Course Autonomous Mobile Robots

Bachelor Artificial Intelligence

This is the information of Fall 2013

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.

Schedule

The official schedule should be found here. The Studio Class Room is scheduled on Monday and Thursday, from 9u00 to 13u00. The Studio Class Room will be a combination of lectures, book exercises and assignments. The course will take place in A1.30. A list of Frequent Asked Questions will be maintained. 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.

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
   Solve Localization assignment, Matlab code, color picker and two locally recorded datasets (one for training and one for testing).

Week 48: Chapter 5.6 Probabilistic Map Based Localization
   

Week 49: Chapter 5.6.8 Kalman Filter Localization
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
    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: Large-Scale 3D Point Clouds - Introduction, Basic Data Structures, Registration and Reconstruction.

Week 50: Chapter 6 - Planning and Navigation

Week 51: Partial Exam, December 20th, 13:00-15:00, G3.02
   

Literature


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

Reading guide

  • Monday October 28th: page 56
  • Wednesday October 30th: page 99
  • Wednesday November 6th: page 194
  • Friday November 15th: page 264

  • Thursday November 28th: page 321
  • Thursday December 5th: page 368
  • Tuesday December 10th: 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 6.4:
.

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 January 6, 2014

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

6 visitors in August 2007 arnoud@science.uva.nl