The aim of the course is to develop student's ability for implementation of intelligent robotic systems using the conceptual design and robot evolutiveness, in accordance with the basic paradigms of artificial intelligence. Students will acquire knowledge and skills necessary for further development of production technologies within the Industry 4.0 by learning the robotic system structure based on methodological approach which includes robot mechanics, sensor and actuator subsystems, control and robot movement optimization as well as hardware-software integration. It is done by using the laboratory equipment like different industrial robots structures, re-configurable mobile robots with various sensors and embedded laboratory model of the manufacturing system in accordance with implementation of 3D computer simulation for Industry 4.0 development.

After the course students will be able to: • Implement developed software tools for modelling and analyses of intelligent robotic systems. • Choose the advanced methods based on artificial neural networks (by using the Matlab and BPnet software) and other computational intelligence techniques in order to carry out the intelligent behaviour of the mobile robot in interaction with the manufacturing environment. • Understand the interaction between the software and hardware subsystems of intelligent mobile robot structure through robotic reconfiguration and programming in Matlab environment. • Develop the ability for team work.

New teaching contents are: 1. Industrial robot, mobile robot: Definitions. Functional structure. Technical characteristics. Classification. Description of mechanical structure. Types of structures. End effectors, grippers and tools. Singularities. 2. Description of orientation. Algorithm of associating coordinate systems to robot segments. The Jacobian. Robot kinematics: spatial descriptions and transformations, direct and inverse kinematics problem. 3. Robot control. Control system structure. Single axis control (drive and measuring subsystem, transmission subsystem). 4. Sensors, internal and external. Recognition systems. 5. Robot programming, methods. Robot programming languages. 6. Artificial intelligence within the advanced robotic systems. 7. Artificial neural networks. 8. Genetic algorithms. 9. Intelligent control of mobile robots. Mobile robots motion in unknown environments. 10.Mobile robot movement optimization. Algorithm А*.

Practical teaching: 1. First calculation task: Spatial relations and transformations. 2. Second calculation task: Robot kinematics, direct and inverse kinematics problem. 3. Laboratory work - exercise 1: Modelling and simulation of the robotic system. Robot programming. 4. Laboratory work - exercise 2: Modelling and simulation of the systems of artificial neural networks. Programming in Matlab. 5. Laboratory work - exercise 3: Intelligent control of mobile robot. Programming in Matlab. 6. Laboratory work - exercise 4: Optimal mobile robot motion by using optimization algorithms. Programming in Matlab. 7. Seminar work: robot kinematics, mobile robot programming, mobile robot localization and map building - intelligent behaviour, etc.

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/1./ Miljković,Z., Slavković,N., Petrović,M.M., (2020) Robots, sensors and additional equipment within the Laboratory for robotics and artificial intelligence (ROBOTICS & AI): • Five-axis micro-industrial and educational robot Mitsubishi MOVEMASTER EX RV-M1; • Six-axis industrial robot ILR LOLA50; • Five-axis industrial robot "GOŠKO"; • DELTA robot with 3+1 DoF; • Khepera II – KheIIBase mobile robot with Khepera Gripper Turret, camera CMUcam VISION TURRET–KheCMUCam and infrared sensors; • Lego® Mindstorms NXT and Lego® Mindstorms EV3 mobile robots with various sensors; • "Buggy” mobile robot with infrared sensors and control module "Clicker2” based on micro-controller ARM STM32F407VGT6; • Recognition system for intelligent mobile robot control based on stereo „visual servoing". /2./ Z. Miljković, M.M. Petrović, (2021) INTELLIGENT MANUFACTURING SYSTEMS – with excerpts from robotics and artificial intelligence, Textbook (XXVIII+409 p.), University of Belgrade - Faculty of Mechanical Engineering, (1st Edition: ISBN 978-86-6060-071-6). /In Serbian/ /3./ Z. Miljković, N. Slavković, M.M. Petrović, (2022) Handouts for each lecture. /In Serbian and English/ /4./ Z. Miljković, N. Slavković, M.M. Petrović, (2022) Instructions for doing students' calculation tasks, laboratory exercises and seminar work. /In Serbian and English/ /5./ Z. Miljković, N. Slavković, (2022) The Course site (http://cent.mas.bg.ac.rs/nastava/ir_msc/index.htm) containing relevant information for students, book references as well as addresses of robot manufacturers and respective institutions (IFR, RIA, JARA, CIRP, etc.). /In Serbian and English/ /6./ Miljković,Z., Slavković,N., Petrović,M.M., (2020) Manuals for programming robots and additional equipments within the Laboratory of robotics and artificial intelligence. /In Serbian and English/ /7./ G. Dudek, M. Jenkin, (2024, 3rd Edition) Computational Principles of Mobile Robotics, (450 p.), Cambridge University Press (e-Textbook, ISBN: 9781108682404).

Total assigned hours: 90

New material: 25
Elaboration and examples (recapitulation): 20

Auditory exercises: 0
Laboratory exercises: 15
Calculation tasks: 10
Seminar paper: 5
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 1
Review and grading of lab reports: 2
Review and grading of seminar papers: 1
Review and grading of the project: 0
Test: 2
Test: 4
Final exam: 5

Activity during lectures: 5
Test/test: 40
Laboratory practice: 15
Calculation tasks: 5
Seminar paper: 5
Project: 0
Final exam: 30
Requirement for taking the exam (required number of points): 30

R.Siegwart, I.R.Nourbakhsh, D.Scaramuzza, (2011, 2nd Edition) INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS, (472 p.), The MIT Press (ISBN-13: 978-0262015356).; Z. Miljković, M.M. Petrović, (2021) INTELLIGENT MANUFACTURING SYSTEMS – with excerpts from robotics and artificial intelligence, Textbook in Serbian, UB-FME, XXVIII+409 p. (ISBN 978-86-6060-071-6).; Miljković,Z., Aleksendrić,D., (2018, 2nd Edition) ARTIFICIAL NEURAL NETWORKS – solved examples with short theory background, Textbook in Serbian, UB-FME, VI+225 p. (ISBN 978-86-7083-961-8).; Craig J.J., (2017, 4th Edition) Introduction to Robotics: Mechanics and Control, (450 p.), Pearson (ISBN-13: 978-0133489798).; Sciavicco L., Siciliano B., (2000) Modelling and Control of Robot Manipulators, (402 p.), Springer (ISBN-13: 978-1852332211).