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.
2. Robotic paradigms and control architectures
3. Path planning - Grid and graph-based path planning methods
— 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
6. Data collection planning
7. Curvature-constrained data collection planning
8. Randomized sampling-based motion planning methods
9. Pursuit-evasion games
10. Patrolling games
11. Temporal task-motion planning
12. Autonomous Navigation with Environment Changes Understanding
13. Multi-Agent Pathfinding (MAPF) and Multi-robot Motion Planning
Topics of Invited Talks
AA. Autonomous navigation
Slides
References
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
Rodney Brooks.
Intelligence without representation. Artificial Intelligence 91
pdf
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
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
Stachniss, Cyrill: Introduction to Robot Mapping
video
Cadena et al.: Past, Present and Future of SLAM: Towards the Robust-Perception Age. IEEE T-RO 2018.
pdf
Grissetti et al.: Tutorial on Graph-Based SLAM. ITS Magazine
pdf
CC. Long-term navigation and spatio-temporal mapping
Slides
References
Krajnik et al.
CHRONOROBOTICS: Representing the structure of time for service robots In IJCRAI 2019.
pdf
Kunze et al.
Artificial Intelligence for Long-term Autonomy: a survey. IEEE RA-L 19.
pdf
Krajnik et al.
Image Features for Visual T\&R Navigation in Changing Environments. RASS 17.
pdf
Halodova et al.
Predictive and adaptive maps for long-term visual navigation. In IROS 19.
pdf
Krajnik et al.
FreMEn: Frequency map enhancement for long-term mobile robot autonomy in changing environments.IEEE T-RO 2017.
pdf
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