Robot Control

Course description

The entire content is divided into following major topics:
– control of open kinematic chains (robotic sensors, joint space control and operational space control)
– control of closed kinematic chains based on interaction forces (measurement of forces and torques, impedance control, direct force control) and
– visual servoing (robot vision sensor, pose computation, computed pose based control and image based control),

– robot control based on iterative learning control, reinforcement learning and artificial intelligence,

– use of Robot operating system (ROS) for robot control.

Course is carried out on study programme

Elektrotehnika 2. stopnja

Objectives and competences

a) To understand theoretical basis of control of open and closed kinematic chains and visual servoing.
(b) Validation of properties of the chosen control schemes on real robot mechanisms in the Laboratory of robotics.
(c) Integration of knowledge gained in courses Introduction to robotics, Kinematics and dynamics of robots and Robot control in functional robotic system.

Learning and teaching methods

Lectures, laboratory work in small groups, complex robot control problem solving. Practical exercises take place on a number of modern industrial and other robots equipped with additional force sensors. The students have a textbook “Vodenje robotov” or equivalent English textbook with course content. Invited are guest speakers from the Slovenian industry. In this course, special attention is paid to safety.

Intended learning outcomes

After successful completion of the course, students should be able to:

– design robot control methods for open and closed kinematic chains based on classical control approaches and modern artificial intelligence based concepts,

– synthesize robot control based on visual servoing,

– analyze parameters of the control system that affect stability,

– integrate knowledge of kinematics, differential kinematics, statics and dynamics for the purposes of controlling industrial robots, haptic interfaces and other mechanical systems,

– select the right approach and the complexity of control method for the specific mechanism and planned task,

– solve a specific robot control problem within a workgroup.

Reference nosilca

  1. MIHELJ, Matjaž, BAJD, Tadej, MUNIH, Marko. Vodenje robotov. Ljubljana: Založba FE in FRI, 2011. 
  2. MIHELJ, Matjaž, PODOBNIK, Janez. Haptics for virtual reality and teleoperation, Springer, 2013. 
  3. MIHELJ, Matjaž, PODOBNIK, Janez, MUNIH, Marko. Sensory fusion of magnetoinertial data based on kinematic model with Jacobian weighted-left-pseudoinverse and Kalman-adaptive gains. IEEE transactions on instrumentation and measurement, ISSN 0018-9456. Jul. 2019, vol. 68, no. 7, str. 2610-2620. 
  4. BAUMKIRCHER, Aljaž, SEME, Katja, MUNIH, Marko, MIHELJ, Matjaž. Collaborative robot precision task in medical microbiology laboratory. Sensors. 2022, vol. 22, no. 8, str. 1-10 
  5. BAUMKIRCHER, Aljaž, MUNIH, Marko, MIHELJ, Matjaž. Performance analysis of learning from demonstration approaches during a fine movement generation. IEEE transactions on human-machine systems. Dec. 2021, vol. 51, no. 6, str. 653-662. 

Study materials

  1. MIHELJ, Matjaž, BAJD, Tadej, MUNIH, Marko. Vodenje robotov. Ljubljana: Založba FE in FRI, 2011. 
  2. SICILIANO Bruno, SCIAVICCO, Lorenzo, VILLANI, Luigi, ORIOLO, Giuseppe.: Robotics – Modelling, Planning and Control, Springer 2009. 
  3. SUTTON, Richard S., BARTO, Andrew G. Reinforcement Learning: An Introduction (second edition), The MIT Press 2020. 
  4. SICILIANO, Bruno, KHATIB, Oussama, Handbook of Robotics, Springer, 2016.  

Bodi na tekočem

Univerza v Ljubljani, Fakulteta za elektrotehniko, Tržaška cesta 25, 1000 Ljubljana

E: T:  01 4768 411