Course Autonomous Mobile Robots
Bachelor Artificial Intelligence
This is the information of Fall 2016The information of the previous year could be found here
ContentsThis course gives an introduction in the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The focus will be on the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks. It synthesizes material from the fields of kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence and probability theory.
This course is based on the book 'Introduction to Autonomous Mobile Robots', from Prof. Dr. Roland Yves Siegwart, Prof. Dr. Illah R. Nourbakhsh and Prof. Dr. Davide Scaramuzza. The assignments are all based on the Matlab environment. This YouTube lectures give a short introduction to the Matlab environment: the workspace, variables, vectors, colon operator, matrices, concatenating, matrix initialization.
The official schedule should be found at mytimetable or datanose. The Studio Class Room is scheduled on Thursday and Friday, from 9u00 to 13u00 (first half). The schedule in the second half is condensed to two weeks (Monday, Tuesday and Thursday). The Studio Class Room will be a combination of lectures, book exercises and assignments. Chapter 1-4 of the book will be introduced by Toto van Inge. Chapter 5-6 will be covered by Arnoud Visser.
Students, who were not able to attend a lecture, can catch up by listing to the recordings of my lectures (in Dutch). Download Lecturnity Player and listen to lecture, synchronized with the sheets.
For the assignments not only a solution is expected, but also a rational. The experiments performed to solve the given problem should be described in a lab report, which will be graded based on the following criteria.
Week 44: Chapter 3 - Kinematics
Week 45: Chapter 4.1 Sensors for Mobile Robots
Week 46: Chapter 4.3-4.5 Feature Extraction
Week 46: Chapter 4.6-4.7 Place Recognition
Week 47: Partial Exam
Week 48: Chapter 5.1-5.5 The Challenge of Localization including YouTube lecture
Week 48: Chapter 5.6 Probabilistic Map Based Localization
Week 49: Chapter 5.6.8 Kalman Filter Localization including YouTube lecture
Week 49: Chapter 5.8 Simultaneous Localization and Mapping (part I and part II)
Week 51: Partial Exam, December 20th, 9:00-11:00, C1.110
Roland Siegwart, Illah R. Nourbakhsh and Davide Scaramuzza 'Introduction to Autonomous Mobile Robots', 2nd edition, The MIT Press, 2011.
Embedding in AI curriculumThis course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach' 3rd edition, by Stuart Russell and Peter Norvig:
The course is this year evaluated by the participants with a 5.0:
Software toolkitsChapter 4, section 2.6 (page 186) - Structure from Motion:
Last updated September 20, 2017
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