Final Design & Considerations Embedded Motion Control 2012

Group 7:

Siddhi Imming Bart Moris Roger Pouls Patrick Vaes

Eindhoven, June 29, 2012

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Overview of the program structure for Tremaux’s algorithm with arrow detection

PAGE 1 29-6-2012

Jazz

Hough Line

detection

Laser message

Line information

Odometry

map

Create dump map

Request Dump block

pose

SLAM Read dump

map

Visits front, back, left, right

Image_raw

Request arrow detection

Navigation

Detect arrows

velocity

velocity

Line detection

• Line detection based on laser data and Hough Transform

• Discretized parameter space with appropriate resolution

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jazz 0 °

45° 90°

jazz 0 °

45°

90° Angle Point 1 Point 2 Point 3 0 ° 5 3 1 45° 7 7 7 90° 5 7 10

Line detection

• Line detection based on laser data and Hough Transform

• Discretized parameter space with appropriate resolution

• Filter to find lines as local maxima in parameter space

• Appoint points to corresponding line

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SLAM / Mapping

• Create map based on SLAM Gmapping • Mapping based on re-observing landmarks and

estimated positions based on laser and odometry data, weighted with Kalmann filter

• Proven that it works, and reuse of what is invented is the idea of ROS

• Overlay of original map • Containing blocks with an integer indicating number

of visits • Based on synchronized original map and pose

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Dump Map

SLAM / Mapping

• Create map based on SLAM Gmapping • Mapping based on re-observing landmarks and

estimated positions based on laser and odometry data, weighted with Kalmann filter

• Proven that it works, and reuse of what is invented is the idea of ROS

• Overlay of original map • Containing blocks with an integer indicating number

of visits • Based on synchronized original map and pose

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Dump Map

Arrow detection

• Overview

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Image node

Convert HSV

Blowup 2x Correct Image

Convert BW

Watershed segmentation Left, Right or

no Arrow found

Match shapes

Corner detection

Use biggest contour as

mask

Arrow detection

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Image pico Correct Image

Arrow detection

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Convert HSV Blowup 2x

Arrow detection

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Blowup 2x Correct Image

Arrow detection

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Convert HSV

Convert BW

Arrow detection

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Convert BW

Watershed Segmentation

color

Watershed Segmentation

BW

Arrow detection

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Watershed segmentation

Left, Right or no Arrow found

Match shapes

Corner detection

• Check first distance to parallel lines • Check for line at 90 degrees to parallel lines and if

we are close enough, half forward speed, determine exit 3 times, put dump, else drive parallel between lines

• Exit detection and handling • Detection of type via distance between points on line

and position of the line relative to Jazz • If necessary, check for arrow • Request number of visits at each exit • Take decision, according to algorithm

Navigation node

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Navigation node

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• Parallel driving •Corridor divided in several zones, with different correction speeds to enlarge safety

Robustness measures

• Several drive strategies implemented: • Left hand rule, right hand rule, Tremaux’s rule with

random and fixed order of preference, influenced by detected arrow

• Forward speed is maximal at the center of the corridor and is lower near a wall

• Angular corrections in a corridor are with a low angular speed to prevent ‘overshoot’

• Initialization after startup and when no lines are detect for some time

• Entrance is blocked, using the dump map

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