From Offline Programming to Cyber-Physical Systems: an Optimized Software Architecture

The increasing role of industrial robots in modern manufacturing is crucial to improving competitiveness. However, small- to medium-sized enterprises (SMEs) still face challenges in adopting robotic technologies, primarily due to the complexity and time-consuming nature of robot programming. Traditional programming methods rely on either teach pendant programming, which causes machine downtime, or offline programming (OLP), which reduces downtime but requires extensive calibration and validation. Despite its advantages, OLP remains a challenging approach due to the difficulty of accurately replicating real-world conditions in a virtual environment. A key challenge is ensuring proper calibration, as even minor discrepancies between the simulation and the actual robotic system can lead to positioning errors, inefficiencies, and operational failures.This paper presents SmartOffline NextGen (SmOffNG), an advanced OLP solution developed by SACMI, designed to enhance the efficiency of the calibration and the performance of the system. The proposed innovation introduces a dynamic recompilation functionality that minimizes computational overhead while maintaining high accuracy. The method leverages a precomputed lattice of offset configurations to predict optimal recalibrations without requiring full reprocessing. By applying a spatial locality principle, the system efficiently selects the closest precomputed configuration, significantly reducing recalibration times while preserving precision.The main contributions of this work include:• An optimized architecture for program rebuilding.• Virtualization of the rebuilding lattice to enhance flexibility and efficiency.• Performance optimization of the cyber-physical system in terms of cycle time and duty cycle.The proposed approach enables faster and more reliable robot calibration, making OLP more accessible and effective for industrial applications. Experimental validation demonstrates the feasibility and benefits of the solution, highlighting significant improvements in computational efficiency and robotic system performance.