1. I-SOBOT SOCCER Padmashri Gargesa Intelligent Robotics I I (Winter 2011)

2. Overview  Objective  Project Description  Environment Setup  Color filtering and object detection  Trajectory Planning  Links and References

3. 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

4.  Takara Tomy’s ISOBOT  1.3 Megapixel Gigaware webcam  USB UIRT – IR Transmission Hardware Software  OpenCV  USB UIRT device library

5. 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.

6. 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. ObjectColor Field borderGreen BallPeach GoalWhite

7. 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.

8. 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

9. 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.

10. 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.

11. 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

12. Link and References  http://www.youtube.com/watch?v=SUIOWowloTk http://www.youtube.com/watch?v=SUIOWowloTk  http://opencv.willowgarage.com/wiki/ http://opencv.willowgarage.com/wiki/  http://www.usbuirt.com/ http://www.usbuirt.com/  http://www.academypublisher.com/proc/wisa09/papers/wis a09p267.pdf http://www.academypublisher.com/proc/wisa09/papers/wis a09p267.pdf