Embedded Motion Control 2014 Group 1

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Group Info

Name: Student id: Email:
Groupmembers (email all)
Sander Hoen 0609581 s.j.l.hoen@student.tue.nl
Marc Meijs 0761519 m.j.meijs@student.tue.nl
Wouter van Buul 0675642 w.b.v.buul@student.tue.nl
Richard Treuren 0714998 h.a.treuren@student.tue.nl
Joep van Putten 0588616 b.j.c.v.putten@student.tue.nl
Tutor
Sjoerd van den Dries n/a s.v.d.dries@tue.nl

Meetings

Weekly meetings are planned during the course. Every Wednesday a standard meeting is planned to discuss progress with the group and with the tutor. Presentations from these weekly meetings can be found with the presentation links below. Important meeting decissions can be found with use of the meeting liks below. Next to the standard weekly meetings evening meetings are planned to work as a group on the software design.

  1. Meeting - 2014-05-02
  2. Meeting - 2014-05-12
  3. Meeting - 2014-05-14
  4. Meeting - 2014-05-15
  5. Meeting - 2014-05-16
  6. Meeting - 2014-05-21
  7. Meeting - 2014-05-23
  8. File:Presentatie week 3.pdf
  9. File:Presentation week 4.pdf

Time Table

Fill in the time you spend on this course on Dropbox "Time survey 4k450.xlsx"

Planning

Week 1 (2014-04-25 - 2014-05-02)

  • Installing Ubuntu 12.04
  • Installing ROS
  • Following tutorials on C++ and ROS.
  • Setup SVN
  • Plan a strategy for the corridor challenge

Week 2 (2014-05-03 - 2014-05-09)

  • Finishing tutorials
  • Interpret laser sensor
  • Positioning of PICO

Week 3 (2014-05-10 - 2014-05-16)

Week 4 (2014-05-17 - 2014-05-25)

Week 5 (2014-05-26 - 2014-06-01

  • Test first maze competition software in practice
  • Determine bottlenecks for maze competition
  • Start with arrow detection

Software architecture

We decided to use a architecture as shown in the following figure:
Software-architecture.png

The components with their respective functions and in and outputs are listed here + who wil work on it:

node subscibes topic: input publishes on topic: output Description
Line detection - Sander - laser scan /pico/line_detection lines consisting out of start and end point (x_1,y_1),(x_2,y_2) etc. transformation of raw data to lines by use of hough-transform
Position - Richard line coordinates (X_left, X_right, Y, theta) also named 'relative position' Determine distance to wall to left, right and front wall. Also determines angle theta with respect to the corridor.
Arrow detection camera Arrow left of right determine if pico sees an arrow and in what direction.
state recognition - Joep lines, vision, relative position an integer of whitch state is recognized recognize situation and transform this to one of the states.
setpoint generator - Wouter state, relative position speed and position Create setpoint for position of pico by use of state. (determine wanted position and speed).
Drive - Marc setpoint, relative position x,y,thata (Moving pico) make sure that pico is positioned centered if this is needed and turn when needed.




Line detection - Sander
inputs: --
function: transformation of raw data to lines by use of hough-transform
output: lines consisting out of start and end point (x_1,y_1),(x_2,y_2) etc.

convert laser data to points (x,y)
use hough transform
filter lines

data output format: (richard + sander)

topic: /pico/lines

msg: lines



relative distance - Richard
input topic: /pico/lines
function: Determine distance to wall to left, right and front wall. Also determines angle theta with respect to the corridor.
output: (Y_left, Y_right, X, theta)
output topic: /pico/dist

msg: dist


The angle theta can be calculated with the next fomula:

[math]\displaystyle{ \theta = atan((y2-y1)/(x2-x1)) }[/math]

the position perpendicular to the line/wall is calculated with the next formula:

[math]\displaystyle{ X_r = x2 - ((y2-y1)/(x2/x1))*y2*sin(\theta_1) }[/math]

[math]\displaystyle{ X_l = x4 - ((y4-y3)/(x4/x3))*y4*sin(\theta_2) }[/math]

theta is average of left and right or only left or right depending on situation



Drive - Marc
inputs: setpoint, relative position
function: make sure that pico is positioned centered if this is needed and turn when needed.
outputs: (Moving pico)


Situation - Wouter
inputs: lines, vision

Lines can be categorized in two types of lines:
Longitudinal lines: y-coordinates of begin and end point are similar
Lateral lines: x-coordinates of begin and end point are similar

Situations to be recognized:

- inbetween two walls
No obstacles in front, no lateral line detected within X meter.
2 longitudinal lines are detected.

- Junction
3 lines are detected. From which two are longitudinal lines and one is lateral within (X meter).
Detect direction of juction by comparing the x -values of the longitudinal lines with the x-value of the lateral line.
Left junction: When the x value of the left line (the line with the smallest Y values) is 'minimum corridor width' smaller then the x value of the lateral line a gap on the left side is recognized.
Right junction: When the x value of the right line (the line with the smallest Y values) is 'minimum corridor width' smaller then the x value of the lateral line a gap on the left side is recognized.

- Dead end
3 lines are detected. From which two are longitudinal lines and one is lateral within (X meter).
Detect direction of dead end by comparing the x -values of the longitudinal lines with the x-value of the lateral line. When the x values of both longitudinal lines are similar to those of the lateral line a dead end can be recognized.

- T junction: 3 situations named T-right, T-left, T-right-left.
T-right: 3 longitudinal lines are detected, 1 lateral lines detected on the right side of pico.
T-left: 3 longitudinal lines are detected, 1 lateral lines detected on left side of pico.
T-right-left: 2 longitudinal lines are detected: 4 lateral lines are detected

- X junction
4 longitudinal and 4 lateral lines are detected


State generator - Joep
input: Situation, relative position
input topic: /pico/sit

function: Create setpoint for position of pico by use of state. (determine wanted position and speed).
output:
output topic: /pico/

msg:

PICO states corridor challenge

For the robot, the internal states can be visualized as in the following figure:
Automaton corridor01.png