Difference between revisions of "Robotics"

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(Note on 06/07/2022 Exam)
 
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The following are last-minute news you should be aware of ;-)
 
The following are last-minute news you should be aware of ;-)
 +
* 05/08/2022: [https://chrome.deib.polimi.it/index.php?title=Robotics#Note_on_06.2F07.2F2022_Exam Note on the 06/07/2022 exam grading]
 +
* 05/08/2022: Grades of the [[Media:Grades_20220706.pdf|06/07/2022 call]].
 +
* 21/07/2022: Grades of the Homeworks (updated) are out [[Media:Grades_21-22_HWv2.pdf|find them here!]].
 +
* 20/07/2022: Grades of the Homeworks are out [[Media:Grades_21-22_HW.pdf|find them here!]].
 +
* 07/07/2022: Final results of 15/06/2022 and 06/07/2022 for [[Media:Grades_20220615_laureandi.pdf|Laureandi]].
 +
* 03/07/2022: Results of 15/06/2022 written exam are [[Media:Grades_20220615_tmp.pdf|here]]. They include only the written grades, maximum is 26!
 +
* 03/06/2022: Updated all slides of the course, including last lecture
 +
* 02/06/2022: Last lecture of the course will be online Friday 03/06/2022 from 09:00 to 11:00
 +
* 01/06/2022: Slides updates and new lecture rescheduling ongoing
 +
* 31/05/2022: Poll for the last lecture is [https://forms.gle/p5DghgB87DErQurd6 here!]
 +
* 19/05/2022: The second homework is out!!!! Don't forget to use the Slack channel for requests and suggestions!!!
 +
* 11/05/2022: Updated the detailed schedule of lectures, double check it!!!
 +
* 11/04/2022: Corrected bags and updated slides are now available in the shared folder. New deadline: May 8 2022, 23:59 CEST
 +
* 11/04/2022: All communications regarding the project will be though the shared folder and slack
 +
* 04/04/2022: Hold-on! -> due to a bug in the bags an update of the homework will be available soon
 
  * 30/03/2022: The first homework project of the course is out!
 
  * 30/03/2022: The first homework project of the course is out!
 
  * 06/03/2022: Added link to Paolo Cudrano slides, updated slide first two weeks Matteo Matteucci
 
  * 06/03/2022: Added link to Paolo Cudrano slides, updated slide first two weeks Matteo Matteucci
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|14/04/2022 || Thursday || 14:15 - 16:15 || 26.11 || Matteo Matteucci || Lecture || Localization and Kalman Filters
 
|14/04/2022 || Thursday || 14:15 - 16:15 || 26.11 || Matteo Matteucci || Lecture || Localization and Kalman Filters
 
|-
 
|-
|20/04/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Mapping and SLAM
+
|20/04/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Localization and Kalman Filters
 
|-
 
|-
|21/04/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || Message filters / rospy
+
|21/04/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || rospy, rosbag, message filter, plotjuggler
 
|-
 
|-
|27/04/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Mapping and SLAM
+
|27/04/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Localization and Kalman Filters
 
|-
 
|-
 
|28/04/2022 || Thursday || --- || --- || --- || --- || --- Lauree ---
 
|28/04/2022 || Thursday || --- || --- || --- || --- || --- Lauree ---
 
|-
 
|-
|04/05/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Algorithms for Robot Navigation
+
|04/05/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Mapping and SLAM
 
|-
 
|-
|05/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || ROS on multiple devices
+
|05/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || ROS on multiple machines, time synchronization, Actionlib, latched pub, async spinner
 
|-
 
|-
|11/05/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Algorithms for Robot Navigation
+
|11/05/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Mapping and SLAM
 
|-
 
|-
|12/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || ROS Robot Navigation
+
|12/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || Matteo Matteucci || Lecture || Algorithms for Robot Navigation
 
|-
 
|-
|18/05/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Matteo Matteucci || Lecture || Algorithms for Robot Navigation
+
|18/05/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Simone Mentasti || Laboratory || Robot Navigation, Stage, Gmapping
 
|-
 
|-
|19/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || ROS Robot Navigation
+
|19/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || Robot Navigation (Part II), Robot Localization, mapviz
 
|-
 
|-
|25/05/2022 || Wednesday || 12:15 - 14:15 || 26.11 || Simone Mentasti || Laboratory || IMU Tools and Robot Localization
+
|25/05/2022 || Wednesday ||--- || --- || --- || --- || --- No Lecture ---
 
|-
 
|-
|26/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || <Spare> || <Spare> || <Spare>
+
|26/05/2022 || Thursday || 14:15 - 16:15 || 26.11 || Simone Mentasti || Laboratory || ROS2, foxglove, second project
 
|-
 
|-
|01/06/2022 || Wednesday || 12:15 - 14:15 || 26.11 ||  <Spare> || <Spare> || <Spare>
+
|01/06/2022 || Wednesday || 12:15 - 14:15 || 26.11 ||  Matteo Matteucci || Lecture || Algorithms for Robot Navigation
 
|-
 
|-
 
|02/06/2022 || Thursday || --- || --- || --- || --- || --- No Lecture ---
 
|02/06/2022 || Thursday || --- || --- || --- || --- || --- No Lecture ---
 
|-
 
|-
|08/06/2022 || Wednesday || 12:15 - 14:15 || 26.11 ||  <Spare> || <Spare> || <Spare>
+
|03/06/2022 || Friday || 09:00 - 11:00 || Online ||  Matteo Matteucci || Lecture || Algorithms for Robot Navigation
 
|}
 
|}
  
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Lectures will be based on material from different sources, teachers will provide their slides to students as soon they are available.  
 
Lectures will be based on material from different sources, teachers will provide their slides to students as soon they are available.  
  
===Course Slides===
+
===Course Slides 2021/2022===
  
 
Slides from the lectures by Matteo Matteucci
 
Slides from the lectures by Matteo Matteucci
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*[[Media:Robotics_01_2122_Introduction.pdf|[2021/2022] Introduction to Robotics]]: Introduction to Robotics, definitions, examples and SAP cognitive model.  
 
*[[Media:Robotics_01_2122_Introduction.pdf|[2021/2022] Introduction to Robotics]]: Introduction to Robotics, definitions, examples and SAP cognitive model.  
 
*[[Media:Robotics_02_2122_Sensors_Actuators.pdf |[2021/2022] Sensors and Actuators]]: an overview of most commonly used actuator and sensors in robotics, the DC motor and its characteristics, gears and torque. Sensor classification, common sensors in robotics with pros and cons.
 
*[[Media:Robotics_02_2122_Sensors_Actuators.pdf |[2021/2022] Sensors and Actuators]]: an overview of most commonly used actuator and sensors in robotics, the DC motor and its characteristics, gears and torque. Sensor classification, common sensors in robotics with pros and cons.
*[[Media:Robotics_03_2021_Odometry.pdf |[2020/2021] Robot Odometry]]: Robot Localization intro, direct and inverse kinematics, robot odometry for different kinematics (differential drive, skid steering, Ackerman, etc.).
+
*[[Media:Robotics_03_2122_Odometry.pdf |[2021/2022] Robot Odometry]]: Robot Localization intro, direct and inverse kinematics, robot odometry for different kinematics (differential drive, skid steering, Ackerman, etc.).
*[[Media:Robotics_04_2021_Localization.pdf |[2020/2021] Robot Localization]]: Sensor models, Robot Localization, Bayesian filtering, Kalman Filtering, Monte Carlo Localization.
+
*[[Media:Robotics_04_2122_Localization.pdf |[2021/2022] Robot Localization]]: Sensor models, Robot Localization, Bayesian filtering, Kalman Filtering, Monte Carlo Localization.
*[[Media:Robotics_05_2021_SLAM.pdf |[2020/2021] Simultaneous Localization and Mapping]]: Mapping with known poses, scan matching, EKF-SLAM, FAST-SLAM
+
*[[Media:Robotics_05_2122_SLAM.pdf |[2021/2022] Simultaneous Localization and Mapping]]: Mapping with known poses, scan matching, EKF-SLAM, FAST-SLAM
 
** [https://drive.google.com/drive/folders/1JO8AQIWaOYeW11d9rInox0pZPZG-fdfc?usp=sharing At this link] you can find the videos included in the slides about (simulataneous) localization and mapping  
 
** [https://drive.google.com/drive/folders/1JO8AQIWaOYeW11d9rInox0pZPZG-fdfc?usp=sharing At this link] you can find the videos included in the slides about (simulataneous) localization and mapping  
*[[Media:Robotics_06_2021_MotionControl_tmp.pdf |[2020/2021] Robot Motion Control]]: Introduction to motion control, Virtual Histogram methods, Dynamic Window Approach (+ planning algorithms)
+
*[[Media:Robotics_06_2122_MotionControl.pdf |[2021/2022] Robot Motion Control]]: Introduction to motion control, Virtual Histogram methods, Dynamic Window Approach, Search-based and Sampling-based planners ([https://politecnicomilano.webex.com/webappng/sites/politecnicomilano/recording/655c2629c53a103abfbe0050568193e1/playback here the video recording of last lecture])
 
<!--
 
<!--
 
*[[Media:Robotics_03_Mobile_Robots_Kinematics.pdf |[2015/2016] Mobile Robots Kinematics]]: mobile (wheeled) robot kinematics, holonomic and non holonomic constraints, differential drive model. [https://drive.google.com/open?id=0B5eSI7n7LkDhM3NIRGlNdktRSzA ppsx]
 
*[[Media:Robotics_03_Mobile_Robots_Kinematics.pdf |[2015/2016] Mobile Robots Kinematics]]: mobile (wheeled) robot kinematics, holonomic and non holonomic constraints, differential drive model. [https://drive.google.com/open?id=0B5eSI7n7LkDhM3NIRGlNdktRSzA ppsx]
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<!--Slides from the lectures by Simone Mentasti as well as examples can be found [https://goo.gl/GonArW at this link], for your convenience we publish here the PDF of the lectures, but check the previous link for coding examples:-->
 
<!--Slides from the lectures by Simone Mentasti as well as examples can be found [https://goo.gl/GonArW at this link], for your convenience we publish here the PDF of the lectures, but check the previous link for coding examples:-->
  
Last version of slides from the lectures by Paolo Cudrano are available [https://polimi365-my.sharepoint.com/:f:/g/personal/10457911_polimi_it/Eq7UEjxDOOtNrZtvLSKLrEUBKpga-uGlXx8qkZgJjXQJMg HERE!].
+
Slide from the teaching assistant, including code examples and homework descriptions are available in their respective folders
 
+
* Last version of slides from the lectures by Paolo Cudrano are available [https://polimi365-my.sharepoint.com/:f:/g/personal/10457911_polimi_it/Eq7UEjxDOOtNrZtvLSKLrEUBKpga-uGlXx8qkZgJjXQJMg HERE!].
Last version of slides from the lectures by Simone Mentasti are available [HERE!].
+
* Last version of slides from the lectures by Simone Mentasti are available [https://drive.google.com/drive/folders/1W36wPi0H7kA7qYGiOfbyR5ikBASlyCpZ HERE!].
 
<!--
 
<!--
 
Last version of slides from the lectures by Simone Mentasti are available [https://goo.gl/GonArW HERE!].  
 
Last version of slides from the lectures by Simone Mentasti are available [https://goo.gl/GonArW HERE!].  
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*[http://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf Simultaneous localization and mapping with the extended Kalman filter.] Joan Sola'.  
 
*[http://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf Simultaneous localization and mapping with the extended Kalman filter.] Joan Sola'.  
 
*[http://robots.stanford.edu/papers/Thrun03g.pdf FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data Association.] Sebastian Thrun, Michael Montemerlo, Daphne Koller, Ben Wegbreit, Juan Nieto, and Eduardo Nebot.
 
*[http://robots.stanford.edu/papers/Thrun03g.pdf FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data Association.] Sebastian Thrun, Michael Montemerlo, Daphne Koller, Ben Wegbreit, Juan Nieto, and Eduardo Nebot.
 +
* [https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf The Dynamic Window Approach to Collision Avoidance.] Dieter Fox, Wolfram Burgard, and Sebastian Thrun.
  
 
<!--
 
<!--
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==== Homework 2021/2022 ====
 
==== Homework 2021/2022 ====
  
The First project con the Robotics class is available [https://polimi365-my.sharepoint.com/:f:/g/personal/10457911_polimi_it/EhsMssV_kDBKkp7gY-xGV3gBNGpBpnyoHPR_Gu5eAMebyw?e=2XCRe5 HERE!], deadline is 29/04/2022!
+
* The First project con the Robotics class is available [https://polimi365-my.sharepoint.com/:f:/g/personal/10457911_polimi_it/EhsMssV_kDBKkp7gY-xGV3gBNGpBpnyoHPR_Gu5eAMebyw?e=2XCRe5 HERE!], deadline is 29/04/2022!
 +
* The Second project con the Robotics class is available [https://goo.gl/GonArW HERE!], deadline is 26/06/2022!
  
 
==Frequently Asked Questions==
 
==Frequently Asked Questions==
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==Past Years Useful Material==  
 
==Past Years Useful Material==  
  
Here you find material from past editions of the course that you umight find useful in preparing the exam.
+
Here you find material from past editions of the course that you might find useful in preparing the exam.
  
 
===Past Exams and Sample Questions===
 
===Past Exams and Sample Questions===
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* [[Media:20160720.pdf|Exam of 20/07/2016]]
 
* [[Media:20160720.pdf|Exam of 20/07/2016]]
 
* [[Media:20160627.pdf|Exam of 27/06/2016]]
 
* [[Media:20160627.pdf|Exam of 27/06/2016]]
 +
 +
====Note on 06/07/2022 Exam====
 +
 +
This is a short note on the grading of the [[Media:20220706.pdf|06/07/2022 exam]]. On average has not been different from the others calls except one exercise you might want to know more about, i.e., exercise 1. I take this opportunity to comment on the grading of all exercises so you can get an immediate comment and if there is something missing you can then write to me.
 +
 +
* Exercise 1: the key point here is the use of a "single RGB camera", because of this, we do not have distance measurements and cannot consider the sensor as lidar, sonar, or stereo vision system. Because of this, beam or scan sensor models dedicated to range sensors are not applicable. In this case, you need to use a landmark-based sensor model getting landmarks from the vision system, e.g., a door or a fire extinguisher (examples made multiple times during lectures). As for the localization system any solution which leverages this sensor model is ok, it could be an Extended Kalman Filter, or a Monte Carlo Localization (as the soccer dog example we made in class and you find in the slides), but what is important is to motivate the system on the characteristics of the landmark sensor, not just provide a generic description of a localization algorithm.
 +
* Exercise 2: more or less all exams got the first two points correct, as for the third two options equally correct exist. A) you specify the actuator on the back wheels for forward motion and on the frontal wheels for steering and then you provide the derivation of the Ackerman kinematics; B) you specify that frontal wheels are just caster wheels for support and then you have 2 independent motors on the back deriving then the differential drive kinematics. Partial answers get partial grades. If you are curious the "real" robot is made like B)
 +
* Exercise 3: the decision on the action to choose is based on a scoring function which is different in the cases of 3.1, 3.2, and 3.3. Describing these scoring functions provides full marks, only stating they exist or mixing them up only partial mark, not mentioning these scoring functions zero mark. As for 3.4, the solution is manually (or automatically) tuning the coefficients via trial and errors possibly in simulation.
 +
* Exercise 4: not stating clearly the characteristics of topics, services and actions gives partial credit.
 +
* Exercise 5: most of you got the point, full SLAM requires to estimate the map and the full trajectory. If you want to implement it using an EKF-SLAM algorithm you need to add all the poses to the state of the EKF and estimate jointly the map and the trajectory. This makes the state grow linearly with time and the complexity quadratically with the size of the state ... if you are curios you can check SAM (Smoothing and Mapping) id does exactly this.
 +
* Exercise 6: who has applied A* got full mark, who has just searched for the path and found it by looking at the graph exploring it partially got very low mark. Intermediate marks are give because of incomplete solutions or errors in the execution.
  
 
===Past Course Project===
 
===Past Course Project===

Latest revision as of 18:51, 5 August 2022


The following are last-minute news you should be aware of ;-)

* 05/08/2022: Note on the 06/07/2022 exam grading
* 05/08/2022: Grades of the 06/07/2022 call. 
* 21/07/2022: Grades of the Homeworks (updated) are out find them here!. 
* 20/07/2022: Grades of the Homeworks are out find them here!. 
* 07/07/2022: Final results of 15/06/2022 and 06/07/2022 for Laureandi. 
* 03/07/2022: Results of 15/06/2022 written exam are here. They include only the written grades, maximum is 26! 
* 03/06/2022: Updated all slides of the course, including last lecture
* 02/06/2022: Last lecture of the course will be online Friday 03/06/2022 from 09:00 to 11:00
* 01/06/2022: Slides updates and new lecture rescheduling ongoing 
* 31/05/2022: Poll for the last lecture is here!
* 19/05/2022: The second homework is out!!!! Don't forget to use the Slack channel for requests and suggestions!!! 
* 11/05/2022: Updated the detailed schedule of lectures, double check it!!!
* 11/04/2022: Corrected bags and updated slides are now available in the shared folder. New deadline: May 8 2022, 23:59 CEST
* 11/04/2022: All communications regarding the project will be though the shared folder and slack
* 04/04/2022: Hold-on! -> due to a bug in the bags an update of the homework will be available soon 
* 30/03/2022: The first homework project of the course is out!
* 06/03/2022: Added link to Paolo Cudrano slides, updated slide first two weeks Matteo Matteucci
* 06/03/2022: Added link to last year's recordings
* 23/02/2022: Detailed calendar published
* 23/02/2022: Lectures start today!


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.

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.

Course Program

Lectures will provide theoretical background and real-world examples. Lectures will be complemented with practical software exercises in simulation and on real data 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,
  • Simultaneous 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!!

Note: Lecture timetable interpretation

  • On Wednesday, in 26.11, starts at 12:15, ends at 14:15
  • On Thursday, in 26.11, starts at 14:50, ends at 16:15
Date Day Time Room Teacher Type Topic
23/02/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Course/Robotics Intro
24/02/2022 Thursday 14:15 - 16:15 26.11 Matteo Matteucci Lecture Actuators
02/03/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Sensors
03/03/2022 Thursday 14:15 - 16:15 26.11 Paolo Cudrano Laboratory ROS Install
09/03/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Sensors
10/03/2022 Thursday 14:15 - 16:15 26.11 Paolo Cudrano Laboratory ROS Basics
16/03/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Kinematics
17/03/2022 Thursday 14:15 - 16:15 26.11 Paolo Cudrano Laboratory Pub / Sub
23/03/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Kinematics
24/03/2022 Thursday 14:15 - 16:15 26.11 Paolo Cudrano Laboratory Services and Params
30/03/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Localization and Lidars
31/03/2022 Thursday 14:15 - 16:15 26.11 Paolo Cudrano Laboratory TF / Rviz / Actions
06/04/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Localization and Bayes Filters
07/04/2022 Thursday 14:15 - 16:15 26.11 Matteo Matteucci Lecture Localization and Kalman Filters
13/04/2022 Wednesday --- --- --- --- --- Prove in itinere ---
14/04/2022 Thursday 14:15 - 16:15 26.11 Matteo Matteucci Lecture Localization and Kalman Filters
20/04/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Localization and Kalman Filters
21/04/2022 Thursday 14:15 - 16:15 26.11 Simone Mentasti Laboratory rospy, rosbag, message filter, plotjuggler
27/04/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Localization and Kalman Filters
28/04/2022 Thursday --- --- --- --- --- Lauree ---
04/05/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Mapping and SLAM
05/05/2022 Thursday 14:15 - 16:15 26.11 Simone Mentasti Laboratory ROS on multiple machines, time synchronization, Actionlib, latched pub, async spinner
11/05/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Mapping and SLAM
12/05/2022 Thursday 14:15 - 16:15 26.11 Matteo Matteucci Lecture Algorithms for Robot Navigation
18/05/2022 Wednesday 12:15 - 14:15 26.11 Simone Mentasti Laboratory Robot Navigation, Stage, Gmapping
19/05/2022 Thursday 14:15 - 16:15 26.11 Simone Mentasti Laboratory Robot Navigation (Part II), Robot Localization, mapviz
25/05/2022 Wednesday --- --- --- --- --- No Lecture ---
26/05/2022 Thursday 14:15 - 16:15 26.11 Simone Mentasti Laboratory ROS2, foxglove, second project
01/06/2022 Wednesday 12:15 - 14:15 26.11 Matteo Matteucci Lecture Algorithms for Robot Navigation
02/06/2022 Thursday --- --- --- --- --- No Lecture ---
03/06/2022 Friday 09:00 - 11:00 Online Matteo Matteucci Lecture Algorithms for Robot Navigation


Course Evaluation

Course evaluation is composed by two parts:

  • A written examination covering the whole program graded up to 26/32
  • A home project in simulation practicing the topics of the course graded up to 6/32

The final score will sum the grade of the written exam and the grade of the home project.

Course Project (i.e., the two [2] homeworks)

In the course project, you will use ROS to develop a simple autonomous mobile robot performing simple mapping, localization, and navigation task. The project requires some coding either in C++ / Python following what will be presented during the lectures (we suggest using C++ as it will be the language used in class). The project will be presented in two (2) 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.

Course Slides 2021/2022

Slides from the lectures by Matteo Matteucci

Slide from the teaching assistant, including code examples and homework descriptions are available in their respective folders

  • Last version of slides from the lectures by Paolo Cudrano are available HERE!.
  • Last version of slides from the lectures by Simone Mentasti are available HERE!.

Year 2020/2021 Recording

As I registered these due to pandemics, I am making them available. They DO NOT REPLACE THIS YEAR classroom lectures which are to be considered as the official material of this year, but they might be useful to double-check your notes.

Also, labs are available, however, the organization during the pandemic was kind of different with 2 teams to reduce classroom occupancy. This is why they might resemble a kind of disconnected.

Useful stuff from the web

These are videos from the web which might be useful to understand better the material presented in the lectures

This blog post can be useful to better understand the EKF-SLAM idea and implementation

If you have problems in installing Linux on your machine you can use a USB drive distro and boot on it instead of your OS. Note: We are testing this guide these days we might have some tips and tricks for it so stay tuned!

The ROS framework is C++ based, if you want to check some C++ tutorial online you can have a look at

Useful readings

These are papers which explain some of the topics in the lecture with a higher level of details


Course Projects

Homework 2021/2022

  • The First project con the Robotics class is available HERE!, deadline is 29/04/2022!
  • The Second project con the Robotics class is available HERE!, deadline is 26/06/2022!

Frequently Asked Questions

Course Structure

What is the biggest difference with the course 093217 ROBOTICS AND DESIGN?

  • Robotics and Design is a practical course focused on the development of a robotics application, you will not learn about the theoretical aspects of robotics, but you will build a robot with a purpose which changes every year. I consider the two courses as complimentary.

Exams and Evaluation

Are there any solutions available for the past exams?

  • No, if you have doubts or questions, just send me your proposed solution and I will reply tailoring the answer to your current understanding.

Is it important to buy/read the text book to be able to follow the course? I can’t find it in the library, is there any alternative book?

  • No, it is not required, as from past experience attending classes and checking the material provided y the teachers is enough. Obviously reading the book will provide much more information..

Homeworks and ROS

In the schedule when it says ROS, are these lectures as well or are they practical work i.e. lab/excercise?

  • They are ex-cathedra lectures where you are expected to bring your laptop, it is not mandatory and you can follow the class in a classical passive way, but I suggest to consider it as a lab and take your laptop with you if you can.

Out of all the scheduled activities this semester, approximately how many of these are practical lab/excercise?

  • Indeed not all ROS lectures will present coding exercises, I expect half of them will be about coding and the other half more on the technical background you need to understand what you are coding.

Should I install ROS on my laptop/desktop?

  • Absolutely yes. This means you need to have linux on your machine, possibly ubuntu 16.04 or 18.04. This can be achieved in different ways, we suggest a native install via dual boot or as main operating system (we do not take any responsibility of something happening to your data or hardware in doing this operation). Other options such as virtual machine or live distro are not as effective as a real install, but they work.

Which editor/IDE should I use for ROS?

  • We do not suggest any particular editor for ROS, standard text editors such as nano/gedit/sublime + a terminal are enough. Nevertheless, you can use the environment you prefer for C++ development; some students, in the past, have used Eclipse or Clion. You can also check the list of supported ROS editors or Roboware, the latter has been designed for ROS, but it does not offer any special feature you will miss using standard C/C++ editors.

As I understand the “homework/project” is a group project. Is this correct and how are the groups formed?

  • It is not a group project, while it is allowed to do it in groups (up to 3 people). I expect the groups to form naturally in classes. We usually set up a slack group for the project you can organize autonomously. Nevertheless, you can do the project alone as well (but we advise you to do it in groups).

When “Part 1” of the homework/project will start?

  • Right after we have finished the first block of lectures about ROS. This should happen around Easter plus/minus a week.

Past Years Useful Material

Here you find material from past editions of the course that you might 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.

Note on 06/07/2022 Exam

This is a short note on the grading of the 06/07/2022 exam. On average has not been different from the others calls except one exercise you might want to know more about, i.e., exercise 1. I take this opportunity to comment on the grading of all exercises so you can get an immediate comment and if there is something missing you can then write to me.

  • Exercise 1: the key point here is the use of a "single RGB camera", because of this, we do not have distance measurements and cannot consider the sensor as lidar, sonar, or stereo vision system. Because of this, beam or scan sensor models dedicated to range sensors are not applicable. In this case, you need to use a landmark-based sensor model getting landmarks from the vision system, e.g., a door or a fire extinguisher (examples made multiple times during lectures). As for the localization system any solution which leverages this sensor model is ok, it could be an Extended Kalman Filter, or a Monte Carlo Localization (as the soccer dog example we made in class and you find in the slides), but what is important is to motivate the system on the characteristics of the landmark sensor, not just provide a generic description of a localization algorithm.
  • Exercise 2: more or less all exams got the first two points correct, as for the third two options equally correct exist. A) you specify the actuator on the back wheels for forward motion and on the frontal wheels for steering and then you provide the derivation of the Ackerman kinematics; B) you specify that frontal wheels are just caster wheels for support and then you have 2 independent motors on the back deriving then the differential drive kinematics. Partial answers get partial grades. If you are curious the "real" robot is made like B)
  • Exercise 3: the decision on the action to choose is based on a scoring function which is different in the cases of 3.1, 3.2, and 3.3. Describing these scoring functions provides full marks, only stating they exist or mixing them up only partial mark, not mentioning these scoring functions zero mark. As for 3.4, the solution is manually (or automatically) tuning the coefficients via trial and errors possibly in simulation.
  • Exercise 4: not stating clearly the characteristics of topics, services and actions gives partial credit.
  • Exercise 5: most of you got the point, full SLAM requires to estimate the map and the full trajectory. If you want to implement it using an EKF-SLAM algorithm you need to add all the poses to the state of the EKF and estimate jointly the map and the trajectory. This makes the state grow linearly with time and the complexity quadratically with the size of the state ... if you are curios you can check SAM (Smoothing and Mapping) id does exactly this.
  • Exercise 6: who has applied A* got full mark, who has just searched for the path and found it by looking at the graph exploring it partially got very low mark. Intermediate marks are give because of incomplete solutions or errors in the execution.

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! ;-)

Homework 2020/2021

Here they are the curse homework projects:

  • The first course project has been published on 14/04/2021
    • The description of the first ROS Project is HERE
    • The material for the project is HERE
    • You have to deliver it by 16/05/2021 !!!
  • The second course project has been published on 26/05/2021
    • The description of the second ROS Project is HERE
    • The material for the project is HERE
    • You have to deliver it by 27/06/2021 !!!

Homework 2019/2020

Here they are the curse homework projects:

Homework 2018/2019

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!

Homework

Homework 2016/2017

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!

Homework

Homework 2015/2016

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.

Additional Resources

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:

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