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i-SOBOT SOCCER

i-SOBOT SOCCER. Padmashri Gargesa Intelligent Robotics I I (Winter 2011). Overview. Objective Project Description Environment Setup Color filtering and object detection Trajectory Planning Links and References. Objective.

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i-SOBOT SOCCER

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

  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.

  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. Bot Coordinates Trajectory Shot Region Ball Coordinates Goal Coordinates

  12. 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/papers/wisa09p267.pdf

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