03/04/2020: Under Update! Tomorrow we start the new course edition!
- 1 Course Aim & Organization
- 2 Teaching Material (the textbook)
- 3 Past Years Useful Material
Course Aim & Organization
This course will introduce basic concepts and techniques used within the field of autonomous mobile robotics. We analyze the fundamental challenges for autonomous intelligent systems when these move on wheels or legs and present the state of the art solutions currently employed in mobile robots and autonomous vehicles with a focus on autonomous navigation, perception, localization, and mapping.
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
Lectures will provide theoretical background and real world examples. Lectures will be complemented with practical exercises in simulation for all the proposed topics and the students will be guided in developing the algorithms to control an autonomous robot.
Among other topics, we will discuss:
- Mobile robots kinematics,
- Sensors and perception,
- Robot localization and map building,
- Simultaneour Localization and Mapping (SLAM),
- Path planning and collision avoidance.
Detailed course schedule
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all days we have lectures!!
WORK IN PROGRESS [ ... ]
Course evaluation is composed by two parts:
- A written examination covering the whole program graded up to 27/32
- A home project in simulation practicing the topics of the course graded up to 5/32
The final score will sum the grade of the written exam and the grade of the home project.
In some (exceptional) cases the home project could be substituted with a lab project, possibly with a slightly higher grade, but this has to be motivated and discussed with the teacher.
In the course project you will use ROS to develop a simple autonomous mobile robot performing a simple mapping, localization and navigation task. The project requires some coding either in C++ / Python following what will be presented during the lectures. The project will be presented in two parts you have about one month to do each. Details will follow.
Teaching Material (the textbook)
Lectures will be based on material from different sources, teachers will provide their slides to students as soon they are available.
Slides from the lectures by Matteo Matteucci
- [2019/2020] Course Introduction: introductory slides of the course with useful information about the course syllabus, grading, and the course logistics.
- [2019/2020] Introduction to Robotics: Introduction to Robotics, definitions, examples and SAP cognitive model.
- [2018/2019] Sensors and Actuators: an overview of most commonly used actuator and sensors in robotics, the DC motor and its characteristics, gears and torque.
- [2018/2019] Robot Odometry: Robot Localization intro, direct and inverse kinematics, robot odometry for different kinematics (differential drive, skid steering, Ackerman, etc.).
- [2018/2019] Robot Localization: Sensor models, Robot Localization, Bayesian filtering, Kalman Filtering, Monte Carlo Localization.
- [2018/2019] Simultaneous Localization and Mapping: Mapping with known poses, scan matching, EKF-SLAM, FAST-SLAM
- At this link you can find the videos included in the slides about (simulataneous) localization and mapping
- [2018/2019] Robot Motion Control: Introduction to motion control, Virtual Histogram methods, Dynamic Window Approach
Slides from the lectures by Simone Mentasti
- [2018/2019] Middleware in Robotics: Middleware for robotics and ROS Installation Party
- [2018/2019] ROS Environment: Ros workspace, publisher/subscriber
- [2018/2019] ROS Basics: Messages, services, parameters,launch file
- [2018/2019] ROS Tools: Bags, tf, actionlib, rqt_tools
- [2018/2019] Actiolib: Actiolib and message filters
- [2018/2019] ROS on Multiple Machines: how to run ROS nodes on different machines
- [2018/2019] Robot Navigation: ROS Navigation Stack, Movebase, Navcore, Gmapping
- [2018/2019] Opencv/CV_BRIDGE: how to nterface OpenCV and ROS
- [2018/2019] Robot Localization: useful stuff for the course project ;-)
Not yet out ...
Past Years Useful Material
Here you find material from past editions of the course that you umight find useful in preparing the exam.
Past Exams and Sample Questions
Since the 2015/2016 Academic Year the course has changed the teacher and this has changed significantly the program and the exam format as well. For this reason we do not have many past exams to share with you, they will accumulate along the years tho.
- Exam of 17/07/2017
- Exam of 01/07/2017
- Exam of 26/09/2016
- Exam of 05/09/2016
- Exam of 20/07/2016
- Exam of 27/06/2016
Past Course Project
Here you find past course projects in case you are interested in checking what your colleagues have been pass through before you. In some cases they may have been more lucky in some others you might be the lucky one ... that's life! ;-)
The 2018/2019 course project is divided in two releases. The homework philosophy should be "You have to struggle, but not too much!". Indeed the homework is made to challenge you and make you exercising and learn by doing, nevertheless if you find yourself stuck please write us and we will give you the required hints to continue and complete ... this includes extending the deadline (for all) or allowing you to use python instead of C++ (for selected students).
Advice: Start as soon as possible doing the homework!
- 2018/2019 Course Project Part 1: due on Wednesday 29/05/2019, this is the first part of the 2018/2019 course project.
- 2018/2019 Course Project Part 2: due on Monday 08/07/2019, this is the second and last part of the 2018/2019 course project.
The 2016/2017 course project is divided in two releases to provide you something to work on as early as possible during the course. The homework philosophy should be "You have to struggle, but not too much!". Indeed the homework is made to challenge you and make you exercising and learn by doing, nevertheless if you find yourself stuck please write us and we will give you the required hints to continue and complete.
Advice: Start as soon as possible doing the homework!
- 2016/2017 Course Project Part A v1.1: due on Wednesday 31/05/2017 (6 weeks from now), this is the first part of the 2016/2017 course project.
- 2016/2017 Course Project Part B v1.0: due on Wednesday 28/07/2017 (6 weeks from now), this is the second part of the 2016/2017 course project.
- 2016/2017 Model for Course Project part B v1.0: thi si the gazebo model to be used in exercise 4 in the second part of 2016/2017 course project.
This year project is divided in steps; each of them is worth some points out of the 5/32 points available for the final mark. You find the project description here, it is complete, it contains parts up to 4, parts 5 is optional, but we suggest to do it anyway since it requires a limited amount of time.:
- 2015/2016 Course Project v1.0
- 2015/2016 Kobra STL files: in case you want to make your simulation look more real here you find the STL files of the Kobra robot in the "Safer" version. Unfortunately the STL files are scaled down with respect to the real robot, so you have to modify those if you want to use.
If you are interested in a more deep treatment of the topics presented by the teachers you can refer to the following books and papers:
- Probabilistic Robotics by Dieter Fox, Sebastian Thrun, and Wolfram Burgard.
The following are links to online sources which might be useful to complement the material above
- ISO 8373:2012: ISO Standard "Robots and robotic devices -- Vocabulary"
- ROS: the Robot Operating System
- Gazebo: the Gazebo robot simulator
- AIRLab ROS Howto: a gentle introduction to ROS with node template and program examples