Master Artificial Intelligence

Course Vision for Autonomous Robots

This is the information of Fall 2024

This year the course will be given for the first time, by Arnoud Visser and Shaodi You as lecturers, with support of Qi Bi.

Description

The description is available in the course catalogue with code VFAR6Y. The course is a free choice in the Master Artificial Intelligence Curriculum. It assumes that you have finished Computer Vision 1 prior to this course.

This course gives an introduction in the fundamentals of perception algorithms for autonomous robots. 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 spatial transformations, computer vision, artificial intelligence and probability theory.

This course is based on the book Robotics, Vision and Control, supported by Computational Principles of Mobile Robotics.

The assignments are all Python and ROS based.

Schedule

The official schedule should be found at mytimetable or datanose.

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: Lecture - 1st hour - Introduction
Week 44: Lecture and Lab - 2nd hour - Crash course ROS2

Week 45: Lecture - 1st hour - Position and orientation
Week 45: Lecture and Lab - 2nd hour - Line following with Bird-Eye view and Hough Transform

Week 46: Lecture - 1st hour - Time and Motion
Week 46: Lecture and Lab -

Week 47: Lecture - 1st hour - Stereo Vision and Visual Odometry
Week 47: Lecture and Lab -

Week 48: Lecture - 1st hour - Visual Localization and Mapping
Week 48: Lecture and Lab -

Week 49: Lecture - 1st hour - Planning and Navigation
Week 49: Lecture and Lab -

Week 50: Lecture - Latest research Week 50: Lecture - Finalize final project

Week 51: Final exam

Literature

Peter Corke, Robotics, Vision and Control - Fundamental Algorithms in Python, Springer Tracts in Advanced Robotics 146, 3rd edition, May 2023

Gregory Dudek and Michael Jenkin, Computational Principles of Mobile Robotics, Cambridge University Press, 3rd edition, February 2024.

Embedding in AI curriculum

This course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach' 4th edition, by Stuart Russell and Peter Norvig:

Chapter 12: Quantifying Uncertainty
Chapter 13: Probabilistic Reasoning
Chapter 14: Probabilistic Reasoning over Time
Chapter 25: Computer Vision
Chapter 26: Robotics

Software toolkits

the Robot Operating System (ROS)

Last updated October 25, 2024

Course Vision for Autonomous Robots