Robotic Systems for Material Handling: Design Framework and Digital Twins

The integration of robotic systems in material handling processes has become pivotal in modern manufacturing. This chapter presents a comprehensive framework for the design and deployment of such systems, underpinned by the concept of digital twins. The research focuses on the development of a robust digital twin system that includes a robots simulator couple with an optimization model for Manufacturer's Pallet Loading (MPL). The digital twin serves as a virtual replica of the physical material handling system, facilitating real-time monitoring, predictive analytics, and simulation-based decision-making. In the context of this study, the emphasis lies in the architecture and components of the digital twin, highlighting its role in enhancing the efficiency and adaptability of material handling operations. This simulation-based approach assesses risks associated with physical experimentation and accelerates the design and optimization of robotic automated systems. Furthermore, a systematic methodology is presented for the implementation of robotic systems in material handling. The methodology encompasses the entire lifecycle, from initial design and simulation to practical deployment. It underscores the importance of data-driven decision-making, collaborative human–robot workflows, and continuous improvement through iterative updates of the digital twin. Real-world case studies from diverse industrial settings are utilized to validate the effectiveness and adaptability of the data-driven approach. The implementation of the introduced method has led to utilization coefficients surpassing 90%, even when dealing with non-standard secondary packaging configurations. The outcomes of this research contribute significantly to the advancement of material handling systems. By merging digital twin technology, robot simulation, and optimization models, manufacturers are provided with a powerful toolkit for improving operational efficiency, reducing costs, and responding flexibly to dynamic production demands. In a rapidly evolving industrial landscape characterized by automation and digitalization, this work offers a roadmap for the seamless integration of robotic systems into material handling processes.