Course Autonomous Mobile Robots

Bachelor Artificial Intelligence

This is the information of Fall 2016

The information of the previous year could be found here

Description

The description is available in the course catalogue with code AUMR6Y. The course is a free choice in the Bachelor Artificial Intelligence Curriculum.

Contents

This 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.

Schedule

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 1 & 2 - Introduction & Locomotion
   Solve OpenLoop steering assignment, including RWTH Toolbox Installation Instructions.

Week 44: Chapter 3 - Kinematics
   

Week 45: Chapter 4.1 Sensors for Mobile Robots

Week 45: Chapter 4.2 Fundamentals of Computer Vision
    Solve Assignment 3, matlab files, camera snapshots.

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
   Solve Localization assignment, Matlab code, color picker and two locally recorded datasets (one for training and one for testing) Extra: 5 december recordings: (training set and testing set) and the 2014 Dataset
from Areg.

Week 48: Chapter 5.6 Probabilistic Map Based Localization
   

Week 49: Chapter 5.6.8 Kalman Filter Localization including YouTube lecture
and Kalman Filter Geometric Approach (Slides and Dutch recording)
Only slides 9-45. Note the slightly different notation for the intermediate prediction;
   x(k+1|k) by Choset et al.and x(t) by Thrun/Siegwart et al.

Week 49: Chapter 5.8 Simultaneous Localization and Mapping (part I and part II)
YouTube lecture EKF-SLAM, YouTube lecture Monocular SLAM
    Solve Assignment 4, with provided Logger, Example log, Matlab files and a locally recorded dataset (dataset and route (displacements of 15cm at the straight lines)).

Week 50: Chapter 6 - Planning and Navigation (part I and part 2)
YouTube lecture 1, lecture 2, lecture 3, lecture 4, lecture 5.

Week 51: Partial Exam, December 20th, 9:00-11:00, C1.110
   

Literature


Roland Siegwart, Illah R. Nourbakhsh and Davide Scaramuzza 'Introduction to Autonomous Mobile Robots', 2nd edition, The MIT Press, 2011.

Reading guide

  • Monday October 26th: page 56
  • Tuesday October 27th: page 99
  • Tuesday November 2th: page 194
  • Tuesday November 9th: page 264

  • Wednesday November 25th: page 321
  • Wednesday December 2th: page 368
  • Wednesday December 9th: page 424

Embedding in AI curriculum

This course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach' 3rd edition, by Stuart Russell and Peter Norvig:
  • Chapter 13: Quantifying Uncertainty
  • Chapter 14: Probabilistic Reasoning
  • Chapter 15: Probabilistic Reasoning over Time

  • Chapter 24: Perception
  • Chapter 25: Robotics

Evaluation

The course is this year evaluated by the participants with a 5.0:
.

Links

Software toolkits

Chapter 4, section 2.6 (page 186) - Structure from Motion: Chapter 4, section 5 (page 234) - Interest Point Detectors: Chapter 5, section 8 (page 365) - Simultaneous Localization and Mapping algorithms: Bibliography (page 444) - Referenced webpages:
Last updated September 20, 2017

o This web-page and the list of participants to this course is maintained by Arnoud Visser (a.visser@uva.nl)
Faculty of Science
University of Amsterdam

a.visser@uva.nl