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 of your own notes from the lectures. They are rather provided to help you to understand the studied problems.

• slides: b4m36uir-lec01-slides.pdf
• slides - handout: b4m36uir-lec01-handout.pdf
• slides - handout 2×2: b4m36uir-lec01-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec01-handout-3x3.pdf

— Jan Faigl 2020/09/18 10:50

• slides: b4m36uir-lec02-slides.pdf
• slides - handout: b4m36uir-lec02-handout.pdf
• slides - handout 2×2: b4m36uir-lec02-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec02-handout-3x3.pdf

— Jan Faigl 2020/09/19 18:34

• slides: b4m36uir-lec03-slides.pdf
• slides - handout: b4m36uir-lec03-handout.pdf
• slides - handout 2×2: b4m36uir-lec03-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec03-handout-3x3.pdf

— Jan Faigl 2020/10/12 16:19

• slides: b4m36uir-lec04-slides.pdf
• slides - handout: b4m36uir-lec04-handout.pdf
• slides - handout 2×2: b4m36uir-lec04-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec04-handout-3x3.pdf

— 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

• slides: b4m36uir-lec05-slides.pdf
• slides - handout: b4m36uir-lec05-handout.pdf
• slides - handout 2×2: b4m36uir-lec05-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec05-handout-3x3.pdf

— Jan Faigl 2020/10/19 09:10

• slides: b4m36uir-lec06-slides.pdf
• slides - handout: b4m36uir-lec06-handout.pdf
• slides - handout 2×2: b4m36uir-lec06-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec06-handout-3x3.pdf

— Jan Faigl 2020/10/16 12:46

• slides: b4m36uir-lec07-slides.pdf
• slides - handout: b4m36uir-lec07-handout.pdf
• slides - handout 2×2: b4m36uir-lec07-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec07-handout-3x3.pdf

— Jan Faigl 2020/10/19 09:10

• slides: b4m36uir-lec08-slides.pdf
• slides - handout: b4m36uir-lec08-handout.pdf
• slides - handout 2×2: b4m36uir-lec08-handout-2x2.pdf
• slides - handout 3×3: b4m36uir-lec08-handout-3x3.pdf

— Jan Faigl 2020/10/19 09:10

• slides: b4m36uir-lec09-slides.pdf
• slides - 2×2: b4m36uir-lec09-slides-2x2.pdf
• slides - 3×3: b4m36uir-lec09-slides-3x3.pdf

–

• Yoav Shoham, Kevin Leyton-Brown: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. [Sections 3.2, 4.1, 6.3] http://www.masfoundations.org
• Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. Machine Learning Proceedings 1994, 157–163. https://www2.cs.duke.edu/courses/spring07/cps296.3/littman94markov.pdf

• Robin, C., & Lacroix, S. (2016). Multi-robot target detection and tracking: taxonomy and survey. Autonomous Robots, 40(4), 729–760. https://hal.archives-ouvertes.fr/hal-01183372/document
• Chung, T. H., Hollinger, G. A., & Isler, V. (2011). Search and pursuit-evasion in mobile robotics: A survey. Autonomous Robots, 31(4), 299–316. https://www-users.cs.umn.edu/~isler/pub/pe-survey.pdf
• Sgall J. (2001). Solution of David Gale's lion and man problem. Theoretical Computer Science. 259(1-2):663-70. https://core.ac.uk/download/pdf/82510373.pdf
• Homicidal chauffeur game: http://sector3.imm.uran.ru/poland2008patsko/index.html
• S. Karaman, E. Frazzoli. Incremental Sampling-Based Algorithms for a Class of Pursuit- Evasion Games, 2011. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1018.3630&rep=rep1&type=pdf

— Pavel Rytir 2020/12/07 19:00

• slides: b4m36uir-lec10-slides.pdf
• slides - 2×2: b4m36uir-lec10-slides-2x2.pdf
• slides - 3×3: b4m36uir-lec10-slides-3x3.pdf
• Urrutia, J. (1997). Art Gallery and Illumination Problems. Handbook of Computational Geometry, 973–1027. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.3028&rep=rep1&type=pdf
• Guibas, L. J., Latombe, J. C., LaValle, S. M., Lin, D., & Motwani, R. (1997, August). Visibility-based pursuit-evasion in a polygonal environment. In Workshop on Algorithms and Data Structures (pp. 17-30). guibas1997.pdf
• McMahan, Gordon, Blum (2003): Planning in the presence of cost functions controlled by an adversary. ICML. http://www.cs.cmu.edu/~ggordon/mcmahan-ggordon-blum.icml2003.pdf
• Raboin, E., Nau, D., Kuter, U., Gupta, S. K., & Svec, P. (2010). Strategy generation in multi-agent imperfect-information pursuit games. AAMAS, pp. 947-954. http://www.cs.umd.edu/~nau/papers/raboin2010strategy.pdf

— Pavel Rytir 2020/12/07 19:00

• slides: b4m36uir-lec11-slides.pdf
• slides - 2×2: b4m36uir-lec11-slides-2x2.pdf
• slides - 3×3: b4m36uir-lec11-slides-3x3.pdf

• Kiekintveld, C., Jain, M., Tsai, J., Pita, J., Ordóñez, F. and Tambe, M. Computing optimal randomized resource allocations for massive security games. AAMAS 2009. computing_optimal_randomized_resource_allocations_.pdf
• Agmon, Noa, Gal A. Kaminka, and Sarit Kraus. Multi-robot adversarial patrolling: facing a full-knowledge opponent. Journal of Artificial Intelligence Research 42 (2011): 887-916. http://u.cs.biu.ac.il/~galk/publications/papers/jair11.pdf
• Basilico, Nicola, Nicola Gatti, and Francesco Amigoni. Patrolling security games: Definition and algorithms for solving large instances with single patroller and single intruder. Artificial Intelligence 184 (2012): 78-123. 1-s2.0-s0004370212000240-main.pdf

— Pavel Rytir 2020/12/07 19:00

• slides (pdf): b4m36uir-lec12-slides.pdf

• J. Benton, A. J. Coles and A. Coles: Temporal planning with preferences and time-dependent continuous costs, ICAPS, 2012, pp. 2-10.
• S. Edelkamp, E. Plaku, Y. Warsame: Monte-Carlo Search for Prize-Collecting Robot Motion Planning with Time Windows, Capacities, Pickups, and Deliveries. KI, 2019, pp. 154-167.
• S. Edelkamp, M. Lahijanian, D. Magazzeni, E. Plaku: Integrating Temporal Reasoning and Sampling-Based Motion Planning for Multigoal Problems Wit Dynamics and Time Windows. IEEE Robotics Automation Letters, 3(4):3473-3480, 2018.
• M. Fox and D. Long: PDDL2.1: An extension to PDDL for expressing temporal planning domains, Journal of Artificial Intelligence Research, 20:61-124, 2003.
• E. Plaku, S. Rashidian, and S. Edelkamp: Multi-group motion planning in virtual environments, Comput. Animation Virtual Worlds, 2016.
• Y. Warsame, S. Edelkamp, E. Plaku: Energy-Aware Multi-Goal Motion Planning Guided by Monte Carlo Search, CASE 2020, pp. 335-342.

• slides (pdf): b4m36uir-lec13-slides-opt.pdf
• slides (original pdf 101 MB): b4m36uir-lec13-slides.pdf

— Jan Faigl 2021/01/03 21:05

• slides: b4m36uir-navigation.pdf
• slides 2×2: b4m36uir-navigation-2x2.pdf
• slides 3×3: b4m36uir-navigation-3x3.pdf
• videos and supplementary materials: available on google drive

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

• slides: b4m36uir-slam.pdf
• slides 2×2: b4m36uir-slam-2x2.pdf
• slides 3×3: b4m36uir-slam-3x3.pdf
• videos and supplementary materials: available on google drive

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

• slides: b4m36uir-chronorobotics.pdf
• slides 2×2: b4m36uir-chronorobotics-2x2.pdf
• slides 3×3: b4m36uir-chronorobotics-3x3.pdf
• videos and supplementary materials: available on google drive

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