Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

Abstract

This paper presents the design and construction of a robotic arm that plays chess against a human opponent, based on an artificial vision system. The mechanical design was an adaptation of the robotic arm proposed by the rapid prototyping laboratory FabLab RUC (Fabrication Laboratory of the University of Roskilde). Using the software Solidworks, a gripper with 4 joints was designed. An artificial vision system was developed for detecting the corners of the squares on a chessboard and performing image segmentation. Then, an image recognition model was trained using convolutional neural networks to detect the movements of pieces on the board. An image-based visual servoing system was designed using the Kanade–Lucas–Tomasi method, in order to locate the manipulator. Additionally, an Arduino development board was programmed to control and receive information from the robotic arm using Gcode commands. Results show that with the Stockfish chess game engine, the system is able to make game decisions and manipulate the pieces on the board. In this way, it was possible to implement a didactic robotic arm as a relevant application in data processing and decision-making for programmable automatons.

Playing Checkers with an Intelligent and Collaborative Robotic System

“…The board is usually less problematic to be recognized since computer vision libraries, like the opensource OpenCV, provide many tested and robust algorithms based on model recognition suitable to fulfill this purpose. Examples are Harris corner detection [11,12], Canny edge detection, and Hough Line Transform [13,14]. These algorithms are suitable when the relative position between the robot and board can change since they find the board position fairly accurately and quickly.…”

“…Moreover, the development of such algorithms is not a simple task, especially if the aim is to develop an efficient and competitive system. To easily solve this problem, some authors preferred to implement an open-source game engine like Stockfish [14,21] for chess and Raven [16] for checkers. Instead, other researchers tried to develop a game engine by themselves.…”

“…In this manner, the gripper can correctly reach and grasp the pieces. As robotic agents, robotic arms are mainly used [12,14,18] thanks to their dexterity and manipulation capability. Cartesian mechanisms are also used [19,26] due to their easy control, but their structure is often large and bulky.…”