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Table of Contents

Lectures

Supporting materials for the lectures of the academic year 2018/2019. 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 of 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-126) 9:15-10:45
#1 39. Lecture 01 - Course information, introduction to robotics 21.09. - lec01
#2 40. 28.09. - National holidays (28.09.)
#3 41. Lecture 02 - Robotic paradigms and control architectures 05.10. - lec02
#4 42. Lecture 03 - Path planning - grid and graph-based path planning methods 12.10. - lec03
#5 43. Lecture 04 - Robotic information gathering - Mobile robot exploration 19.10. - lec04
#6 44. Lecture 05 - Multi-goal Path planning 26.10. - lec05
#7 45. Lecture 06 - Data Collection Planning 02.11. - lec06
#8 46. Lecture 07 - Curvature-constrained Data collection Planning 09.11. - lec07
#9 47. Lecture 08 - Randomized sampling-based motion planning methods 16.11. - lec08
#10 48. Lecture 09 - Game theory in robotics 23.11. - lec09
#11 49. Lecture 10 - Visibility based pursuit evasion games 30.11. - lec10
#12 50. Lecture 11 - Patrolling games 07.12. - lec11
#13 51. Lecture 12 - Temporal Task-Motion Planning 14.12. - lec12
52. 21.12. - Winter holidays (21.12. - 3.1.)
53. 28.12. - Winter holidays (21.12. - 3.1.)
#14 01. Lecture 13 - Multi-robot Systems 04.01. - lec13
TBA - Ungraded Assessment Deadline!

1. Course information, introduction to robotics

— Jan Faigl 2020/09/18 10:50

2. Robotic paradigms and control architectures

— Jan Faigl 2020/09/19 18:34

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

— Jan Faigl 2020/10/12 16:19

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 2020/10/19 09:10

5. Multi-goal Path planning

— Jan Faigl 2020/10/19 09:10

6. Data Collection Planning

— Jan Faigl 2020/10/16 12:46

7. Curvature-constrained Data collection Planning

— Jan Faigl 2020/10/19 09:10

8. Randomized sampling-based motion planning methods

— Jan Faigl 2020/10/19 09:10

9. Game theory in robotics

–

Game theory basics
Pursuit evasion games

— Pavel Rytir 2020/12/07 19:00

10. Visibility based pursuit evasion games

— Pavel Rytir 2020/12/07 19:00

11. Patrolling games

— Pavel Rytir 2020/12/07 19:00

12. Temporal Task-Motion Planning

13. Multi-robot Systems

— Jan Faigl 2021/01/03 21:05

Topics on Autonomous Navigation, Localization, and Mapping

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

courses/b4m36uir/lectures/start.txt · Last modified: 2021/01/03 21:06 by faiglj