Autonomous Systems (AS) include development of intelligent machines capable to fulfill working tasks in advanced manufacturing environment through hardware-software integration, without explicit human control. Considering the production technologies of the 21st century which include hardware-software integration of AS, especially robots, as well as automatic subsystems, this subject aims to qualify PhD students for independent development of modern manufacturing systems and processes, their modelling, until implementation of advanced technologies within the intelligent manufacturing systems based on theoretical and practical aspects of new algorithms and methods in domain of artificial intelligence.

Starting from the fundamental concepts, this subject includes scientific multidisciplinary in accordance with biological inspired bases through perspective development realization in the fields of intelligent control, artificial life and application of autonomous systems in robotized production technologies of the 21st century. The outcome of this subject is oriented towards scientific progress of PhD students, especially through intensive scientific experimental research work in domain of hardware-software integration of AS within advanced technologies of the 21st century based on development of machine intelligence and learning (computational intelligence; machine Q-learning; advanced artificial intelligence techniques; Biological Manufacturing Systems (BMS), etc.).

Theoretical education is organized in several parts: • Autonomous work and control of machine systems - Biologically inspired control of intelligent machines; • Fundamental structural elements of AS - Sensor-actuator relation; • Software architecture for autonomous systems - Hierarchical architecture; Reactive and behavioral architecture; Hybrid architecture; Open architecture; • What is machine learning? - Nature of learning; Probabilistic approach to machine learning; • Empirical control - Algorithm of empirical control; Application and influence of axiomatic design theory on empirical control development; • Control of mobile robots family - Intelligent control of common mobile robot colony (ant colony optimization algorithms); • Trends of development of autonomous robots - Micro- nano-robots; Potential risks of intensive development of autonomous robots.

Practical education is organized in several parts: • Localization and mapping of the manufacturing environment - introduction to SLAM (laboratory work); • Communicative and interactive competence of robots in working environment (laboratory work); • Machine learning in accordance with intelligent control (laboratory work); • Robot learning (laboratory work); Evolutionary algorithms; Learning by Demonstration (LfD); • Architecture of intelligent control of mobile robots (laboratory work); Heterogeneous robotic teams and cooperative work; Reconfigurability of mobile robots; • Self-organizing, autonomous evolution and self-replication of robots.

MSc degree of technically oriented faculty.

[1] Z. Miljković, M.M. Petrović, INTELLIGENT MANUFACTURING SYSTEMS – with excerpts from robotics and artificial intelligence (1st ed.), Textbook, XXVIII+409 p., University of Belgrade - Faculty of Mechanical Engineering, 2021, 18.1 /In Serbian/ [2] Z. Miljković, D. Aleksendrić, ARTIFICIAL NEURAL NETWORKS – solved examples with theoretical background (2nd ed.), Textbook, University of Belgrade - Faculty of Mechanical Engineering, 2018, 18.1 /In Serbian/ [3] M. Kalajdžić (editor), Lj. Tanović, B. Babić, M. Glavonjić, Z. Miljković, et al., CUTTING TECHNOLOGY (9th ed.), Handbook, University of Belgrade - Faculty of Mechanical Engineering, 2021, 18.1 /In Serbian/ [4] Z. Miljković, Systems of artificial neural networks in production technologies, Monograph book within the Series Intelligent Manufacturing Systems, Vol. 8, University of Belgrade - Faculty of Mechanical Engineering, 2003, 18.1 /In Serbian/ [5] B. Babić, FLEXY - Intelligent system for FMS design, Monograph book within the Series Intelligent Manufacturing Systems, Vol. 5, University of Belgrade - Faculty of Mechanical Engineering, 1994, 18.1 /In Serbian/ [6] Laboratory mobile robots (PAL-TIAGo - Mobile Manipulator Robot with stereo vision system; K-Team's Khepera II mobile robot with gripper and camera; LEGO Mindstorms NXT and LEGO Mindstorms EV3 Sets of reconfigurable mobile robots equipped with sensors and micro-controllers; RAICO (Robot with Artificial Intelligence based Cognition) & DOMINO (Deep learning based Omnidirectional Mobile robot with INtelligent cOntrol) - prototypes of own development mobile robots), Laboratory CeNT, University of Belgrade - Faculty of Mechanical Engineering, 18.12 [7] Laboratory model of designed manufacturing system, Laboratory CeNT, University of Belgrade - Faculty of Mechanical Engineering, 18.12 [8] Software packages (BPnet, ART Simulator, MATLAB, Python 3.14.0rc2 and 3.13.7, AnyLogic, Flexy), Laboratory CeNT, University of Belgrade - Faculty of Mechanical Eng., 18.13

Total assigned hours: 65

New material: 30
Elaboration and examples (recapitulation): 20

Auditory exercises: 0
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 10
Review and grading of the project: 0
Test: 0
Test: 0
Final exam: 5

Activity during lectures: 20
Test/test: 0
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 40
Project: 0
Final exam: 40
Requirement for taking the exam (required number of points): 40

Z. Miljković, D. Aleksendrić, (2018) ARTIFICIAL NEURAL NETWORKS–SOLVED EXAMPLES WITH THEORETICAL BACKGROUND (In Serbian), 2nd Ed.. 225 pp. (ISBN 978-86-7083-961-8), UB–Faculty of Mech. Eng., Belgrade.; R. Siegwart, I.R. Nourbakhsh, D. Scaramuzza, (2011) INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS, 2nd Edition, 472 pp. (ISBN 9780262015356), The MIT Press, Cambridge, MA 02142.; G.A. Bekey, (2005) AUTONOMOUS ROBOTS: From Biological Inspiration to Implementation and Control, 577 pp. (ISBN 9780262025782), The MIT Press, London, England.; R.A. Brown, (1994) MACHINES THAT LEARN: Based on the Principles of Empirical Control, 891 pp. (ISBN 9780195069662), Oxford University Press.; E. Alpaydin, (2010) INTRODUCTION TO MACHINE LEARNING, 2nd Edition, 400 pp. (ISBN 9780262012119), The MIT Press, Cambridge, England.