Cyber-physical systems in non-rigid assemblies: A methodology for the calibration of deformable object reconstruction models

Abstract

Despite the advances in robot agent cognition and systems’ decision-making, under the prism of cyber-physical systems and industrial metaverse, the manufacturing processes involving the handling of non-rigid product assemblies present a delay in the adoption of smart automation. Model-based planning and control can address the particularities of deformable object manipulation; however, their competence is heavily dependent on the models’ accuracy and reconstruction frequencies. Despite the many breakthroughs that have been achieved in real-time modelling and behavior prediction of deformable objects, the calibration of such models and the measurement of their accuracy remain a significant challenge. In this paper, a method for the definition of the physics parameters of flexible material reconstruction models is presented. The proposed systematic approach, employing a number of optimization algorithms, fine-tunes the model’s parameters for the real-world deformable object, as captured by the perception system, to be aligned with its digital twin. A mass-spring model for the reconstruction of two-dimensional fabric objects is used as an application paradigm. An experimental setup in an industrially relevant environment validates the applicability of the proposed approach and is used for assessing alternative sensing practices and optimization algorithms.