Multi-Criteria Path Optimization for a Hybrid Assembly Process

Manufacturing increasingly demands flexible assembly processes that balance automation with human expertise. This paper addresses the challenge of optimizing pipe assembly layouts in a hybrid assembly scenario from aircraft manufacturing through a novel formulation as a multi-criteria shortest path problem. We introduce a process assistant system that integrates cognitive assistance through projection-based augmented reality with physical robotic support, maintaining worker decision-making autonomy throughout the assembly process. The underlying process optimizer, the core contribution of this work, uses a modified A∗algorithm (MCA∗) that simultaneously optimizes path length, material costs, and workspace ergonomics while satisfying physical and operational constraints inherent to our use case. Experimental validation across varying obstacle densities demonstrates that MCA∗achieves superior balance across optimization criteria compared to traditional path-finding approaches while suitable for real-time assembly planning applications.