Embedded Motion Control 2014 Group 3

Group Members

Name:	Student id:
Jan Romme	0755197
Freek Ramp	0663262
Anne Krus	0734280
Kushagra	0873174
Roel Smallegoor	0753385
Janno Lunenburg - Tutor	-

Final software

LaserProcessing

LaserData from Pico

The data from the laser on pico is in lasercloud format. This means that the data is represented in an array of distances. The starting angle and angle increment are known. This means we have the distances from laser to objects for a range of angles.

LaserCloud to Pointcloud

Because we are going to fit lines through the walls, it would be easier to have the data in Carthesian Coordinates. In this node the laserData is transformed into a PointCloud, which is published on the topic. It is also possible to filter the data in this node when needed. For now all data is transformed into the PointCloud.

Arrow detection

The following steps describe the algorithm to find the arrow and determine the direction:

1 Read rgb image from "/pico/asusxtion/rgb/image_color" topic.

2 Convert rgb image to hsv color space.

3 Filter out the red color using cv::inRange

4 Find contours and convex hulls and filter it

The filter removes all contours where the following relationship does not hold: [math]\displaystyle{ 0.5 \lt \frac{Contour \ area}{Convex \ hull \ area} \lt 0.65 }[/math]. This removes some of the unwanted contours. The contour and convex hull of the arrow:

5 Use cv::approxPolyDP over the contours

The function cv::approxPolyDP is used to fit polylines over the resulting contours. The arrow should have approximately 7 lines per polyline. The polylines fitted over the contours with [math]\displaystyle{ 5 \ \lt \ number \ of \ lines \ in \ polyline \ \lt \ 10 }[/math] is the arrow candidate.

6 Determine if arrow is pointing left or right

First the midpoint of the arrow is found using [math]\displaystyle{ x_{mid} = \frac{x_{min}+x_{max}}{2} }[/math]. When the midpoint is known the program iterates over all points of the arrow contour. Two counters are made which count the number of points left and right of [math]\displaystyle{ x_{mid} }[/math]. If the left counter is greater than the right counter the arrow is pointing to the left, otherwise the arrow is pointing to the right.

7 Making the detection more robust As last an effort is made to make the arrow detection more robust, for example when at one frame the arrow is not detected the program still knows there is an arrow. This is done by taking the last 5 iterations, check if in all these iterations the arrow is detected then publish the direction of the arrow onto the topic "/arrow". If in the last 5 iterations no arrow is seen the arrow is not visible anymore thus publish that there is no arrow onto the topic "/arrow".

Organization

Time survey

Link: time survey group 3

Planning

Week 1 (28/4 - 4/5)

Finish the tutorials

Week 2 (5/5 - 11/5)

Be able to detect walls and convert them to start and end points

Week 3 (12/5 - 18/5)

Finish strategy to be able to successfully finish the competition

Older Software

Overview of first strategy

In the overview the different packages (dotted boxes) and nodes (solid boxes) are displayed. The topics are displayed at the sides of the nodes.

Topic overview

Initial (boolean) safety

The safety node is created for testing. When something goes wrong and Pico is about to hit the wall the safety node will publish a Bool to tell the strategy it is not safe anymore. When the code is working well safety shouldn't be needed anymore.

Obstacle Detection

Finding Walls from PointCloud data

The node findWalls reads topic "/cloud" which contains laserdata in x-y coordinates relative to the robot. The node findWalls returns a list containing(xstart,ystart) and (xend, yend) of each found wall (relative to the robot). The following algorithm is made:
- Create a cv::Mat object and draw cv::circle on the cv::Mat structure corresponding to the x and y coordinates of the laserspots.
- Apply Probabilistic Hough Line Transform cv::HoughLinesP
- Store found lines in list and publish this on topic "/walls"

A visualization of the output (left: laserdata from the real Pico right: detected lines 'walls'):

Select Walls

In FindWalls lines are fitted over all the walls. In selectwalls the walls are filtered to find the two walls in the driving direction. The walls are send as a starting and endpoint. To be able to compare the walls to each other, the begin point is projected on x=0 (at height of Pico). The closest walls left and right of Pico with the same direction are the two walls to use for navigation.
Next part should be in strategy i think
From the grade of the walls compared to Pico a setpoint can be set on a certain distance in x direction, on which pico can correct to drive straight. A setpoint further away leads to smaller corrections compared to a setpoint closer to pico. The setpoint can also be uses for taking the turns.

Overview of second strategy

See diagram, circles represent separate nodes that communicate using topics. Blocks represent functions, which will be written in separate c++ files and included in the "main" c++ file. This way, no one will interfere in code of someone else.

Notes (TODO)

Week3

Combine detection and strategy part
+ Determine waypoint (turnpoint)
- Find door (using end of line)
+ Turn
+ Drive out of maze

Week 4 (week after corridor challenge)

Because of the Elevator project Freek and Anne are not responsible for anything this week.

Jan and Roel will write (optimize) the wall finding algorithm and use it also for the doors. Kushagra will write the safety node.