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Artificial Intelligence in Robotics

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Code Completion Credits Range Language
B4M36UIR Z,ZK 6 2P+2C Czech
It is not possible to register for the course B4M36UIR if the student is concurrently registered for or has already completed the course BE4M36UIR (mutually exclusive courses).
The requirement for course B4M36UIR can be fulfilled by substitution with the course BE4M36UIR.
It is not possible to register for the course B4M36UIR if the student is concurrently registered for or has previously completed the course BE4M36UIR (mutually exclusive courses).
Garant předmětu:
Jan Faigl
Stefan Edelkamp, Jan Faigl, Tomáš Kroupa
Stefan Edelkamp, Jan Faigl, Tomáš Kroupa, Jiří Kubík, David Milec, Miloš Prágr, Jakub Sláma, David Valouch
Department of Computer Science

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 prg.ai 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 https://prg.ai/minor.

Syllabus of lectures:

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)

Syllabus of tutorials:

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

Study Objective:

The goal of the course is to developed practical experience with the deployment of planning and optimization methods in robotics states. The hands-off experience supports the practical understanding limitations of physical systems. After completing the course, the students should understand that problems with real robots need to make sufficient simplifications yielding satisfactory solutions but still be computationally feasible.

Study materials:

• Course materials - https://cw.fel.cvut.cz/wiki/courses/uir/start

• 1st chapter: Robin R. Murphy: Introduction to AI Robotics, MIT Press, Cambridge, MA, 2001

• Steven M. LaValle: Planning Algorithms, Cambridge University Press, 2006 (http://planning.cs.uiuc.edu )

• 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.

Further information:
Time-table for winter semester 2023/2024:

(lecture parallel1)
Karlovo nám.
Zengerova posluchárna K1
Valouch D.
(lecture parallel1
parallel nr.101)

Karlovo nám.
HW-lab K307
Prágr M.
(lecture parallel1
parallel nr.102)

Karlovo nám.
HW-lab K307
Kubík J.
(lecture parallel1
parallel nr.103)

Karlovo nám.
HW-lab K307
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-06-13
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet4701506.html