40th International Conference on Production Engineering of Serbia
ICPES 2025
Nis, Serbia, 18-19th september 2025
COGNITIVE MOBILE ROBOTICS BASED ON INTELLIGENT MECHANISMS OF LEARNING
Zoran MILJKOVIC, Aleksandar JOKIC, Ðorde JEVTIC
DOI: 10.46793/ICPES25.002M
The purpose of this contribution is the deployment of digital manufacturing through new cognitive intelligence mechanisms. With the implementation of Industry 4.0 principles, mobile intelligent robots utilized as transportation vehicles in the manufacturing system need a higher degree of autonomy to fulfill all the requirements of the contemporary market. Although industrial robots are common in manufacturing systems, mobile robotics requires the expertise of specialists in cognitive robotics issues to gain international competitiveness, particularly for small and medium-sized enterprises. The industrial mobile robots’ autonomous subsystems based on deep machine learning provide significantly more flexibility as well as more accurate and robust real-time decisions compared to common deterministic sensor-based algorithms. The main goal of this paper is to create artificial intelligence-based solutions for cognitive mobile robotics within Industry 4.0 using a Machine Learning (ML) based approach, particularly deep learning (convolutional neural networks, recurrent neural networks, etc.). The focus of the paper is the generation of new ML-based cognitive intelligence mechanisms for obstacle avoidance, decision-making, and visual control of intelligent mobile robots, whereas the main goal of the paper is to demonstrate the possibility of integrating intelligent ML-based algorithms into a high-level cognitive architecture by enabling better understanding of the environment in real-time through the processing of higher-quality and more complex sensory data, thereby enhancing the overall flexibility of mobile robotic systems within intelligent manufacturing systems.
Mobile robots, intelligent control systems, digital manufacturing, cognitive intelligence mechanisms, deep learning, autonomous systems, visual servoing
[1] A. Jokic, M. Petrovic, Z. Miljkovic, Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment, Expert Systems with Applications, Vol. 190, p. 116203, 2022.
[2] M. Petrovic, Z. Miljkovic, A. Jokic, Efficient Machine Learning of Mobile Robotic Systems based on Convolutional Neural Networks, in Artificial intelligence for Robotics and Autonomous Systems. Studies in Computational Intelligence, A. Koubaa and A. T. Azar, Eds., Springer, 2023, pp. 1–26.
[3] A. Jokic, M. Petrovic, Z. Miljkovic, Real-time Mobile Robot Perception based on Deep Learning Detection Model, in New Technologies, Development and Application V (NT 2022). Lecture Notes in Networks and Systems, Springer, Cham, 2022, pp. 670–677.
[4] A. Jokic, M. Petrovic, Z. Miljkovic, Mobile robot decision-making system based on deep machine learning, in 9th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2022), 2022, pp. 653–656.
[5] N. Sünderhauf et al., The limits and potentials of deep learning for robotics, International Journal of Robotics Research, Vol. 37, No. 4–5, pp. 405–420, 2018.
[6] R. Brooks, A robust layered control system for a mobile robot, IEEE journal on robotics and automation, Vol. 2, No. 1, pp. 14–23, 2003.
[7] S. Vasudevan, S. Gächter, V. Nguyen, R. Siegwart, Cognitive maps for mobile robots—an object based approach, Robotics and Autonomous Systems, Vol. 55, No. 5, pp. 359–371, 2007.
[8] J. E. Laird, The Soar cognitive architecture. MIT press, 2019.
[9] G. R. Team et al., Gemini robotics: Bringing ai into the physical world, arXiv preprint arXiv:2503.20020, 2025.
[10] S. Song, S. P. Lichtenberg, J. Xiao, SUN RGB-D: A RGB-D scene understanding benchmark suite, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 567–576.
[11] Z. Miljkovic, A. Jokic, M. Petrovic, Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots, in Deep Learning for Unmanned Systems. Studies in Computational Intelligence, A. Koubaa and A. T. Azar, Eds., Springer, Cham, 2021, pp. 447–479.
[12] F. Chaumette, S. Hutchinson, Visual servo control. I. Basic approaches, IEEE Robotics and Automation Magazine, Vol. 13, No. 4, pp. 82–90, 2006.