Keywords
Artificial intelligence
Computer vision
Programmable logic controller
Robotics
How to Cite
Abstract
Objective: to develop and verify an integration architecture combining artificial intelligence, computer vision, a programmable logic controller, and a robotic manipulator to recognize a chessboard state, select a move, and coordinate its physical execution. Method: an applied technological development study was conducted in 2026 and organized into five functional layers: perception by an industrial vision sensor, integration through a Python application, decision-making by the Stockfish engine, coordination and safety by a Siemens S7-1200 programmable logic controller, and actuation by an embedded controller and a SCARA manipulator. Verification focused on software logic, communication protocols, fault handling, and integrated simulation. Results: all 72 automated tests of the integration application passed, as did the 25- and 61-test sets for the cell program and bench software; the motion core was also verified by 37 tests. Communication timeout control reduced a blocked wait from 5.0 s to less than 1.5 s. In integrated simulation, engine lockup was handled in 4.3 s and a stalled main loop was detected in 4.0 s, while the game state was preserved after restart. Conclusion: the results support the functional consistency and recovery capability of the architecture in software and simulation while keeping the industrial controller responsible for coordination and safety. Physical evaluation of vision, positioning, response time, and complete games is still required to characterize overall cell performance.
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