A Multi-Objective Framework for Human–Robot Collaborative Assembly with Augmented Reality Visualization

Abstract The increasing complexity of customized manufacturing in Industry 5.0 has amplified the need for human-robot collaboration (HRC) to enhance assembly systems’ flexibility, adaptability, and real-time responsiveness. This study proposes an integrated optimization framework comprising four key modules: Optimal Assembly Sequence Planning (OASP), Optimal Resource Allocation (ORA), Optimal Layout Planning (OLP), and immersive lay out validation using augmented reality (AR). Assembly sequences are developed via a part concatenation strategy, while resource-task assignments are formulated as a multi-criteria optimization problem and solved using the Nelder–Mead simplex algorithm. Layout plan ning employs linear programming under separation constraints to prevent spatial overlap. AR-based visualization enables real-time layout validation and operator interactions. The framework wastested on two industrial case studies—a vibration generator and a transmis sion assembly—demonstrating over 70% reduction in layout generation time compared with modified particle swarm optimization (MPSO) and improvements in space utilization and task sequencing. These results establish the framework as a scalable, digital twin-ready decision support tool for designing adaptive HRC systems in smart manufacturing.