Project Abstract

This dissertation project delves into the development of a machine learning-based system capable of real-timerecognition of chess pieces on physical boards. The project not only streamlines the process of game analysisby automating move recording but also introduces the exciting prospect of enabling players to engage inmatches against a computer opponent on a tangible chessboard. This opens up new possibilities for a moreimmersive and dynamic chess-playing experience. Furthermore, the project integrates a robotic SCARA arm,which physically executes moves dictated by an AI, thus merging the analytical depth of computer chess withthe tactile satisfaction of moving real pieces. This innovative approach not only augments the chess-playingexperience, making it more engaging and interactive but also paves the way for educational tools andinteractive exhibits that could further democratize the learning of chess strategies in real-world settings.

Keywords: Chess, Object detection, Robotic

 Conference Details

Session: Poster Session B at Poster Stand 2

Location: Sir Stanley Clarke Auditorium at Wednesday 8th 09:00 – 12:30

Markers: Ulrich Berger, Oliver Kullmann

Course: MEng Computing, Masters 4th Year

Future Plans: I’m looking for work