Robust Computer Vision Chess Analysis and Interaction with a Humanoid Robot

Andrew Chen,Kevin Wang

doi:10.3390/computers8010014

Abstract

As we move towards improving the skill of computers to play games like chess against humans, the ability to accurately perceive real-world game boards and game states remains a challenge in many cases, hindering the development of game-playing robots. In this paper, we present a computer vision algorithm developed as part of a chess robot project that detects the chess board, squares, and piece positions in relatively unconstrained environments. Dynamically responding to lighting changes in the environment, accounting for perspective distortion, and using accurate detection methodologies results in a simple but robust algorithm that succeeds 100% of the time in standard environments, and 80% of the time in extreme environments with external lighting. The key contributions of this paper are a dynamic approach to the Hough line transform, and a hybrid edge and morphology-based approach for object/occupancy detection, that enable the development of a robot chess player that relies solely on the camera for sensory input.

Highlights

Board games provide a popular pathway for using computer vision (CV) to support human-robot interaction research
A key goal was to play without controlled lighting conditions, without fixed board positions, and without needing to modify the chess board or pieces, which are shortcomings faced by other chess robot systems
As part of a broader chess robot system, a robust computer vision algorithm has been developed for analyzing chess boards and their current game states with fewer restrictions than other robot chess approaches

Summary

Introduction

Board games provide a popular pathway for using computer vision (CV) to support human-robot interaction research. Chess is often used by researchers because of its reasonably high state-space complexity; in 1950, Claude Shannon [1] first estimated the game-tree complexity of chess to be 10120 ; more than the number of atoms in the observable universe. This complexity makes “solving” chess a non-trivial problem that is challenging for both humans and computers. Creating a robot that plays chess against humans requires three main subsystems to be developed: perception of the chess board and the current piece configuration, computation of the game move, and actuation of the robot arm to manipulate the pieces on the board.

Methods

Results

Conclusion