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B3M33MKR: Mobile and and Collective Robotics (winter term 2021/22)


Wednesday 11:00-12:30, KN:E-126 Lecturers:


Wednesday 12:45-14:15 KN:E-230 (taught primarily in Czech)

Wednesday 14:30-16:00 KN:E-230 (taught in English)

Teaching assistants:

Course description

The course introduces a basic mobile robot structure design together with control methods aimed to achieve autonomous and collective behaviours for robots. Methods and tool s for data acquisition and processing are presented herein with the overall goal to resolve the task of autonomous navigation for mobile robots comprising the tasks of sensor fusion, environmental modelling including Simultaneous Localization And Mapping (SLAM) approaches. Besides sensor-processing related tasks, methods for robot trajectory planning will be introduced. The central topic of the course stands in specific usage of the afore methods capable of execution with groups of robots and taking the advantage of their cooperation and coordination in groups. Labs and seminars are organized in a form of an Open Laboratory whereas the students will resolve the given problem in simulated environments as well as with real robot hardware.


Week Topic Slides Version
1.Course introductions, Taxonomy of the localization problem, continuous localization. (MK) Slides 23/09/2020
2.Probabilistic methods of localization (MK)Slides
4.SLAM. (MK)Slides
Likelihood Slides
5.Localization in multi-robot teams. (MK)Slides
6.Kinematics of a mobile robot, trajectory control. (LP)Slides 8/12/2020
8.World models and their building. (LP) Slides
9.Introduction to planning in mobile robotics. (LP) Slides 8/12/2020
10.Planning under uncertainty. (LP) Slides 8/12/2020
12.Probabilistic methods to planning (RRT). (LP) Slides 8/12/2020
13.Multi-robot systems, aspects of their design, cooperation, coordination, communication. Swarms. (MK) Slides
14.Introduction to a Research Topic in Mobile Robotics (L. Camara) Slides 8/12/2020

  • S. Thrun, W. Burgard, and D. Fox: Probabilistic Robotics. MIT Press, Cambridge, MA, 2005.
  • A. Kelly: Mobile Robotics: Mathematics, Models, and Methods. Cambridge University Press, 2013.
  • H. Choset, K. M. Lynch, S. Hutchinson, G. A. Kantor, W. Burgard, L. E. Kavraki, S. Thrun: Principles of Robot Motion: Theory, Algorithms, and Implementations (Intelligent Robotics and Autonomous Agents series), MIT Press, 2005.
  • Borenstein, J., Everett, B., and Feng, L.: Navigating Mobile Robots: Systems and Techniques A. K. Peters, Ltd., Wellesley, MA, ISBN 1-56881-058-X, 1996.
  • Dudek, G., Jenkin, M.: Computation Principles of Mobile Robotics, Cambridge University, Press, SBN 0521560217, 2000.

Content of seminars

Week Topic
1.Labs organization, Turtlebot + sensors intro
2.Multi-robot coordination (MRC) start in simulator
3.Individual work (MRC)
7.Particle filter (PF)
8.Individual work (PF)
13. Integration with real robots
14. Integration, grading.

Organization of labs

See the separate page.

Credit allowance conditions

  • Getting the seminar credit - you will not be allowed to pass the exam without this one.
  • Passing the oral exam.

Exam topics

  • Continuous localization, ICP and its derivatives, Correlative Scan Matching
  • Bayes filter and its derivation, KF & EKF, Particle filter, motion model, sensor model
  • SLAM, online x offline SLAM, EKF SLAM, Fast SLAM, Rao-Blackwellization
  • Multi-robot systems – taxonomy, taxonomy of task assignment, planning for MRS
  • Multi-robot localization – maximum-likelihood estimation, particle filter, EKF
courses/b3m33mkr/start.txt · Last modified: 2021/09/13 10:59 by kulich