Table of Contents

Lectures

Supporting materials for the lectures of the academic year 2021/2022. The materials are slides, also available in printer-safe version as handouts with 2×2 and 3×3 slides on a single page.

These supportive materials are not intended as a replacement for your own notes from the lectures. They are rather provided to help you to understand the studied problems.

#TWeek Week Monday (Room No. KN:E-107) 11:00-12:30
#1 38. Lecture 01 - Course information, introduction to robotics 20.09. - lec01
#2 39. Lecture 02 - Robotic paradigms and control architectures 27.09. - lec02
#3 40. Lecture 03 - Path planning - grid and graph-based path planning methods 04.10. - lec03
#4 41. Lecture 04 - Robotic information gathering - Mobile robot exploration 11.10. - lec04
#5 42. Lecture 05 - Multi-goal path planning 18.10. - lec05
#6 43. Lecture 06 - Data collection planning 25.10. - lec06
#7 44. Lecture 07 - Curvature-constrained data collection planning 01.11. - lec07
#8 45. Lecture 08 - Randomized sampling-based motion planning methods 08.11. - lec08
#9 46. Lecture 09 - Pursuit-evasion games 15.11. - lec09
#10 47. Lecture 10 - Patrolling games 22.11. - lec10
#11 48. Lecture 11 - Temporal task-motion planning 29.11. - lec11
#12 49. Lecture 12 - Autonomous Navigation with Environment Changes Understanding 06.12. - lec12
#13 50. Lecture 13 - Multi-Agent Pathfinding (MAPF) and Multi-robot Motion Planning 13.12. - lec13
51. 21.12. - Winter holidays (20.12. - 2.1.)
52. 28.12. - Winter holidays (20.12. - 2.1.)
#14 01. Exam Test (reserve) 03.01. -
8.1.2022 @ 23.59 CEST - Ungraded Assessment Deadline!

1. Course information, introduction to robotics

— Jan Faigl 2021/09/20 23:13

2. Robotic paradigms and control architectures

— Jan Faigl 2021/09/13 05:06

3. Path planning - Grid and graph-based path planning methods

— Jan Faigl 2021/10/04 10:38

4. Robotic information gathering - Mobile robot exploration

— Jan Faigl 2019/10/14 13:50 Update: Comments on Hungarian algorithm and dummy tasks and resources. Further comments on the relation of the decision-making and particular realization of the whole navigation stack.

— Jan Faigl 2021/10/10 21:49

5. Multi-goal path planning

— Jan Faigl 2021/10/18 12:51

6. Data collection planning

— Jan Faigl 2021/09/13 05:06

7. Curvature-constrained data collection planning

— Jan Faigl 2021/11/01 12:52

8. Randomized sampling-based motion planning methods

— Jan Faigl 2021/11/08 10:49

9. Pursuit-evasion games

— Tomas Kroupa 2021/12/05 20:00

10. Patrolling games

— Tomas Kroupa 2021/12/05 16:55

11. Temporal task-motion planning

12. Autonomous Navigation with Environment Changes Understanding

Tomáš Krajník TBD

13. Multi-Agent Pathfinding (MAPF) and Multi-robot Motion Planning

Pavel Surynek TBD

Topics of Invited Talks

AA. Autonomous navigation

Slides
References
  1. Bonin-Font, Francisco, Alberto Ortiz, and Gabriel Oliver. Visual navigation for mobile robots: A survey. Journal of intelligent and robotic systems 53.3 (2008): 263-296. pdf
  2. Rodney Brooks. Intelligence without representation. Artificial Intelligence 91 pdf
  3. Filliat, David, and Jean-Arcady Meyer. Map-based navigation in mobile robots:: I. a review of localization strategies. Cognitive Systems Research 4.4 (2003): 243-282. pdf
  4. Tomáš Krajník, Filip Majer et al. Navigation without localisation: reliable teach and repeat based on the convergence theorem. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. pdf

BB. Simultanneous Localisation and Mapping

Slides
References
  1. Stachniss, Cyrill: Introduction to Robot Mapping video
  2. Cadena et al.: Past, Present and Future of SLAM: Towards the Robust-Perception Age. IEEE T-RO 2018. pdf
  3. Grissetti et al.: Tutorial on Graph-Based SLAM. ITS Magazine pdf

CC. Long-term navigation and spatio-temporal mapping

Slides
References
  1. Krajnik et al. CHRONOROBOTICS: Representing the structure of time for service robots In IJCRAI 2019. pdf
  2. Kunze et al. Artificial Intelligence for Long-term Autonomy: a survey. IEEE RA-L 19. pdf
  3. Krajnik et al. Image Features for Visual T\&R Navigation in Changing Environments. RASS 17. pdf
  4. Halodova et al. Predictive and adaptive maps for long-term visual navigation. In IROS 19. pdf
  5. Krajnik et al. FreMEn: Frequency map enhancement for long-term mobile robot autonomy in changing environments.IEEE T-RO 2017. pdf
  6. Krajnik et al. Warped Hypertime Representations for Long-termAutonomy of Mobile Robots IEEE RA-L 2019.pdf

— Tomáš Krajník 2020/01/13 13:28

DD. Multi-robot systems

— Jan Faigl 2021/01/03 21:05