Mobile Robot Control 2024

'Hero the Toyota HSR'

---

Introduction

This course is about software design and how to apply this in the context of autonomous robots. The accompanying assignment is about applying this knowledge to a real-life robotics task.

Course Schedule and Lecture Slides

Lectures will typically take place on the Wednesdays between [todo] in [todo]. Guided self-study will take place on the [todo] between [todo] in the [todo]. The course schedule is as follows:

Date
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]
[DAY/DATE]	[LECTURE TITLE]	[SLIDES & EXERCISES]

Getting Started

To get started, you can follow the installation instructions found in the exercises for week 1. You can already do this before the first lecture.

Here we will collect the Frequently Asked Questions. Please check this page before contacting the student assistants or the tutors! If you find any issues or questions you had to deal with, please add them as well so your colleagues don't run into the same problems.

Restaurant Competition

Overview of a possible restaurant setup, not up to scale!

Challenge Description

The figure on the right shows a 2D representation of a possible Restaurant setup, as an example. The objective is for Jackal to "deliver" orders from the kitchen to a few tables. Which tables must be reached and in what order will be defined by the judges just before the challenge starts. The restaurant will contain a number of unknown static and dynamic objects (boxes, human actors walking)

The delivery of an order is defined as follows

• Drive up to the table.
• Position near the table, facing towards the table. The robot should be close enough for a customer to comfortably take their order from the tray. The exact part of the table that the robot stands next to does not matter.
• Give a clear sound signal, signalling Jackal has arrived at table A (io.speak("I arrived at table four")).
• Repeat until all the tables are visited in the correct order (your robot does not need to return to the starting point)

Environment Specifications

• All walls in the restaurant will be approximately straight. No weird curving walls.
• The tables can be regarded to be solid objects that will show up as rectangles in the LiDAR measurements (So you won't have to detect the table's legs).
• The doors inside the restaurant will be openings in the walls of about 0.5-1m that may be closed or open. Doors can be opened by standing in front of one and having the robot ask for it to be opened.
• There may be multiple routes to a given goal.
• A number of dynamic objects will be present in the form of human actors. Additionally, a number of static objects will be placed throughout the restaurant (including possible chairs next to the tables!). The position does not have to be parallel to the walls.
  • Chairs are Not guaranteed to show up as squares in your LiDAR measurements (you might only see the legs!).
  • These extra objects will not be present on the map that you're provided ahead of time.

Challenge Conditions

• Jackal will start in the start area, defined by a rectangle of approximately 1 by 1 meters. The orientation of Jackal is arbitrary (i.e., not known to your software).
• The list of tables to be visited will be provided right before the challenge starts as a list of integers (0 identifies the first table in the array).
• After starting the software, Jackal has to drive to the first table to deliver the order.
• If Jackal found the correct table and signalled his arrival, he has to drive to the next tables to deliver the orders.
• The task is completed after Jackal visited all tables on the list.
• Bonus points are given to the groups that can detect the static and dynamic objects and present them in the world model. How this is presented is left to the groups.
• Within the restaurant start area, we will make sure that some visible features (i.e. lines, corners) remain visible.
• An actual map of the restaurant will be provided to the teams one week before the final challenge, this will encompass a vector map and a gridmap (an example is provided at the bottom of this section).
• Any outside sensing systems, such as the Opti-track, that might have been available during testing will not be available during the final challenge.

Challenge Rules

• The list of tables to visit has to be supplied to the executable when starting the challenge, in the following format (for tables in the order: 2 -> 4 -> 3):

./Jackal_do_your_thing 2 4 3 

• Do not touch the walls or objects! Slightly touching is allowed, however, bumping (i.e., driving head-on into a wall) is not allowed! If Jackal hits the wall, we decide whether it counts as bumping.
• Every team has two trials (= max one restart). A trial ends if:
  • Jackal bumps into: the wall, a static or a dynamic object.
  • Jackal has not moved or has not made sensible movements (as judged by the tutors) for 30 seconds
  • The total time limit of 10 minutes per group is reached
  • The group requests a restart (on the first trial)
• restart means:
  • Jackal restarts at the defined start position
  • The trail time (= the time graded) is reset, but
  • the total time keeps running
• Maximum speed (is limited in Jackal): 0.5 m/s translational, 1.2 rad/s rotational.
• There will be no second attempt if first attempt was successful
• Every situation that might occur, that is not covered in this document will be evaluated on the spot. If this happens, the judges have the final word.

Robot Software

• Make sure your software is easy to set-up, i.e:
  • Your software can be updated with one easy command, e.g. 'git pull'
  • Your software can be compiled using 'cmake' and 'make'
  • It is allowed to use multiple executables.
  • If your set-up deviates from this method, let your tutor know 1 week before the challenge!
• The software of all groups will be updated on the robot the morning before the challenge starts
  • This way, teams starting the challenge have as much time as teams that do the challenge at the end, compiling in between trials is not allowed.

Example map format and code

• We provide a simple example of a room with two tables and the code to read the map into your own C++ code.
• For this simple example, a simulator map is also provided. (Note: a simulator map will not be provided for the final challenge).
• We used the 20cm thickness blocks for your convenience
• Remember to add unknown objects to your simulator and test environments and/or create other challenging maps and test scenarios!

You can find an example map (JSON) and the code to get you started here: File:Mrc map format 2021.zip

• 

  Overview of example map.

• 

  Visualisation of vector map.

• 

  Bitmap representation of map, used for simulator (see zip file).

An example map (PNG) for the restaurant challenge with more tables is provided here: File:ExampleRestaurantMap.png.

The corresponding data that you could use in a JSON-file is provided here (click 'Expand'):

{
  "tables":[
    [
      [29, 35],
      [35, 34],
      [34, 28],
      [28, 29]],

    [
      [45, 47],
      [47, 46],
      [46, 44],
      [44, 45]],

    [
      [33, 32],
      [32, 21],
      [21, 22],
      [22, 33]],

    [
      [48, 49],
      [49, 59],
      [59, 58],
      [58, 48]],

    [
      [26, 24],
      [24, 25],
      [25, 27],
      [27, 26]],

    [
      [42, 36],
      [36, 37],
      [37, 43],
      [43, 42]],

    [
      [50, 51],
      [51, 62],
      [62, 61],
      [61, 50]]
  ],

  "walls":[
    [0, 1],
    [1, 8],
    [8, 2],
    [2, 0],
    [2, 3],
    [3, 55],
    [55, 54],
    [54, 2],
    [7, 8],
    [8, 64],
    [64, 63],
    [63, 7],
    [54, 56],
    [56, 66],
    [66, 65],
    [65, 54],
    [57, 60],
    [60, 68],
    [68, 67],
    [67, 57],
    [61, 64],
    [64, 70],
    [70, 69],
    [69, 61],
    [13, 14],
    [14, 19],
    [19, 18],
    [18, 13],
    [15, 16],
    [16, 23],
    [23, 20],
    [20, 15],
    [11, 12],
    [12, 31],
    [31, 30],
    [30, 11],
    [6, 10],
    [10, 17],
    [17, 9],
    [9, 6],
    [38, 40],
    [40, 41],
    [41, 39],
    [39, 38]
  ],

  "doors":[
    [
      [4, 5],
      [5, 12],
      [12, 11],
      [11, 4]],

    [
      [30, 31],
      [31, 39],
      [39, 38],
      [38, 30]]
  ],

  "start_area":[
    [
      [52, 53],
      [53, 67],
      [67, 66],
      [66, 52]]
  ],

  "points":[
    {"x": 0.0, "y": 5.0, "_comment": 0 },
    {"x": 6.0, "y": 5.0, "_comment": 1 },
    {"x": 0.0, "y": 4.8, "_comment": 2 },
    {"x": 0.2, "y": 4.8, "_comment": 3 },
    {"x": 3.7, "y": 4.8, "_comment": 4 },
    {"x": 3.9, "y": 4.8, "_comment": 5 },
    {"x": 5.1, "y": 4.8, "_comment": 6 },
    {"x": 5.8, "y": 4.8, "_comment": 7 },
    {"x": 6.0, "y": 4.8, "_comment": 8 },
    {"x": 4.8, "y": 4.5, "_comment": 9 },
    {"x": 5.8, "y": 4.1, "_comment": 10 },
    {"x": 3.7, "y": 4.0, "_comment": 11 },
    {"x": 3.9, "y": 4.0, "_comment": 12 },
    {"x": 0.2, "y": 3.8, "_comment": 13 },
    {"x": 1.5, "y": 3.8, "_comment": 14 },
    {"x": 2.3, "y": 3.8, "_comment": 15 },
    {"x": 3.7, "y": 3.8, "_comment": 16 },
    {"x": 5.5, "y": 3.8, "_comment": 17 },
    {"x": 0.2, "y": 3.6, "_comment": 18 },
    {"x": 1.5, "y": 3.6, "_comment": 19 },
    {"x": 2.3, "y": 3.6, "_comment": 20 },
    {"x": 2.4, "y": 3.6, "_comment": 21 },
    {"x": 2.9, "y": 3.6, "_comment": 22 },
    {"x": 3.7, "y": 3.6, "_comment": 23 },
    {"x": 4.8, "y": 3.6, "_comment": 24 },
    {"x": 5.8, "y": 3.6, "_comment": 25 },
    {"x": 4.8, "y": 3.1, "_comment": 26 },
    {"x": 5.8, "y": 3.1, "_comment": 27 },
    {"x": 0.2, "y": 3.0, "_comment": 28 },
    {"x": 1.2, "y": 3.0, "_comment": 29 },
    {"x": 3.7, "y": 3.0, "_comment": 30 },
    {"x": 3.9, "y": 3.0, "_comment": 31 },
    {"x": 2.4, "y": 2.6, "_comment": 32 },
    {"x": 2.9, "y": 2.6, "_comment": 33 },
    {"x": 0.2, "y": 2.5, "_comment": 34 },
    {"x": 1.2, "y": 2.5, "_comment": 35 },
    {"x": 4.8, "y": 2.3, "_comment": 36 },
    {"x": 5.8, "y": 2.3, "_comment": 37 },
    {"x": 3.7, "y": 2.2, "_comment": 38 },
    {"x": 3.9, "y": 2.2, "_comment": 39 },
    {"x": 3.7, "y": 1.8, "_comment": 40 },
    {"x": 3.9, "y": 1.8, "_comment": 41 },
    {"x": 4.8, "y": 1.8, "_comment": 42 },
    {"x": 5.8, "y": 1.8, "_comment": 43 },
    {"x": 0.2, "y": 1.7, "_comment": 44 },
    {"x": 1.2, "y": 1.7, "_comment": 45 },
    {"x": 0.2, "y": 1.2, "_comment": 46 },
    {"x": 1.2, "y": 1.2, "_comment": 47 },
    {"x": 2.4, "y": 1.2, "_comment": 48 },
    {"x": 2.9, "y": 1.2, "_comment": 49 },
    {"x": 4.6, "y": 1.2, "_comment": 50 },
    {"x": 5.1, "y": 1.2, "_comment": 51 },
    {"x": 1.2, "y": 1.0, "_comment": 52 },
    {"x": 2.2, "y": 1.0, "_comment": 53 },
    {"x": 0.0, "y": 0.2, "_comment": 54 },
    {"x": 0.2, "y": 0.2, "_comment": 55 },
    {"x": 1.2, "y": 0.2, "_comment": 56 },
    {"x": 2.2, "y": 0.2, "_comment": 57 },
    {"x": 2.4, "y": 0.2, "_comment": 58 },
    {"x": 2.9, "y": 0.2, "_comment": 59 },
    {"x": 3.6, "y": 0.2, "_comment": 60 },
    {"x": 4.6, "y": 0.2, "_comment": 61 },
    {"x": 5.1, "y": 0.2, "_comment": 62 },
    {"x": 5.8, "y": 0.2, "_comment": 63 },
    {"x": 6.0, "y": 0.2, "_comment": 64 },
    {"x": 0.0, "y": 0.0, "_comment": 65 },
    {"x": 1.2, "y": 0.0, "_comment": 66 },
    {"x": 2.2, "y": 0.0, "_comment": 67 },
    {"x": 3.6, "y": 0.0, "_comment": 68 },
    {"x": 4.6, "y": 0.0, "_comment": 69 },
    {"x": 6.0, "y": 0.0, "_comment": 70 }
  ]
}

You can use the following settings (resolution etc.) in your YAML-file:

image: ExampleRestaurantMap.png #include the (relative) path to where you put the PNG-file 
resolution: 0.0125
origin: [0.0, 0.0, 0.0]
occupied_thresh: 0.9
free_thresh: 0.1
negate: 0

A distorted version of this map, with slightly displaced walls and tables and some added obstacles, is provided here: File:ExampleRestaurantMapDistorted.png.

Map For The Final Challenge

The map for the final challenge will be published here in the week leading up to the final challenge. Clutter will be added (both static and moving) on the day of the challenge, adhering to the rules specified under "Restaurant Challenge".

Exercise Group Wiki Pages

During the course we will ask you to form groups of 6. In the first part of the course you will do exercises that should prepare you for the final challenge. These are the groups in which you will be doing exercises during the first half of the course. Make sure to write your answers to the exercises on your wiki page and feel free to include as many videos and pictures of driving robots as you want.

Group 01 - visit gitlab - visit wiki - (not yet claimed)

Group 02 - visit gitlab - visit wiki - (not yet claimed)

Group 03 - visit gitlab - visit wiki - (not yet claimed)

Group 04 - visit gitlab - visit wiki - (not yet claimed)

Group 05 - visit gitlab - visit wiki - (not yet claimed)

Group 06 - visit gitlab - visit wiki - (not yet claimed)

Group 07 - visit gitlab - visit wiki - (not yet claimed)

Group 08 - visit gitlab - visit wiki - (not yet claimed)

Group 09 - visit gitlab - visit wiki - (not yet claimed)

Group 10 - visit gitlab - visit wiki - (not yet claimed)

Group Wiki Pages

In the second part of the course you will design your own robotic system with these groups under the guidance of a tutor. As part of your final grade we ask you to report on your execution of the assignment using the wiki pages below.

Group name	GitLab page	Wiki page	Tutor
Wall-E	GitLab	visit wiki	César López Martínez
HAL-9000	visit GitLab	visit wiki	Jordy Senden & René van de Molengraft
R2-D2	visit GitLab	visit wiki	Rudolf Huisman & Aron Aertssen
Rosey	visit GitLab	visit wiki	Ruben Beumer
The Iron Giant	visit GitLab	visit wiki	Koen de Vos & Gijs van Rhijn
Ultron	visit GitLab	visit wiki	Peter van Dooren

With your wiki page you will convey your approach and your lessons-learned to ‘the outside world’; the tutors and the next generation of MRC students. There are two ways to do this: 1) readable code with comments and 2) a detailed description of your system on the wiki.

There is no strict layout for the wiki, but we do impose a maximum length of 10.000 words!

We give some guidelines and topics that we will be looking for, but encourage you to be creative: what separates your group from the others?

At the end of the course the tutors will look at:

Structure of the wiki page

• Is there an introduction/conclusion?
• Is there a logical ordering storywise?
• Interpretability/readability: proper grammar, use of figures etc.

Strategy description Restaurant challenge

• Is the behaviour described logical and is the description complete
• Reasoning of the used algorithms? Were the algorithms sufficient or too simplistic?
• Detailed description of the algorithms

Software Architecture Restaurant challenge

• Logical interaction between software components
• Are the components implemented correctly

Robustness Restaurant challenge

• How was the performance verified
• What kind of tests were performed to check robustness
• Comments on robustness

Evaluation Restaurant challenge

• Reflection on performance
• Conclusions about what could have gone better

Peer review

At the end of the project we would like each group to hand in a peer review. This should reflect how each group member contributed to the final project. We expect each group member to contribute equally in the technical aspects of the project.

Please consider your peers contributions in the form of:

• Technical expertise
• System design
• Implementation in software
• Documentation of the project
• Comminication during the project

Each of these is of roughly equal importance.

We would like to ask the group to come up with a grade for each member ranging from -1.0 to +1.0 where the sum of all grades is 0.0. Your final grade will be calculated as group_grade + peer_review_grade.

Practical sessions

• Testing takes place on the RoboCup field in Impuls.
• Three robots will be available for testing, you will share the field with three groups
• Be sure you have your software on git before coming to the test session so that you only have to git clone/git pull to get your code on the robot!
• Please charge the robot whenever possible so there is no down time due to empty batteries.

To submit for a timeslot you have to be logged in. Through the 'edit'-button for Practical sessions, you can select a timeslot by typing 'Group <groupnumber>' behind the desired timeslot.

• You may only reserve 2 test slots per week
• Submissions are last checked the day before at 22:00.

Week #

For week # each group can choose 2 time slots.

Week # Tuesday [MONTH]

Time	Group
[time]	free	free
[time]	free	free
[time]	free	free
[time]	free	free
[time]	free	free
[time]	free	free
[time]	free	free

Contact Details

This year's staff consists of the following TU/e employees:

• Peter van Dooren (p dot v dot dooren at tue dot nl)
• Koen de Vos
• Aron Aertssen
• Ruben Beumer
• Gijs van Rhijn
• Gijs van den Brandt
• Rudolf Huisman
• Robert de Bruijne
• Jordy Senden
• René van de Molengraft
• Elena Torta