Course details

Robotics (in English)

ROBa Acad. year 2021/2022 Winter semester 5 credits

Basic components of the stationary industrial robots. Kinematic chains. Kinematics. Solution of the inverse kinematic task. Singularities. Dynamics. Equations of motion. Path planning. Robot control. Elements and structure of the mobile robots. Models and control of mobile robots. Sensoric systems of mobile robots. Localization and navigation. Environment maps. Man-machine interface, telepresence. AI in robotics. Microrobotics.


Deputy Guarantor

Orság Filip, Ing., Ph.D. (DITS FIT BUT)

Language of instruction



Examination (written)

Time span

26 hrs lectures, 6 hrs laboratories, 20 hrs projects

Assessment points

55 exam, 19 mid-term test, 6 exercises, 20 projects




Bambušek Daniel, Ing. (DCGM FIT BUT)

Subject specific learning outcomes and competences

The students acquire knowledge of current state and trends in robotics. Also, they acquire practical knowledge from construction and use of robots.

Learning objectives

To inform students about current state and future of robotics. Also, to inform students about peculiarities of robotic systems and prepare them for introduction of robotic systems to industry.

Why is the course taught

Robotics experiences a big boom thanks to coming of various autonomous systems, be it autonomous vehicles or mass production. General knowledge of dealing with various tasks and basics of robotics clears the way to employment in the fields, which use robots on a daily basis.

Study literature

  1. Siegwart, R. a Nourbakhsh, I. R.: Introduction to Autonomous Mobile Robots. MIT Press, 2011. ISBN-13: 978-0262015356
  2. Thrun, S., Burgard, W. a Fox, D.: Probabilistic Robotics. MIT Press, 2005. ISBN 0-262-201623
  3. Choset, H., Lynch, K. M., Hutchinson, S. et al.: Principles of Robot Motion. MIT, Press, 2005. ISBN 0-262-03327-5.
  4. Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2000,  ISBN 0262133830

Fundamental literature

  • LYNCH, Kevin M. and PARK, Frank C. Modern robotics: mechanics, planning, and control. Cambridge (Reino Unido) : Cambridge University Press, 2018.
  • GOVERS, Francis X. Artificial intelligence for robotics: buildintelligent robots that perform human tasks using AI techniques. Birmingham, UK : Packt Publishing Ltd, 2018.
  • Nolfi, S., Floreano, D.: Evolutionary Robotics : The Biology, Intelligence, and Technology of Self-Organizing Machines (Intelligent Robotics and Autonomous Agents), Bradford Books, 2004, ISBN 0262640562
  • Holland, J., M.: Designing Autonomous Mobile Robots: Inside the Mind of an Intelligent Machine, 2003, ISBN 0750676833
  • Craig, J., J.: Introduction to Robotics: Mechanics and Control, Prentice Hall, 2003, ISBN 0201543613
  • Sciavicco, L., Siciliano, B.: Modelling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing), Springer Verlag, 2000, ISBN 1852332212
  • Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2000,  ISBN 0262133830 
  • Spong, M., Vydyasagar, M.: Robot Dynamics and Control, J. Willey, 1989, ISBN 047161243X

Syllabus of lectures

  1. History, evolution, and current trends in robotics. Introduction to robotics. Robotic applications. Robotic competitions.
  2. Kinematics and statics. Direct and inverse task of kinematics.
  3. Path planning in the cartesian coordinate system.
  4. Effectors,sensors and power supply of robots. Applications of the cameras, laser distance meters, and sonars.
  5. Midterm test.
  6. Basic parameters of the mobile robots. Model and control of the wheel mobile robots.
  7. Robotic middleware. Robot Operating System (ROS), philosophy of ROS, nodes and communication among them.
  8. Maps - configuration space and 3D models. Probability in robotics. Introduction. Bayesian filtering, Kalman and particle filters. Model of robot movements and measurement model.
  9. Methods of the global and local localization. GPS based localization, Monte Carlo method.
  10. Map building. Algorithms for simultaneous localization and mapping (SLAM).
  11. Trajectory planning in known and unknown environment. Bug algorithm, potential fields, visibility graphs and probabilistic methods.
  12. Introduction to control and regulation.
  13. Multicopters, principle, control, properties, usage. Human - robot collaboration.

Syllabus of laboratory exercises

  1. Lego Mindstorms
  2. Basics of ROS, sensor reading
  3. Advanced work in ROS

Syllabus - others, projects and individual work of students

Project implemented on some of the robots from FIT.

Progress assessment

  1. Graded laboratories.
  2. Mid-term written test.
  3. Evaluated project with a defence.


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Course inclusion in study plans

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