Course details

Robotics (in English)

ROBa Acad. year 2022/2023 Winter semester 5 credits

Current academic year

Basic components of the stationary industrial robots. Kinematics. Solution of the inverse kinematic task. Equations of motion. Path planning. Elements and structure of the mobile robots. Models and control of mobile robots. Sensoric systems of mobile robots. Localization and navigation. Environment maps. Robot control.


Course coordinator

Language of instruction



Examination (written)

Time span

  • 26 hrs lectures
  • 12 hrs laboratories
  • 14 hrs projects

Assessment points

  • 55 pts final exam (written part)
  • 19 pts mid-term test (written part)
  • 12 pts labs
  • 14 pts projects




Subject specific learning outcomes and competences

The students acquire knowledge of current state and trends in robotics. Also, they acquire practical knowledge from construction, programs 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, creation and maintaining of robotic systems into practice.

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

  • George A. Bekey: Autonomous Robots: From Biological Inspiration to Implementation and Control, 2005, Bradford Book, ISBN-13 978-0262025782
  • Ronald C. Arkin: Behavior-Based Robotics, Bradford Books, 1998, ISBN-13 ‏ : ‎ 978-0262529204

  • John M. Holland: Designing Autonomous Mobile Robots: Inside the Mind of an Intelligent Machine, 2003, Newnes,  ISBN-13 ‏ 978-0750676830 
  • Alex Ellery: Planetary Rovers: Robotic Exploration of the Solar System, Springer, 2016, ISBN-13 ‏978-3642032585

  • Gary Bradsky and Adrian Kaehler: LearningOpenCV, O'Reilly, 2008, ISBN 978-0-596-51613-0

  • Grewal, Andrews and Bartone: Global Navigation Satellite Systems, Inertial Navigation, and Integration, Wiley, 2013, ISBN-13 ‎ 978-1118447000

  • Kaplan and Hegarty: Understanding GPS/GNSS: Principles and Applications, Artech House, 2017, ISBN-13 ‎ 978-1630810580

  • Hassan K. Khalil: Nonlinear Systems, Pearson, ISBN-13 ‎ 978-0130673893

  • Dorf and Bishop: Modern Control Systems, Pearson Hall, 2011, ISBN-13 ‎ 978-0136024583

  • Ogata: Modern Control Engineering, Pearson, 2009, ISBN-13 ‎ 978-0136156734

  • Franklin, Powel and Emami-Naeini: Feedback Control of Dynamic Systems, Pearson, ISBN13 978-0-13-349659-8

  • Beard and McLain: Small Unmanned Aircraft: Theory and Practice, Princeton, 2021, ISBN-13 ‎ 978-0691149219

  • Sayed: Fundamentals of Adaptive Filtering, Wiley, 2003, ISBN-13 ‏978-0471461265

  • Russel and Norvig: Artificial Intelligence: A Modern Approach, Pearson, 2009, ISBN-13 ‏978-0136042594

Fundamental literature

  • Siegwart, R. a Nourbakhsh, I. R.: Introduction to Autonomous Mobile Robots. MIT Press, 2011. ISBN-13: 978-0262015356 

  • Thrun, S., Burgard, W. a Fox, D.: Probabilistic Robotics. MIT Press, 2005. ISBN 0-262-201623 

  • Choset, H., Lynch, K. M., Hutchinson, S. et al.: Principles of Robot Motion. MIT, Press, 2005. ISBN 0-262-03327-5. 

  • Murphy, R., R.: An Introduction to AI Robotics (Intelligent Robotics and Autonomous Agents), Bradford Books, 2019, ISBN 9780262038485

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.
  9. Probability in robotics. Introduction. Bayesian filtering, Kalman and particle filters. Model of robot movements and measurement model.
  10. Methods of the global and local localization. GPS based localization, Monte Carlo method.
  11. Map building. Algorithms for simultaneous localization and mapping (SLAM).
  12. Trajectory planning in known and unknown environment. Bug algorithm, potential fields, visibility graphs and probabilistic methods.
  13. Introduction to control and regulation.

Syllabus of laboratory exercises

  1. Basic work with Arduino
  2. Working with sensors
  3. Motor control
  4. Basics of ROS, sensor reading
  5. Advanced work in ROS
  6. Final task

Syllabus - others, projects and individual work of students

Project implemented on the robot from FIT.

Progress assessment

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

Controlled instruction

There are compulsory projects and laboratories that follow on from the projects.

Course inclusion in study plans

Back to top