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

Lectures

Laboratory

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
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (3. semester)
Communication and Space Technologies (profile)
Free Elective Courses (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (3. semester)
Computer Engineering (profile)
Free Elective Courses (3. semester)
Computer Science (profile)
Free Elective Courses (3. semester)
Control Systems and Robotics (profile)
Core-elective courses 2 (3. semester)
Data Science (profile)
Free Elective Courses (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (3. semester)
Electronic and Computer Engineering (profile)
Free Elective Courses (3. semester)
Electronics (profile)
Free Elective Courses (3. semester)
Information and Communication Engineering (profile)
Free Elective Courses (3. semester)
Network Science (profile)
Free Elective Courses (3. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (3. semester)

Literature

(.), 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

General

ID 222480
  Winter semester
5 ECTS
L3 English Level
L3 e-Learning
45 Lectures
12 Laboratory exercises