i-SOBOT SOCCER

I-SOBOT SOCCERPadmashri GargesaIntelligent Robotics I I (Winter 2011)

Overview Objective Project Description

Environment Setup Color filtering and object detection Trajectory Planning

Links and References

Objective Install an overhead camera and calibrate

soccer field position and orientation. Determine soccer field co-ordinates through

camera vision. Determine Goal, Ball and Bot position through

overhead camera vision. Determine feasible shot region. Plan trajectory. Issue IR commands to the ISOBOT

programmatically in order to traverse the planned trajectory towards the ball

Takara Tomy’s ISOBOT 1.3 Megapixel Gigaware webcam USB UIRT – IR Transmission

Hardware

Software OpenCV USB UIRT device library

Enviornment setup Overhead webcam setup (mounted

on the ceiling) overlooking the entire field.

Soccer field 53’’ X 45’’ in dimension 66’’ vertical distance from the overhead

webcam. Black background to make other objects more

conspicuous Green color border to determine field dimension

and co-ordinates through camera vision.

Color filtering and Object detection

Objects were selected across a wide range of colors to set them apart on the color scale.

Bot detection with a green-red tiled pattern on the Bot’s head.

Object ColorField border GreenBall PeachGoal White

Field Co-ordinates Input Image is background with green border with no objects Conversion to HSV and thresholding with below values

50 < H < 180 170 < S <256 50 < V < 180

Hough lines to detect field coordinates. Rough ROI got from above set on input image and processing

is continued. Conversion to grayscale OpenCV “Contour detection” and “bounding boxes” approach

to get precise co-ordinates and field dimensions. Once field coordinates are set, border is removed. Considered using affine transformations through rotation and

warp matrices.

Object detection Ball Co-ordinates

Conversion to HSV and thresholding with below values.

6 < H < 35 35 < S <256 110 < V < 256

OpenCV “Contour detection” and “bounding boxes” used to get ball dimensions and coordinates.

Goal Co-ordinates Conversion to Grayscale OpenCV “Contour detection” and “bounding boxes”

used to get goal dimensions and coordinates

Object detection Bot Co-ordinates

To detect rear red tile Conversion to HSV and 2 levels of thresholding

Level I 0 < H < 6 84< S <256 84 < V < 256

Level II 170 < H < 200 84 < S <256 84< V < 256

The resulting 2 images are added. To detect front green tile

Conversion to HSV and thresholding with below values. 6 0< H < 100 84< S <256 84 < V < 256

OpenCV “Contour detection” and “bounding boxes” used to get bot location and orientation.

Trajectory Planning

Actual image output from the program is as shown above. Bot location and orientation is shown by the blobs on the far

left. Line connecting goal to ball is ideal strike line. Triangular region behind the ball is the feasible shot region.

Trajectory Planning

Trajectory planned is similar to a cosine curve. The curve like path is essential to make up for the inability

to control bot servos and move bot along a desired angle to a desired distance and for having to rely on the pre-programmed ISOBOT commands for BOT motion.

Goal Coordinates

Ball Coordinates

Shot Region

Bot Coordinates

Trajectory

Link and References http://www.youtube.com/watch?v=SUIOWowloTk http://opencv.willowgarage.com/wiki/ http://www.usbuirt.com/ http://www.academypublisher.com/proc/wisa09/p

apers/wisa09p267.pdf