Automatic checkers

We've made a final version of our project.

The film show it! Enjoy!

Gregory developed, wrote and implemented the software not only for computer but also for robot, which was designed and was built by Ewelina. Tests of the algorithm of checkers and the algorithm for robot were carried out together.

Work on the project proceeded during regular meetings. Meetings were held at least once a week. During the first meeting we established work plan, which we stuck to the end of the project. Some of the work we did independently between meetings. During the meetings we are doing a summary of the work and partial testing.

Gregg here.

I've developed the software for out project.

BTW. All the software is on my GitHub and it's Open Source. Maybe it'll help someone! (I hope so :) ).

I've used python with openCV and PyQT library to design main application, which consists of image recognition, gameplay and GUI:

For the hardware we've used LabVIEW (with NXT toolkit). 

Python and LabVIEW communicates via TCP.

The basic dataflow:

Rukia built out LEGO robot arm to move the pawns on the board.

It was developed using LEGO NXT's, train tracks and pneumatics.

Main skeleton:

Motors attached to ropes move the board and carriages along the axes. Both have endstops to detect zero position.

Axes was built using train tracks to assume straight moves along axes.

The carriage with mounted pneumatic actuactors:

Two of them pulls up the pawn above other and one is to hold it.

We built automatic pump with pressure switch to prevent over pressure in the installation:

Pr0 tip: we've used modeller's fuel (from airplanes models) plastic pipes and airtank (it was for fuel too!). They're much more stronger and much cheaper than lego pipes.

We have developed computer's brain!

We know, the checkers are solved game (Link), but we tried to do out best in time we have.

We've designed alfa-beta pruning (Link) for checkers - it creates game tree of every position under actual.

Our board to value function (we have to know which situation is better than other, but it's really heuristic):

- the closer the pawn to the end the bigger the value,

- if the pawn is a kings is much more valuable,

- if the pawn is under beat it is less valuable,

Analogically for the computer pawns.

We can set searching depth from 5 to 8. The algorithm is so good that we have never beaten it.... The slave becomes better than master...

Hello, hello!

To detect pawns we decided to use android camera (with IP Webcam app - it's very good by the way). Phone was mounted over the board to have clear view of situation on it. As light source we have used built-in LED.

The board with mounted camera:

First of all, we have made a board recognition algorithm, using openCV and python (it is really faster to write code in python than in C++, especially in openCV where types names are so "strange"...).

Rukia designed the algorithm and Gregg implemented it:

- crop and transform image to square board of constant size (600x600 px),

- splits it into separate fields,

- for every field:

a) applies median blur,

b) applies Gaussian blur,

c) applies Canny edge-detector,

d) search for circle using Hough method,

e) if the circle was detected it threshold the image and then recognize color of the pawn (it depends of white pixels in threshold image)

Then build an array of field to pass it forward to game logic.

Board after transformation with marked detected pawns:

And the same board after edge detector:

Whole algorithm written in python is on GitHub

All project files are stored at GitHub.