art deep learning models. The performance of the state-of-the-art deep learning models was compared using the most common classification metrics. In addition, the most important hyperparameter values of the Bidirectional Recurrent Neural Network method were determined using the Egret Swarm Optimization Algorithm, which is new in literature. Among all models, the ESOA-BiRNN model was the most successful method with a value of 0.946124 according to the ROC AUC metric. The success of the proposed method is given together with other classification metrics.

Keywords

UR3 Robot arm , Egret swarm optimization , Bidirectional recurrent neural , Network , Emergency stop

References

• M. Rüßmann, M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel, et al., “Industry 4.0: The future of productivity and growth in manufacturing industries,” Boston consulting group, vol. 9, no. 1, pp. 54–89, 2015.
• A. Jamwal, R. Agrawal, M. Sharma, and A. Giallanza, “Industry 4.0 Technologies for manufacturing sustainability: A systematic review and future research directions,” Applied Sciences, vol. 11, no. 12, Art. no. 12, Jan. 2021.
• B. Siciliano, O. Khatib, Eds., Springer Handbook of Robotics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.
• V. Moysiadis, D. Katikaridis, L. Benos, P. Busato, A. Anagnostis, D. Kateris, et al., “An integrated real-time hand gesture recognition framework for human–robot interaction in agriculture,” Applied Sciences, vol. 12, no. 16, Art. no. 16, Jan. 2022.
• A. Amarillo, E. Sanchez, J. Caceres, and J. Oñativia, “Collaborative human–robot interaction interface: Development for a spinal surgery robotic assistant,” Int J of Soc Robotics, vol. 13, no. 6, pp. 1473–1484, Sep. 2021.
• J. Ogbemhe, K. Mpofu, N. S. Tlale, “Achieving sustainability in manufacturing using robotic methodologies,” Procedia Manufacturing, vol. 8, pp. 440–446, Jan. 2017.
• L. Takayama, W. Ju, C. Nass, “Beyond dirty, dangerous and dull: What everyday people think robots should do,” in Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, in HRI ’08. New York, NY, USA: Association for Computing Machinery, Mar. 2008, pp. 25–32.
• C. Thomas, L. Stankiewicz, A. Grötsch, S. Wischniewski, J. Deuse, and B. Kuhlenkötter, “Intuitive work assistance by reciprocal human-robot interaction in the subject area of direct human-robot collaboration,” Procedia CIRP, vol. 44, pp. 275–280, Jan. 2016.
• D. Oakley, “Science and technology contributions to ımproving worker safety and health”, Accessed: Jul. 28, 2025. [Online]. Available: https://archivedproceedings.econference.io/wmsym/2001/29/29-2.pdf
• L. Gualtieri, E. Rauch, and R. Vidoni, “Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review,” Robotics and Computer-Integrated Manufacturing, vol. 67, p. 101998, Feb. 2021.
• M. Costanzo, G. De Maria, G. Lettera, and C. Natale, “A multimodal approach to human safety in collaborative robotic workcells,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 2, pp. 1202–1216, Apr. 2022.
• H. Li, W. Ma, H. Wang, G. Liu, X. Wen, Y. Zhang, et al., “A framework and method for Human-Robot cooperative safe control based on digital twin,” Advanced Engineering Informatics, vol. 53, p. 101701, Aug. 2022.
• A. M. Zanchettin and B. Lacevic, “Safe and minimum-time path-following problem for collaborative industrial robots,” Journal of Manufacturing Systems, vol. 65, pp. 686–693, Oct. 2022.
• M.-L. Lee, S. Behdad, X. Liang, M. Zheng, “Task allocation and planning for product disassembly with human–robot collaboration,” Robotics and Computer-Integrated Manufacturing, vol. 76, p. 102306, Aug. 2022.
• T. Ren, Y. Dong, D. Wu, K. Chen, “Collision detection and identification for robot manipulators based on extended state observer,” Control Engineering Practice, vol. 79, pp. 144–153, Oct. 2018.
• G. S. Karabay, M. Çavaş, E. Avcı, “Çiçek Sınıflandırmada AlexNet ve MobileNetV2 mimarilerinin performans karşılaştırması,” GUMMFD, vol. 40, no. 2, Feb. 2025.
• K. Usanmaz, S. Güney, “Derin öğrenme yöntemleriyle trafik işaretlerinin gerçek zamanlı sınıflandırılması,” GUMMFD, vol. 40, no. 2, Feb. 2025.
• M. Tyrovolas, K. Aliev, D. Antonelli, C. Stylios, “UR3 CobotOps,” UCI Machine Learning Repository, 2024.
• “UR3e Ultra-lightweight, compact cobot.” Accessed: May 05, 2025. [Online]. Available: https://www.universal-robots.com/products/ur3e/
• T. Li, Y. Liu, Z. Chen, “Application of sine cosine egret swarm optimization algorithm in gas turbine cooling system,” Systems, vol. 10, no. 6, Dec. 2022.
• P. Wei, M. Shang, J. Zhou, X. Shi, “Efficient adaptive learning rate for convolutional neural network based on quadratic interpolation egret swarm optimization algorithm,” Heliyon, vol. 10, no. 18, Sep. 2024.
• Y. Liu, C. Yu, D. Li, L. Wang, X. Li, H. Li, F. Wang, “An optimization capacity design method of household integrated energy system based on multi-objective egret swarm optimization,” IET Renewable Power Generation, vol. 17, no. 8, pp. 1993–2008, 2023.
• Z. Chen, A. Francis, S. Li, B. Liao, D. Xiao, T. T. Ha, et al., “Egret swarm optimization algorithm: An evolutionary computation approach for model free optimization,” Biomimetics, vol. 7, no. 4, Dec. 2022.
• Z. Wang, Y. Lii, X. He, R. Yan, Z. Li, Y. Jiang, et al., “Improved deep bidirectional recurrent neural network for learning the cross-sensitivity rules of gas sensor array,” Sensors and Actuators B: Chemical, vol. 401, p. 134996, Feb. 2024.
• W. Zhou, D. Luo, “The hesitant fuzzy BiRNN based on twice-cycle mechanism and its intelligent applications,” Expert Systems with Applications, vol. 237, p. 121655, Mar. 2024.