Probabilistic Robotics - NAIL101
Title: Pravděpodobnostní robotika
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2023
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Teaching methods: full-time
Additional information: https://dl1.cuni.cz/course/view.php?id=15263
Guarantor: Mgr. Marta Vomlelová, Ph.D.
RNDr. David Obdržálek, Ph.D.
Class: Informatika Mgr. - volitelný
Classification: Informatics > Software Engineering
Is incompatible with: NAIX101
Is interchangeable with: NAIX101
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Annotation -
Last update: T_KSI (13.05.2010)
During its life a robot deals with many problems: It wakes up - without knowing where it is. It is going - without knowing how and where. It is doing - without knowing what and why. These difficulties come from an inaccuracy of sensors and from a complexity of the real world, which cannot be accurately captured by a simple model. Our goal for this class is to familiarize ourselves with various algorithmic methods, which help us with dealing with the uncertainty originating from our and robot's ignorance.
Aim of the course -
Last update: Mgr. Marta Vomlelová, Ph.D. (09.05.2023)

The goal of this course is to get familiar with uncertainity representation and processing with respect to robotics.

Course completion requirements -
Last update: Mgr. Marta Vomlelová, Ph.D. (09.05.2023)

The form of study verification is a credit and an exam.

The credit is granted for submitted solutions to task presented during exercises. The nature of study verification for the credit excludes the possibility of its repetition.

The exam is oral. The demands correspond to the syllabus of the course in the extend that has been presented on lectures and exercises.
Literature - Czech
Last update: T_KSI (13.05.2010)

S. Thrun, W. Burgard, D. Fox: Probabilistic Robotics, MIT Press, 2005

S. Russel, P. Norvig: Artificial Intelligence: A Modern Approach, 3. vydání, Prentice Hall, 2009 (vybrané kapitoly)

Syllabus -
Last update: T_KSI (13.05.2010)
  • A reminder of probability theory
  • Kalman filters and their variants
  • Particle filters
  • Probabilistic localization and mapping
  • Decisioning and planning under uncertainty