Robotic Assembly Sequence Planning

Robotic assembly sequence planning aims to automate the process of determining the optimal order of operations for robots to assemble products, minimizing time and maximizing efficiency. Current research heavily utilizes large language models (LLMs) and graph neural networks to generate assembly sequences, often represented as behavior trees, and increasingly incorporates multimodal data for improved perception and human-robot collaboration. This field is crucial for advancing autonomous manufacturing and improving the flexibility and adaptability of robotic systems in various applications, with recent work focusing on improving the feasibility and scalability of generated plans for complex assemblies.