Self-localization for Humanoid Soccer Robots Based ...

Indonesian Symposium on Robot Soccer Competition 2013 – June 6th , 2013

ISBN : 979-26-0264-X

Self-localization for Humanoid Soccer Robots Based on Triangulation Method Muhamad Andung Muntaha, Muhtadin, and Mauridhi Hery Purnomo 

Abstract -- In this paper a self-localization method is presented.

The self-localization process is done by using robot visual perception obtained from a single camera. The goal of this method is to obtain the pose(x; y; _) of the humanoid robot inside the field of play. Image segmentation algorithm is also presented as preprocessing step to identify the landmarks. Then the distances estimation process is done by observing landmarks diameter, and compared to the real distances. By knowing this information, the relative position can be estimated by means of triangulation method. Finally the result obtained in Webots is presented. Index Terms—color segmentation, features extraction, distances estimation, self-localization, triangulation.

T

I.

INTRODUCTION

HERE is an annual robot soccer competition named RoboCup which held since 1997 and its purpose is to accommodates research on artificial intelligent and robotics, by offering a publicly appealing. At first wheeled mobile robots were used, but soon a four-legged came into play. Currently, humanoid robots is used. The ultimate goal of this competition is by the year 2050, a team of humanoid robots can defeats a FIFA’s world champion. RoboCup has three categories based on robot size, Kid Size, Teen Size and Adult Size. Our team has participated three times in annual local RoboCup Kid Sizelike competition which held since 2011 [1]. Sensors that allowed to be used are strictly limited. Only sensors that represent parts of the human body are allowed. The use of sensor that emits an electromagnetic wave such as ultrasonic is prohibited. The most common configuration of a robot is a head-mounted web camera, a gyroscope and a accelerometer as the main sensors. The allowed configuration of the vision system is using stereo or mono camera with limited degree of movement like in humans. Our robot is using mono camera. The visual perception of the robot playing a main role in implementing the strategy. The basic ability every single robot must has is self-localization inside field of play. With this information it is hoped the football team’s game strategy can be applied to robots. Ibarra et al. (2011), proposed a similar method to obtain pose estimation of the robot. They proposed a method to estimate the distance by using 3d monocular reconstruction. But we use different approach by observing landmarks diameter. We also proposed the algorithm to choose the best solution from some possible solution.

A. Problem statement Muhamad Andung Muntaha is an undergraduate student at Institut Teknologi Sepuluh Nopember. He develops his thesis about localization of humanoid robot in RoboCup environment at the Digital Informatics Laboratory of the Department of Electrical Engineering. His thesis is directed by Mauridhi Hery, a professor in that department and a senior lecturer, Muhtadin.

The main goal of our vision system is to extract edges features of beacons and goal posts. From these information we can calculate the diameter of the cilynder in image in order to estimate the distances between robot and landmarks. For the localization problem, we need to obtain position estimation of the robot inside field of play. This information can be represented by three parameters (x; y; ө). All of theseproblems are solved just by using information taken from the image. B. Proposed methodology As we know that all the landmarks(beacons and goal posts) are cylinder with the same diameter, we developed a method to estimate the distances between robot and landmarks by examining diameter changes of the landmarks effected by real distances. In order to solve the localization problem, we proposed a methodology by intersecting two circles based on the information we have, an estimated distances and global information of the coordinate of the landmarks inside field of play. All the experiments is done in simulation using Webots˝orobot simulator. In order to simulate our real robot, we use the darwin-op platform. Although in the simulation, the development is very similar to real world application. We can simulate all the functionality of the DARwIn-OP˝o and we can build the environment as similar as possible with real RoboCup field of play. Moreover, this simulation software offers a crosscompilation feature so we can compile our simulation code directly into the robot. C. Paper organization After this brief introduction, section II describes about landmarks identification and features extraction. This section including some image processing method such as segmentation, line detection and analysis of segmented image. Section III explain the main methodology about distances estimaton and self-localization using triangulation. In section IV the implementation are described and some result are shown. Finally section V shows our conclusions. II. LANDMARKS IDENTIFICATION AND FEATURES EXTRACTION The landmarks we identify are the beacons and the goal posts. The first step we need to do is color segmentation process. The result of this process is an binary image that we need to analyze in order to identifying the beacons. For goal detection and identification, we need further processing that are edge detection and line detection.

73 | Dian Nuswantoro University – Semarang, Indonesia

Indonesian Symposium on Robot Soccer Competition 2013 – June 6th , 2013

ISBN : 979-26-0264-X

The image captured from the webcam is in RGB color space. Illumination change affects each channel of this color space. To make the color segmentation more robust, we need to convert the image into HSV color space. Because in HSV color space, little change in illumination just affects one color channel.

(a)

(b)

(c)

(we choose the center band). But this method is not accurate enough if the robot sees the beacon in tilted camera position. So we proposed a method that not affected by how the beacon is seen in image by doing a rectangle detection on the segmented image of the center band. The edges represented by two points of the rectangle that are on a line along x-axis of the image coordinate. The diameter is obtained by calculating the distances between these two points. B. Goal We need to find a representation of goal posts and crossbar in order to find the important points such as top corner and the base of the goal posts. Due to goal posts and crossbar structure, the best representation is in a form of a line equation. In order to do that, further image processing algorithm including edge detection and line detection are needed. The line detection algorithm we use is based on hough line transform. The result of this algorithm are bunch of lines with parameter (_; _) that represent minimum distances of a line and its direction from image origin. These lines need to be averaged and classified into vertical and horizontal line that represent goal posts and crossbar. Equation 1 states the line parameter conditional in order to classify it into vertical or horizontal line [3].

Figure 1. Ideal segmentation: (a) input image (b) yellow segmentation result (c) blue segmentation result

The result of segmentation in HSV color space shown in figure 1 are nearly perfect, but in real world application furthervprocessing may be needed to enhance the segmentation result. The common problems that frequently occur in segmentation process are the existence of holes inside regions of color and incomplete edges. Ibarra et al. (2011), proposed morphological filtering to solve the problem [2]. A. Beacon The structure of the beacon is a sequence of three bands consists of two colors. At first we need to do a segmentation of yellow and blue color. At this moment we have two binary images that are the results of the segmentation process. The beacons can be identified by counting yellow blob and blue blob. The existence of two blue blobs and one yellow blob can lead us to conclude that it is an blue-yellow-blue beacon. Then to verify whether it is an beacon is by checking the allignment of each segmented image by dividing its centroid by each other. If the result is close to 1, it can be concluded that these points are on a line. We need to find a point that represent each edge of the beacon so we can calculate the diameter of the cylinder. By knowing the result of the segmented image as shown in figure 1, we can easily find two points that represent the edge by finding the bounding box of one segmented image

The top corner can be obtained by intersecting the horizontal and the vertical line. The base of the posts, on the other hand, can be found by traversing from the top corner downwards the vertical line until white pixel stop appearing in the segmented image. From these two points we can find the center of the goal post and the edges can be found by traversing from this centroid leftwards and rightwards the virtual line that parallel with crossbar (horizontal line). Figure 2 shows us the result of the method proposed. Two red points represent the edges of the goal post.

Figure 2. Goal detection based on hough transform

74 | Dian Nuswantoro University – Semarang, Indonesia

Indonesian Symposium on Robot Soccer Competition 2013 – June 6th , 2013

ISBN : 979-26-0264-X

III. DISTANCES ESTIMATION AND SELFLOCALIZATION Firstly we collect some comparison data between landmark diameter in image and the real distances from experiments. With these data, we plot the graph and by using polynomial curve fitting method we obtained constant values that will be use in running robot to estimate the distances [4]. Increase in degree of the polynomial affects the error produced. By doing some experiment before, we later decide to use third degree polynomial in order to minimize the error and produce result as accurate as possible.

Figure 4. Best solution chosen by means of triangulation

IV. I MPLEMENTATION AND R ESULT A.

Self-localization by triangulation

All the tests were performed in Webots using opencv as image processing library. Table 1 shows the localization result of 20 different positions inside the field of play. Figure 5 (a) shows the position of the robot during the field observations. Figure 5 (b) shows the results of localization by triangulation method. The most accurate localization results occur when the robot is in the middle of the field, because the robot can detect more than one landmarks. The average localization error in value of x = 0.593m, y = 0.273m and θ = 0.0005 rad. Table 1. Localization Results

Figure 3. Two circles intersection [2]

Once the distances between robot and at least two landmarks (a beacon (xb; yb) and a goal post (xp; yp)) are known, i.e. dband dp, it is possible to estimate the robot position by finding intersection point (xc; yp) between two circles with parameter the distances as radius and landmarks position as center point which previously already known [2]. (xc − xb )2 + (yc − yb )2 = db2 (xc − xp )2 + (yc − yp )2

= dp2

(2)

This system has two solutions (intersection points). The chosen solution is the one that closer to the center of the field of play, assuming the robot is inside the field of play. But if the robot detects more than two landmarks, the possible solutions are also increased. The best solution is chosen from the centroid of a triangle formed by three closest points.

P 1

Robot Position X 2.95

Results

Y

ө

X

Y

ө

3.55

2.60

2.61

3.43

2.67

2

2.37

3.78

-3.24

2.05

3.66

-3.10

3

1.41

2.92

-1.16

0.99

2.86

-0.93

4

1.80

2.08

-0.75

1.47

2.04

-0.72

5

1.36

2.16

-3.25

1.94

1.94

-2.47

6

2.31

2.09

-0.36

3.19

1.70

-0.28

7

2.87

2.15

0.37

3.42

2.38

0.31

8

3.36

2.43

0.42

3.75

2.61

0.44

9

36.8

2.39

0.19

4.32

2.30

0.23

10

4.30

2.04

-1.14

3.65

2.30

-1.12

11

4.97

1.57

-0.47

5.22

1.75

-0.43

12

5.61

1.54

0.39

5.74

1.88

0.26

13

6.22

2.63

1.17

4.47

1.25

1.07

14

5.78

2.77

-3.25

4.62

3.56

-3.09

15

5.39

3.09

1.82

4.28

2.95

2.02

16

5.21

3.64

2.53

4.31

3.73

2.54

17

4.81

3.83

2.90

4.04

3.75

2.88

18

3.98

3.92

-2.92

3.71

3.34

-2.96

19

3.16

4.02

2.82

2.93

3.98

2.77

20

3.87

3.42

-0.95

3.53

3.55

-0.94

75 | Dian Nuswantoro University – Semarang, Indonesia

Indonesian Symposium on Robot Soccer Competition 2013 – June 6th , 2013

ISBN : 979-26-0264-X

BIOGRAPHY Muhamad Andung Muntaha is an undergraduate student at department of electrical engineering, institute teknologi sepuluh nopember. He was active in college robotic team and participated in a local robocup-like competition two years consecutively starting from 2011. He also works as an asisstant in digital informatic laboratory. He is very interested in robotics and computer vision. Currently, he is working on his thesis about localization of humanoid robot in robocup environment based on triangulation and particle filter methods.

Figure 5. (a) Robot real position (b) Localization results

V. CONCLUSIONS The described algorithm for color segmentation is not that simple. In real world condition it is necessary to do morphological filtering and further image processing algorithm in order to enhance the segmentation result. From some experiments it can be conclude that the proposed method for localization is quite accurate with the average error below the 4 tolerated value. But the method has a drawback that the result are not continue. When the robot has moved, robot needs to re-scan the field in order to estimate the new position. REFERENCES [1] “Robocup soccer,” 5 2013. [Online]. Available: http://www.robocup.org/robocup-soccer/ [2] J. Zannatha, R. Cisneros Limon, A. Gomez Sanchez, E. Hernandez Castillo, L. Medina, and F. Lara Leyva, “Monocular visual selflocalizationfor humanoid soccer robots,” in Electrical Communications and Computers (CONIELECOMP), 2011 21st International Conference on, 2011, pp. 100–107. [3] A. Mercader Pallarés, D. Puig Valls, and T. González Sánchez, “Goal detection for soccer-playing robots based on hough transform,” Departament dEnginyeria Informàtica i Matemàtiques, Universitat Rovira I Virgili,Tech. Rep., 6 2009. [4] W. Gurley, “Curves fitting techniques,” University of Florida, Tech. Rep.,2002.

76 | Dian Nuswantoro University – Semarang, Indonesia