Artificial Intelligence in Robotics

Semestr: Winter

Range: 2P+2C


Credits: 6

Programme type:

Study form: Fulltime

Course language: English

Time table at FEE


The course aims to acquaint students with the use of planning approaches and decision-making techniques of artificial intelligence for solving problems arising in autonomous robotic systems. Students in the course are employing knowledge of planning algorithms, game theory, and solving optimization problems in selected application scenarios of mobile robotics. Students first learn architectures of autonomous systems based on reactive and behavioral models of autonomous systems. The considered application scenarios and robotic problems include path planning, persistent environmental monitoring, robotic exploration of unknown environments, online real-time decision-making, deconfliction in autonomous systems, and solutions of antagonistic conflicts. In laboratory exercises, students practice their problem formulations of robotic challenges and practical solutions in a realistic robotic simulator or consumer mobile robots. This course is also part of the inter-university programme Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at


Course syllabus:

1. Course information, introduction to robotics
2. Robotic paradigms and control architectures
3. Path planning - grid and graph-based path planning methods
4. Robotic information gathering - Mobile robot exploration
5. Multi-goal path planning
6. Data collection planning
7. Curvature-constrained data collection planning
8. Randomized sampling-based motion planning methods
9. Visibility based pursuit-evasion games
10. Patrolling games
11. Temporal task-motion planning
12. Multi-robot planning
13. Reserve (invited lecture of a guest host)
14. Reserve (invited lecture of a guest host)

Seminar syllabus:

During laboratory exercises, students practice robotic challenges and practical solutions in a realistic robotic simulator or mobile robots.

- Computational models of autonomous systems;
- Path planning, randomized search techniques, multi-goal path planning, and informative path planning;
- Robotic exploration, online decision-making, persistent environmental monitoring, decision-making with limited resources;
- Methods of game theory and safety games in mobile robotics tasks, solving antagonistic conflict;
- Reactive and behavioral models in tasks of collective robotics;
- Coordination and cooperation in autonomous systems;

1. Introduction to CoppeliaSim and open-loop robot locomotion control
2. Exteroceptive sensing and reactive-based obstacle avoidance
3. Mapping
4. Grid and graph-based path planning
5. Incremental path planning
6. Mobile robot exploration
7. Semestral project assignment
8. Data collection path planning with remote sensing (TSPN)
9. Curvature-constrained data collection path planning (DTSPN)
10. Randomized sampling-based algorithms
11. Curvature-constrained local planning with RRT-based algorithms
12. Pursuit evasion - greedy policy and value iteration policy
13. Area patrolling
14. Semestral project discussion


• Course materials -
• 1st chapter: Robin R. Murphy: Introduction to AI Robotics, MIT Press, Cambridge, MA, 2001
• Steven M. LaValle: Planning Algorithms, Cambridge University Press, 2006 ( )
• Maja J. Mataric: The Robotics Primer, 2007, MIT Press.
• Kevin M. Lynch, Frank C. Park: Modern Robotics: Mechanics, Planning, and Control, Cambridge University Press, 2017.