Mobile Robotics for Computer Scientists

Teaching Staff

Meeting Times

  • The class meets Wednesday and Friday 3:30 PM to 4:45 PM
  • Location: 0216 Siebel Center for Computer Science
  • The class counts for 3 credits, and includes coding exercises. There is a 4 credit version for graduate students with additional work.
Instructor's Office Hours
  • Half hour before and after class or by appointment
TA's Office Hours
  • Arun Narenthiran Sivakumar: Wednesday 5:00 PM to 6:00 PM in 1117 Siebel Center
  • Ham Abdul-Rashid: Thursday 4:00 PM to 5:00 PM (Location TBA)

Course Description

This course will introduce students to foundational principles of mobile robotics with a view towards development of robot software. Topics covered will be dynamic modeling, coordinate transformations, principles of operations of different sensors, sensor fusion algorithms including Kalman filters, introduction to Simultaneous Localization and Mapping, and introduction to feedback control for robotics. Prerequisite of CS 225 suggested.


This course will draw from a number of texts but the primary text for the course is:

  • Siegwart et al., Autonomous Mobile Robots (available electronically in UIUC library)
Optional additional reading:
  • Murphy, Introduction to AI for Robotics
  • Kuipers, Quaternions and Rotation Sequences (available electronically in UIUC library)
  • Farrell, Aided Navigation, GPS with high-rate Sensors
  • Kelly, Mobile Robotics: Mathematics, Models, and Methods
  • Dudek and Jenkin, Computational Principles of Mobile Robotics
  • Thrun et al., Probabilistic Robotics
  • A number of papers will also be included in the required reading.

Course Motivation

Robots are computerized systems that perceive and understand the world around them and are capable of navigating the world or manipulating objects to perform tasks. Robots embody intelligence through programmed software-hardware interactions. Unlike a typical computer, robots are able to reposition themselves and manipulate their environments.

The last century has seen an unprecedented growth in manufacturing productivity and quality, largely due to the advent of factory-based robots. These robots accomplish complex manufacturing and assembly tasks in structured and highly controlled indoor environments. In contrast, mobile robots can maneuver themselves to locations where tasks need to be done. This opens the possibility of tackling a much broader variety of tasks in the real-world, outdoor, or loosely structured environments, which are not controlled, and in some cases can be harsh, full of uncertainty, and dynamically changing.

The next age in robotics will be enabled by rapid and profound advances in mobile robotics. The objective of this course is to prepare students in the foundations of mathematical principles, computational algorithms, and systems architecture aspects to enable them to design the next generation of mobile and outdoor robots that accomplish complex tasks in the face of high level of uncertainty.

We are already seeing exciting developments in the mobile robotics. Autonomous driving cars; home-cleaning robots; warehouse robots; GPS-enabled precision agricultural autonomous seeders, harvesters and sprayers; extraterrestrial rovers; and Unmanned Aerial Vehicles are but some examples of field robots. As the century progresses, we will see mobile robots enabling a vast array of exciting applications in domains where fixed robots have not yet had an impact.

In all of these and other emerging applications, the key enabling technology will leverage seamless integration of Cyber and Physical components in compact and self-sufficient robots that are able to communicate and work with each other and humans. Cyber components include software, embedded computers, sensors, and other electronic and computational artifacts; while physical components include hardware (cars, airplanes, power lines) that is subject to the rules of physics (dynamics, kinematics, electromechanics, fluid flows).

Robotic Cyber-Physical Systems (CPS) are expected to achieve the following:

  • Understand, perceive, and model the environment in which they operate
  • Make real-time decisions to meet higher level objectives
  • Kelly, Mobile Robotics: Mathematics, Models, and Methods
  • Ensure the safety of the system and its stakeholders
  • Operate robustly in a wide variety of environments
  • Collaborate with other systems
  • This course is created to provide an introduction to the underlying scientific and engineering principles for the design and automation of mobile field robots.

    Lecture Schedule - available on Canvas

    Module Slides & NOTES

    MODULE 1

    • Introduction to Mobile Robotics

    MODULE 2

    • Robot Dynamics

    MODULE 3

    • Mobile Robot Sensors, Dead Reckoning, and Multi-Sensor fusion

    MODULE 4

    • Symbolic Perception Sensors

    MODULE 5

    • Localization and Mapping

    MODULE 6

    • Principles of Robot Motion Control