40th International Conference on Production Engineering of Serbia
ICPES 2025
Nis, Serbia, 18-19th september 2025
EXPLAINABLE ARTIFICIAL INTELLIGENCE IN INDUSTRIAL AUTOMATION AND ROBOTICS: STATE, PROSPECTS AND A CASE-STUDY
Nemanja Markovic, Emilija Cojbašic, Dejan Rancic, Nedeljko Ducic, Žarko Cojbašic
DOI: 10.46793/ICPES25.285M
The integration of Artificial Intelligence (AI) into industrial automation and robotics has led to considerable improvements in adaptability, efficiency, and decision-making, allowing industrial control and robotics to respond to new challenges and demands in industrial setting. However, the non-transparent nature of many AI solutions poses significant challenges in safety-critical industrial environments where transparency, trust, and reliability are essential. Explainable Artificial Intelligence (XAI) addresses these concerns by providing insights into the inner workings of AI-driven systems. This paper briefly reviews the current landscape of XAI applications in industrial automation and robotics, highlighting key methods and limitations. It categorizes state-of-the-art approaches applicable to industrial control, fault detection, predictive maintenance, and human-robot interaction. Special attention is given to XAI methods based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS), while a XAI in industrial automation case study is also presented. Finally, future research directions are outlined, aimed at integrating XAI more seamlessly with autonomous systems, fostering safer, more transparent, and user-aligned intelligent automation solutions
Explainable Artificial Intelligence (XAI), Industrial Automation, Robotics, Intelligent Control Systems, Trustworthy Artificial Intelligence, Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
[1] Trivedi, C., et al, (2024). Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions. IEEE Open Journal of Industry Applications, DOI 10.1109/OJIA.2024.3399057.
[2] Ahmed, I., Jeon, G. and Piccialli, F. (2022), From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on Industrial Informatics, 18(8), pp.5031-5042.
[3] Saranya, A. and Subhashini, R., (2023). A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends, Decision Analytics Journal, 7, 100230.
[4] Chamola, V., Hassija, V., Sulthana, A.R., Ghosh, D., Dhingra, D. and Sikdar, B., (2023). A review of trustworthy and explainable artificial intelligence (XAI). IEEE Access, DOI: 10.1109/ACCESS.2023. 3294569.
[5] Longo, L. et al (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions, Information Fusion, 106, p.102301, DOI: 10.1016/j.inffus.2024.102301.
[6] Kalasampath, K., Spoorthi, K.N., Sajeev, S., Kuppa, S.S., Ajay, K. and Angulakshmi, M., (2025). A Literature review on applications of explainable artificial intelligence (XAI). IEEE Access, DOI: 10.1109/ACCESS.2025.3546681.
[7] Saranya, A. and Subhashini, R., (2023). A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends, Decision analytics journal, 7, p.100230, https://doi.org/10.1016/j.dajour.2023.100230.
[8] Chamola, V., Hassija, V., Sulthana, A.R., Ghosh, D., Dhingra, D. and Sikdar, B., (2023). A review of trustworthy and explainable artificial intelligence (XAI), IEEE Access, Vol. 11, 78995, DOI: 10.1109/ACCESS.2023.3294569.
[9] Sakai, T. and Nagai, T., 2022. Explainable autonomous robots: a survey and perspective. Advanced Robotics, 36(5-6), pp.219-238.
[10] Sobrín-Hidalgo, D., Guerrero-Higueras, Á.M. and Matellán-Olivera, V., (2025). Generating Explana-tions for Autonomous Robots: a Systematic Review. IEEE Access, DOI: 10.48550/arXiv.2412.18516
[11] Hitendra G. (2021), A Way Towards Explainable AI Using Neuro-Fuzzy System, 2021 5th IEEE International Conference on Information Systems and Computer Networks (ISCON), India.
[12] Ismail, M., Shang, C., Shen, Q. (2022). Towards a Framework for Interpretation of CNN Results with ANFIS. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds), Advances in Intelligent Systems and Computing, vol 1409.
[13] Markovic, N., Miloševic, M., Ciric, I., Cojbašic, E. and Ivacko, N. (2024), ANFIS Based Explainable AI Approach for Industrial Automation in the Food Industry, Proceedings of the ICIST 2024.
[14] Chiu, M.C. and Yang, L.S., (2024). Integrating explainable AI and depth cameras to achieve automation in grasping Operations: A case study of shoe company. Advanced Engineering Informatics, 62, p.102