Course Autonomous Mobile Robots
Bachelor Artificial Intelligence
This is the information of Fall 2016
That year the course was given by Arnoud Visser. The course is still given in
2021-2022, with
Herke van Hoof and
Shaodi You as lecturers.
The information of the year
2015 is also still available.
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
Links
- The Book's webpage.
- The Book's Slides/Exercices page.
- Davide Scaramuzza's Teaching site.
-
Eldgenössische Technische Hochschule Zürich: Vision Algorithms for Mobile Robotics (2021) by Manasi Muglikar, Nico Messikommer and Davide Scaramuzza.
- University of Edinburgh: Introduction to Mobile Robotics (2023) - Chris Lu, Mobile Robotics, Springer, 2003 and Probabilistic Robotics, MIT Press 2005.
- University of Edinburgh: Advanced Robotics (2023) - Ram Ramamoorthy and Steve Tonneau, Modern Robotics, Cambridge Press, 2017 and Introduction to Robotics, Mechanics and Control Pearson 2018.
- University of Edinburgh: Introduction to Vision and Robotics (2021) - Mohsen Khadem, Robotics, Modelling, Planning and Control, Springer, 2009.
-
Australian National University: Robotics (2023) - based on Mark W. Spong, Seth Hutchinson and M. Vidyasager, Robot Modelling and Control, Wiley, 2020.
-
Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2017) by Roland Siegwart, Margarita Chli and Martin Rufli.
-
Tecnico Lisboa: Introduction to Robotics (2018), Pedro Lima.
Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2016) by Roland Siegwart, Margarita Chli and Martin Rufli.
-
Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2015) by Roland Siegwart, Marco Huttler, Mike Bosse, Martin Rufli and Davide Scaramuzza.
- Davide Scaramuzza's old Teaching site (until 2014).
-
Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2012) by Roland Siegwart, Margarita Chli, Martin Rufli and Davide Scaramuzza.
-
Princeton University: Autonomous Robot Navigation (2015) by Dr. Christopher Clark.
-
University of Edinburgh, Intelligent Autonomous Robotics (2016) by Prof. Barbara Webb.
-
Università di Roma La Sapienza: Autonomous and Mobile Robotics (2016) by Prof. Giuseppe Oriolo.
-
Southern Illinois University: Autonomous and Mobile Robotics (2013) by Dr. Henry Hexmoor.
-
Princeton University: Autonomous Robot Navigation (2012) by Dr. Christopher Clark.
-
Southern Illinois University: Autonomous and Mobile Robotics (2012) by Dr. Henry Hexmoor.
-
Washington University in St. Louis: Mobile Robotics (2015) by David V. Lu.
-
Carnegie Mellon University: Introduction to Robotics (2016) by Howie Choset
-
Carnegie Mellon University: Introduction to Robotics Programming (2007) by Alonzo Kelly
-
Carnegie Mellon University: Introduction to Mobile Robotics (2005) by Alonzo Kelly
-
Carnegie Mellon University: Introduction to Mobile Robotics (1997) by Illah R. Nourbakhsh
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 March 5, 2024
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