AI-enabled digital twin framework for reconfigurable robotic palletizing of irregularly shaped products

Palletizing in intralogistics and manufacturing remains particularly challenging when dealing with irregularly shaped products, large variant sets, and frequently changing customer orders. This paper presents an AI-enabled digital twin framework for reconfigurable robotic palletizing of irregularly shaped products. At its core, the framework uses a digital twin that maintains a unified representation of the robotic cell, its resources, and the handled products, and exposes consistent information to all software modules, enabling their interoperation. The palletizing pipeline integrates key enabling technologies to answer how products should be handled, where they are located in the workspace, and how they should be stacked at the output station. First, geometry-based preprocessing and an analytical grasping procedure extract key attributes and grasp points for each product type and store them in a knowledge base. Second, a perception module combines deep learning–based instance segmentation with template matching to identify and localize aluminium profiles that arrive in mixed, colour-oriented stacks without explicit identifiers. Third, a pallet loading optimizer employs a deep reinforcement learning–assisted selector, a constraint satisfaction stage, and a multi-criteria placement evaluation to generate near-optimal stacking configurations that satisfy stability and handling constraints. The digital twin mediates the exchange of this information across the participating enabling technologies, serving as a single source of truth and dispatching the solution execution components responsible for materializing the generated palletizing plan. The framework is deployed and validated in an industrial aluminium extrusion packaging use case using a dual-arm robotic palletizing cell, illustrating a viable path toward reconfigurable automation of irregular product palletizing in intralogistics and manufacturing environments.