Autonomous Mobile Robots

Learning Outcomes

  1. classify mobile robots according to various criteria
  2. analyze driving mehanisms and sensor system sutable for intended application
  3. develop sensor fusion algorithms
  4. develop motion planning algorithms
  5. develop motion of mobile robots localization
  6. develop algorithms of environment 2D map building

Forms of Teaching



Week by Week Schedule

  1. Kinematic models and constraints
  2. Kinematic models and constraints
  3. Maneuverability, Motion control
  4. Maneuverability, Motion control
  5. Decomposition strategies, Map building
  6. Decomposition strategies, Map building
  7. Kalman filter localization, Triangulation
  8. Midterm exam
  9. Kalman filter based SLAM, Bayesian filter based SLAM
  10. Kalman filter based SLAM, Bayesian filter based SLAM
  11. Path planning
  12. Path planning
  13. Obstacle avoidance, Navigation
  14. Navigation
  15. Final exam

Study Programmes

University graduate
Control Systems and Robotics (profile)
(3. semester)


(.), Roland Siegwart, Illah Nourbakhsh and Davide Scaramuzza (2011.), Introduction to Autonomous Mobile Robots, The MIT Press,
(.), Dieter Fox, Sebastian Thrun, and Wolfram Burgard (2005.), Probabilistic Robotics, The MIT Press,
(.), Gregory Dudek and Michael Jenkin (2000.), Computational Principles of Mobile Robots, Cambridge University Press,

For students


ID 222999
  Winter semester
L3 English Level
L3 e-Learning
45 Lectures
12 Laboratory exercises