Subject description - AD3M33MKR

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AD3M33MKR Mobile and Collective Robotics Extent of teaching:14+6L
Guarantors:  Roles:PO,V Language of
Teachers:  Completion:Z,ZK
Responsible Department:13133 Credits:6 Semester:Z


The course introduces basic mobile robot structure design together with control methods aimed to achieve autonomous and collective behaviors 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 modeling 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 a real robot HW.

Study targets:

The aim of the course is to introduce an elementary structure of intelligent mobile robots. Moreover, fundamental problems of robot control and realization of autonomous behavior of one robot as well as a group of several robots are introduced.

Course outlines:

Syllabus: 1. Introduction, curse schedule, history, components of an intelligent mobile robot, specifications of individual subtasks, problems.
2. Fundamental architectures of an intelligent mobile robot,their properties.
3. Common sensors for mobile robotics, camera, range-finder,odometry, introduction to data processing.
4. Data fusion, environment models and their construction I - occupancy grids, topological maps.
5. Data fusion, environment models and their construction II - geometric maps.
6. Trajectory planning - potential field, Dijkstra, D*, BUG, roadmaps.
7. Localization taxonomy, continuous localization 8. Global localization in a known or partially known environment.
9. Global localization in a known or partially known environment.
10. Simultaneous localization and mapping.
11. Collective robotics, overview of current systems, taxonomy of methods for coordination and cooperation in multi-robot systems.
12. Planning in multi-robot systems.
13. Localization in multi-robot systems.
14. Robotic swarms - realization of group behaviour.

Exercises outline:

1. Introduction to lab setup, assessment requirements and lab safety regulations. Student work assignment.
2. Excursion to Mobile Robotics Laboratory
3.-13.  Labs student work  
4. Presentation of achieved results,  assessment


1. 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
2. Latombe, J.C.: Robot Motion Planning, Kluwer Academic Publishers, Norwell, Mass. 1991.
3. Dudek, G., Jenkin, M.: Computation Principles of Mobile Robotics, Cambridge University, Press, SBN 0521560217, 2000.
4. Thrun, S.: Probabilistic algorithms in robotics. AI Magazine, 21(4):93-109, 2000.


Courses: Cybernetics and Artificial Intelligence, Programming 1,2, Intelligent Robotics, Robots. More information:


mobile robotics, collective robotics, intelligent robot, navigation, localization and data fusion in robotics, autonomous mobile robots, sensors processing, robot navigation

Subject is included into these academic programs:

Program Branch Role Recommended semester
MKEEM1 Technological Systems V 3
MKEEM5 Economy and Management of Electrical Engineering V 3
MKEEM4 Economy and Management of Power Engineering V 3
MKEEM3 Electrical Power Engineering V 3
MKEEM2 Electrical Machines, Apparatus and Drives V 3
MKKME1 Wireless Communication V 3
MKKME5 Systems of Communication V 3
MKKME4 Networks of Electronic Communication V 3
MKKME3 Electronics V 3
MKKME2 Multimedia Technology V 3
MKKYR1 Robotics PO 3
MKOI1 Artificial Intelligence V 3
MKOI5 Software Engineering V 3
MKOI4 Computer Graphics and Interaction V 3
MKOI3 Computer Vision and Image Processing V 3
MKOI2 Computer Engineering V 3

Page updated 17.6.2019 05:52:56, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)