Robotics

Course Robotics gives an overview over the entire field of robotics. Topics are selected according to the needs of engineers who introduce or maintain robotic cells or production lines in industry. In the theoretical part of the course students learn the geometric model of the robot, which is essential for programming robots. In the practical part of the course, students in small groups learn programming of industrial robots.

Knowledge in mathematics, physics and electrical engineering in the first level od studies.

1. Introduction of robots in industry
2. Robot components
3. Kinematics and dynamics of robot mechanisms
4. Robot control and trajectory planning
5. Task planning and robot programming
6. Examples of industrial robot applications

Knowledge and understanding:
Knowledge of pose description with homogeneous transformation matrices, knowledge of geometric models of robot mechanisms, knowledge of control schemes that are specific to robotics. Link theoretical knowledge of geometric models with programming of industrial robots. Programming and working with industrial robots. Use of knowledge for development of robotic production cells.

• T. Bajd, M. Mihelj, J. Lenarčič, A. Stanovnik, M. Munih: Robotika, Založba FE in FRI, 2008. Catalogue
• M. Mihelj, T. Bajd, A. Ude, J. Lenarčič, A. Stanovnik, M. Munih, J. Rejc, S. Šlajpah: Robotics, Springer, 2019.

• The written exame is a seminar. Each student chooses a topic that s/he needs to review and present his/her understanding of the selected topic. • The oral exam assesses knowledge of the theoretical and general concepts presented through the lectures and exercises. This is related to the problems of the introduction of robot in industry, structure of robots (in particular industrial robots), functions of robots, design and control of robots, basic motion characteristics of robots, technological and economical aspects of robotization. 50/50

Prof. Aleš Ude, PhD, is a Full Professor of Electrical Engineering and a Scientific Councillor at the Jožef Stefan Institute, where he has headed the Department of Automatics, Biocybernetics and Robotics since 2014. He graduated in Applied Mathematics from the University of Ljubljana in 1990 and received his PhD in robotics and imitation learning from the University of Karlsruhe, Germany, in 1995. Between 1998 and 2000, he worked as a postdoctoral researcher in Japan, first within the ERATO Kawato Dynamic Brain project and subsequently at the ATR research institute in Kyoto, with which he continues to collaborate to this day. His main research interests include robot learning, humanoid robotics, intelligent robotic systems, and reconfigurable robotic cells. In recent years, his work has focused primarily on applying artificial intelligence to autonomous robot learning and adaptation, as well as on developing flexible robotic systems for industrial applications. As a research group leader, he has participated in numerous European research projects funded under the FP6, FP7, Horizon 2020, and Horizon Europe programmes, including as coordinator of the ReconCell and ReconCycle projects. He has also been involved in several research projects with Japanese research institutions and Yaskawa. He is a co-inventor of five Japanese patents and the author or co-author of numerous scientific publications in the field of robotics.

Selected bibliography:

M. Mavsar, J. Morimoto, and A. Ude (2023) GAN-Based Semi-Supervised Training of LSTM Nets for Intention Recognition in Cooperative Tasks, IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2023.3333231, pp. 1-8.

T. Gašpar, I. Kovač, and A. Ude (2021) Optimal layout and reconfiguration of a fixturing system constructed from passive Stewart platforms, Journal of Manufacturing Systems, vol. 60, pp. 226-238.

Z. Lončarević, A. Gams, S. Reberšek, B. Nemec, J. Škrabar, J. Skvarč, and A. Ude (2021) Specifying and optimizing robotic motion for visual quality inspection, Robotics and Computer-Integrated Manufacturing, vol. 72, art. 102200, pp. 1-14.

B. Nemec, K. Yasuda, and A. Ude (2021) A virtual mechanism approach for exploiting functional redundancy in finishing operations, IEEE Transactions on Automation Science and Engineering, vol. 18, no. 4, pp. 2048-2060.

M. Simonič, T. Petrič, A. Ude, and B. Nemec (2021) Analysis of methods for incremental policy refinement by kinesthetic guidance, Journal of Intelligent & Robotic Systems, vol. 102, art. 5, pp. 1-19.