Mobile Robot Control 2023

'Hero the Toyota HSR'

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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 15:30-17:15 in Gemini-South 3.A10. Guided selfstudy will take place on the Fridays between 10:30-12:15 in the same room. The course schedule is as follows:

Wednesday April 26	Lecture: Introduction to mobile robot control	exercises week 1 practical exercises week 1 Lecture slides
Wednesday May 3	Lecture: Navigation 101
Wednesday May 10	Lecture: Navigation 102
Friday May 12	Guided selfstudy
Wednesday May 17	Lecture: Localization 101	Exercises Localization 101
Wednesday May 24	Lecture: Localization 102	Exercises Localization 102
Friday May 26	Guided selfstudy
Wednesday May 31	Lecture: System architecture
Friday June 2	Lecture: Best practices for C++ and Git
Wednesday June 7	Presentation of designs by the groups
Wednesday June 14	Guest lectures: "Safe navigation in a hospital environment" and "Autonomous parking of trucks"
Friday June 30	Final Challenge
Friday July 7	Deadline: Wiki Pages + Peer review

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

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 1 - visit gitlab - visit wiki

Group 2 - visit gitlab - visit wiki

Group 3 - visit gitlab - visit wiki

Group 4 - visit gitlab - visit wiki

Group 5 - visit gitlab - visit wiki

Group 6 - visit gitlab - visit wiki

Group 7 - visit gitlab - visit wiki

Group 8 - visit gitlab - visit wiki

Group 9 - visit gitlab - visit wiki

Group 10 - visit gitlab - visit wiki

Group 11 - visit gitlab - visit wiki

Group 12 - visit gitlab - visit wiki

Group 13 - visit gitlab - visit wiki

Group 14 - visit gitlab - visit wiki

Group 15 - visit gitlab - visit wiki

Group 16 - visit gitlab - visit wiki

Group Wiki Pages

During the second half of the course. We will ask you to form groups of 6 and you will design your own robotic system with these groups under the guidance of a tutor.

Optimus Prime - visit gitlab - visit wiki - Tutor: Peter van Dooren

Wall-E - visit gitlab - visit wiki - Tutor: Koen de Vos

HAL-9000 - visit gitlab - visit wiki - Tutor: Elise Verhees

R2-D2 - visit gitlab - visit wiki - Tutor: Busra Sen

Rosey - visit gitlab - visit wiki - Tutor: Ruben Beumer

The Iron Giant - visit gitlab - visit wiki - Tutor: Rudolf Huisman

Ultron - visit gitlab - visit wiki - Tutor: César López Martinez

Bender - visit gitlab - visit wiki - Tutor: René van de Molengraft & Jordy Senden

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 2

For week 2 each group can choose 2 time slots. Choose wisely.

Week 2 Tuesday May 2

Time	Group
9:45 - 10:40	free	free
10:45 - 11:40	free	free
11:45 - 12:40	free	free
13:30 - 14:25	free	9
14:30 - 15:25	8	9
15:30 - 16:25	8	free
16:30 - 17:25	free	free

Week 2 Thursday May 4

Time	Group
9:45 - 10:40	free	free
10:45 - 11:40	1	free
11:45 - 12:40	1	free
13:30 - 14:25	free	free
14:30 - 15:25	free	free
15:30 - 16:25	free	free
16:30 - 17:25	free	free

Contact Details

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

Peter van Dooren

Koen de Vos

Ruben Beumer

Busra Sen

Elise Verhees

Gijs van Rhijn

Aron Aertssen

Jordy Senden

César López Martínez

René van de Molengraft

Rudolf Huisman

Elena Torta