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.