Difference between revisions of "Embedded Motion Control 2012 Group 7"
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====Step 4.) Find biggest contour====
====Step 4.) Find biggest contour====
We in the binary image for closed contours, i.e. for object pixels that are connected with each other, using the ''FindContours''function of OpenCV. For each contours we calculate the area and store the biggest contour. We use a threshold value (a minimum area) the biggest object must have to proceed with the algorithm. Since from a far distance the shapes of an arrow is not clearly visible.
Revision as of 14:59, 21 June 2012
|Group Members -||email (at student.tue.nl)||Sofware status|
|Siddhi Imming||s.imming||All software installed|
|Bart Moris||b.moris||All software installed|
|Roger Pouls||r.c.e.pouls||All software installed|
|Patrick Vaes||p.r.m.p.vaes||All software installed|
Rob Janssen - R dot J dot M dot Janssen at tue dot nl
|General||Weekly meetings with group on Tuesday, Wednesday and Thursday. |
At least a weekly update of the wiki.
Tasks are divided weekly.
|Week 3 (7 may)||Write code to create a 2D map from the laser data. |
Write code for position control. Read chapter 9 from the book.
|Navigation team||Image processing team|
|Week 4 (14 may)||Recognize the direction of walls|
Recognize opening in walls
|Install webcam in ROS gscam.|
Get familiar with openCV for image processing.
|Week 5 (21 may)||Write code for the navigation for the corridor competition. |
Make sure the Jazz recognizes exits.
Prepare the lecture.
|Make algorithm in MATLAB on example images.|
|Week 6 (28 may)||Give the lecture. |
Finish and test the navigation for the corridor competition.
Make sure Jazz takes the first exit.
|Implement MATLAB algorithm in ROS with openCV.|
|Week 7 (4 jun)||Corridor competition.|
Write code for image recognition.
Make sure arrows and their pointing direction are recognized.
Discuss and choose a strategy for solving the maze.
Start implementing the strategy into the navigation code.
|Test algorithm with the Jazz Robot.|
Make images with Jazz Robot and test in MATLAB.
Improve algorithm and test on Jazz Robot.
|Week 8 (11 jun)||Continue implementing the strategy into the navigation code.|
|Week 9 (18 jun)||Finish implementing the strategy into the navigation code.|
|Week 10/11 (25 jun / 2 jul)||Final competition.|
Week 1 + 2
|Ubuntu installation||Eclipse installation||Jazz Simulator installation||C++ tutorial||ROS tutorial|
Our main goal is to be able to solve the maze Jazz will be faced with during the final competition. As the philosophy in the world of robotics and also behind the ROS platform is to share knowledge and to use knowledge of others in order to speed up the design process. Of course, we take care of not just plugging in some code designed by others without really understanding what is going on and on what principles the process is based on. So our focus will be on designing a robust and fast navigation algorithm, rather than designing all kinds of new implementations of really nice functions that are already available for ROS.
The scheme above shows the first proposal of the components of which the final program will consist. A more detailed overview will be published in the coming days or week(s). The components in grey are considered to be not very important in order to successfully complete the corridor competition. However, these components are necessary for a successful completion of the final competition.
Maze solving Algorithm's
There are many ways to solve a maze. These ways of finding the exit are translated into algorithm's. An overview of these algorithm's is given here: .
Based on this website and on the results shown in the paper "Simulated Maze Solving Algorithms through Unknown Mazes", presented in this pdf-file: , we probably will tackle the problem of solving the maze by means of the so-called Tremaux's Algorithm. As a backup plan the Wall follower algorithm is chosen.
The Tremaux's Algorithm solves a maze by marking how many time a passage has been passed, when a junction is encountered that is not visited yet a random passage is chosen. If the junction is visited before the passage with the lowest counts of visits is chosen (if there are multiple options a random choice between these options is made). Exception to this is if the passage which we are arriving from is only visited once, then we will go back through the same passage, this is to avoid going around in circles.
An example of a maze solved by the Tremaux's Algorithm is shown below, the green markers indicate a decision not made random but based on the number of visits of all the possible passages.
Wall follower algorithm
The Wall follower algorithm is a very simple algorithm to solve a maze. In the beginning a wall is chosen and this wall is followed, basicly this means start moving through a passage and if a junction is encountered alway turn right (or always turn left) if possible. Disadvantage of this algorithm is that it does not always find a solution, but the great advantage is that it is very simple to implement because it does not require a memory of which passages have been visited.
An example of a maze solved by the Wall follower algorithm is shown below.
The detection of the arrows will be done by detecting the corners of the arrow. First, we did a simple test in Matlab with a photo of an arrow, using the Image Processing toolbox of Matlab. And now we are trying to write a corner detection algorithm for ROS. Probably using the "cornerSubpix" function of the OpenCV library. We will use a simple webcam to test our algorithm. To be able to use a webcam in ROS the following tutorial is followed: .
We will only check for arrows in the neighborhood of a junction, to avoid unnecessary image processing. The arrows will be recognized on the basis of corners
Compensate for distortion (fish-eye effect)
The provided images taken with the camera of Jazz have not a really high quality partially due to the so-called fish-eye effect which causes a distortion. Distorted arrows may be hard to recognize and as the camera properties will most likely not change, we have planned to solve this issue by correcting the distortion by a calibration as is described on this webpage: Information on distortion Correction
Step 1.) Convert image to HSV image
HSV varies the hue component of the hue-saturation-value color model. The colors begin with red, pass through yellow, green, cyan, blue, magenta, and return to red. The advance of using HSV is that you can use a color for different illuminations by using a hue an saturation value.
Step 2.) Convert Red to black and white image
From the HSV image we create a black and white image. In the tables above you can easily see which values to use if you only what to find red arrows. We implemented a track-bar for each parameter to find the ideal parameters settings. This resulted in the following boundaries for each parameter:
(H \leq 8 \vee H \geq 352) \land S \geq 0.3 \land V \geq 0.5
Step 3.) Filter noise
- Erosion generally decreases the sizes of objects and removes small anomalies by subtracting objects with a radius smaller than the structuring element.
- With grayscale images, erosion reduces the brightness (and therefore the size) of bright objects on a dark background by taking the neighborhood minimum when passing the structuring element over the image.
- With binary images, erosion completely removes objects smaller than the structuring element and removes perimeter pixels from larger image objects.
Characteristics of Dilation
- Dilation generally increases the sizes of objects, filling in holes and broken areas, and connecting areas that are separated by spaces smaller than the size of the structuring element.
- With grayscale images, dilation increases the brightness of objects by taking the neighborhood maximum when passing the structuring element over the image.
- With binary images, dilation connects areas that are separated by spaces smaller than the structuring element and adds pixels to the perimeter of each image object.
Step 4.) Find biggest contour
We search in the binary image for closed contours, i.e. for object pixels that are connected with each other, using the FindContours function of OpenCV. For each contours we calculate the area and store the biggest contour. We use a threshold value (a minimum area) the biggest object must have to proceed with the algorithm. Since from a far distance the shapes of an arrow is not clearly visible.
Step 5.) Use only area of biggest contour
With the minimum and maximum coordinates of the object we create a new image where we have deleted the remaining noise in the image.
Step 6.) Determine the arrow and direction with Rotation invariant moments (Hu)
It is possible to calculate moments which are invariant under translation, changes in scale, and also rotation. With these moment we can determine if the contour is a arrow. It is also possible to determine the direction of the arrow.
To help Jazz driving straight through corridors and detecting junctions a line detection algorithm is implemented. The hough transform algorithm is the algorithm we have chosen for this, how this algorithm works and is implemented is explained here.
For every point that is detected by the laserscanner lines are drawn through that point with different angles, the distance perpendicular to the line is then measured. If for a certain angle the distances for two points are the same, these points are on the same line. This is further explained with some pictures.
From this example can be concluded that the points or on a line with angle 45° at a distance of 7. Of course in reality more lines per point are calculated depending on the required accuracy. Also the distance does never exactly match because of measurement noise and the fact that real walls are never perfectly straight. Therefore the distance does not have to be exactly the same but has to be within a certain tolerance.
How this works for the real laserdata is explained with some pictures. First for every point the distance is plotted as a function of the angle. This angle goes from 0 degrees to 180 degrees in steps of the desired accuracy. This range can describe all possible lines like shown in the pictures below.
The implementation of this algorithm is now further explained with a captured frame of the laserdata shown below.
First all the angles and distances for each point from the laserdata are calculated, then a raster is created with all possible lines. Each element of this raster represents a line with a certain angle and a certain distance to Jazz (measured perpendicular on the line). The size of the elements of this raster depends on the tolerances for the distance and angle that have been set. The number of points inside each element is counted. When the number of points is more than a certain threshold this is considered to be a line.
The first plot shows a graphical interpretation of all the lines that are calculated for each point. The second plot shows the raster and its elements, the points within each of these elements are counted. The elements which hold at least a certain amount of points represent a line, these are the peaks shown in the last figure.
Extraction of start and endpoints of the lines
The start and endpoints of a line are examined by appointing all the points from the laserscan to the lines the belong to. After this is done, two neighboring points are compared to see if there mutual distance is smaller than some tolerance in order to make sure that they lay on the same line section. For all the points belonging to one section, the first and last point are considered to be the start and endpoint of that particular section. The obtained information is sent to the navigation node which decides about the actions to take. The messages, defined by a new structure, that are sent by the line recognition node contain the number of lines, and for each line, the number of sections, and per section the coordinates of the begin and endpoint together with the number of points on the section.
Driving through corridor
To drive safeley through a corridor without colliding into walls a corridor is virtually divided into 3 different zones.
This zone is in the middle of the corridor and the desired velocity here will be at maximum allowed velocity, the desired driving direction of Jazz will be straight ahead.
These zones are a bit closer to the walls, therefore the desired velocity here will be half of the maximum allowed velocity, the desired driving direction will be slightly to the middle of the corridor to get into the safe zone again.
These zones are so close to the wall that there is a severe risk of colliding with the walls. The desired velocity here will be set to zero if Jazz' driving direction is still directing to the wall. If the driving direction is directing from the wall the desired velocity will be half of the maximum allowed velocity. The desired driving direction will be pretty sharp to the middle of the corridor to leave the critical zone.
The zones are illustrated in the picture below.
In order to succesfully navigate through the maze, an appropriate junction handler has to be designed. In our opinion, the junction handling described below will do the job: There are basically 5 types of junctions Jazz will encounter during its drive through the maze as shown in the figure below. The red arrow indicates the direction from which Jazz approaches the junction. Note that a dead end is also considered to be and handeled is if it is a junction.
The recognition of the different situations is planned to do in the following manner:
- First try to see if the first and last point appointed to a line at an angle of approximately 90 degrees has its first and last point approximately within the width of the corridor. This means that we have to deal with situation C.
- If this is not the case, check whether the absolute value of the y position of either the first or last point of the line at an angle of approximately 90 degrees is larger than the width of the corridor > situation A or E
- See whether all points are close to each other in y-direction. If this is the case, we are in situation A.
- Otherwise Jazz is in situation E.
- If this is not the case, check whether the absolute value of the y position of the first and last point of the line at an angle of approximately 90 degrees is larger than the width of the corridor > situation B or D
- If all points of the line are close to each other in y-direction, we are in situation B
- Otherwise we are in situation D
Based on the junction type detected, the appropriate action should be taken, such as drive forward, turn left/right, check for an arrow and check each passage for the number of visits.
In order to make the junction recognition robust and also working in case of minor deviations from the ideal situation we have taken some measures. The most important measures are explained below. Note that these are additions to the original proposed method, as this method worked fine in ideal cases but not for a whole maze with a seris of junctions connected to each other.
- Paralell lines which are used by Jazz to recognize the walls between which it drives are limited to a distance of 1 meter, in order to make the right distinction between for example a corner and a dead end in case of coupled junctions.
- Searching for an opening in the wall at 90 degrees angle is only done between the paralell lines, with an offset of 30 cm to both sides. This in order to prevent jazz from detecting an exit shifted to the side as being the current exit.
- Checking for points of the line at 90 degrees to be on the paralell lines is done by taking the in to account at which side of jazz they are located (left or right). This is necessary in case jazz does noet drive through the center of a corridor.
- For the check mentioned above there is also accounted for the orientation of jazz which may otherwise cause errors.
- Decisions are made only after one type of exit is recognized 10 times, to prevent Jazz from taking a wrong decision if a line is once not recognized properly.
- Finally, the line at an angle of 90 degrees is always the nearest line at 90 degrees, which may otherwise also cause trouble in case of connected junctions.
Simulation versus real world
Because we encountered some unexpected problems during the first test on the real robot we have made some modification to the simulator so it becomes a better representation of the real world. Our line detection uses the laserdata as an input and was optimized for the laserscanner of the simulator which returned 180 points. The real laserscanner returns 1080 points, which our program could not handle. To prevent this problem in the future an extra program is written that takes in the 180 points laserdata from the simulator and transforms this to 1080 points laserdata. This is done by lineair interpolation between the points of the simulated laserdata.
The visualisation of the new laserdata is shown in de picture below, the red dots represent the points from the new simulated laserdata, the white dots are those from the old simulated laserdata.
We also made a corridor in the simulator that does not have perfectly straight walls, because in reality this is also not the case. For testing the robustness of our line detection algorithm this is very useful. The difference between the old and new corridor world is shown below.
In this section I will publish all kinds of information on the things I am currently learning, I have learned so far and my further ambitions.