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Introduction to Robotics and Intelligent Control SIA601

ECTS 5 | P 30 | A 15 | L 15 | K 0 | ISVU 41160 | Academic year: 2020./2021.

Course groups

Prikaži sve grupe na predmetu

Course lecturers


Course description

Introduction to robotics: basic terms, classification and examples of robots. Description of position and orientation of a rigid body. Transformation between coordinate systems. Direct and inverse kinematics of a robot manipulator. Dynamic model of a robot manipulator. Position and force control of a robot manipulator. Sensors used in robotics. Basics of robot vision. Flexible production systems. Basics of fuzzy set theory. Fuzzy logic control. Structures of fuzzy logic controllers. Basic structures of neural networks. Static and dynamic neural networks. Learning algorithms. Neural networks in modelling, identification and control of systems. Genetic algorithms.

Knowledge and skills acquired

Knowledge needed for creating a kinematic and dynamic model of a robot manipulator based on its mechanical specifications and application of these models for manipulator control. Knowledge of sensors used in robotics and the basic principles of robot vision. The basics of flexible production systems and intelligent control.

Teaching methods

Lectures and laboratory exercises.

Student requirements

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Monitoring of students

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Student assessment

Laboratory exercises revision exam, seminar paper, final exam.

Obligatory literature

1. 1 Kovačić, Z; Bogdan, S; V. Krajči. Osnove robotike Zagreb: Graphis, 2002.

Pretraži literaturu na:

Recommended additional literature

1. 1 J. J. Craig Introduction to Robotics: Mechanics and Control Addison

2. 2 C. T. Lin, C. S. G. Lee Neural Fuzzy Systems - A Neuro-Fuzzy Synergism to Intelligent Systems Prentice Hall, 1996.

Examination methods

Seminar paper and oral exam.

Course assessment

Conducting university questionnaires on teachers (student-teacher relationship, transparency of assessment criteria, motivation for teaching, teaching clarity, etc.). Conducting Faculty surveys on courses (upon passing the exam, student self-assessment of the adopted learning outcomes and student workload in relation to the number of ECTS credits allocated to activities and courses as a whole).

Overview of course assesment

Learning outcomes
Upon successful completion of the course, students will be able to:

1. assess the applicability of robots in production processes and services

2. formulate kinetic models of robot manipulators based on their mechanical specifications using the Denavit-Hartenberg method

3. list types of actuators and sensors which are commonly used in robotics and explain the basic applications of sensors in robotics

4. develop a simple computer programme for robot manipulator control

5. explain the basic principles of genetic algorithms

6. perform experimental testing of an artificial neural network for a particular application

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