关键词: 工业5.0, 智能制造, 人本智造, 主动式人机协作

Abstract: Industry 5.0 emphasizes human centricity,flexible manufacturing and sustainability,fostering the advancement of personalized industrial products.The unstructured and dynamically changing production environments of such products demand highly intelligent Human-Robot Collaboration (HRC).Although existing HRC frameworks driven by the “spatial perception-process analysis-decision reasoning-task execution” paradigm had made significant progress,they remain constrained by limited interaction semantic understanding and insufficient process knowledge,making it difficult to adapt to complex and variable manufacturing conditions.To address these challenges,a process knowledge and interaction semantics-enhanced proactive human-robot collaboration method was proposed.Specifically,a knowledge-augmented adaptive fine-tuning approach was developed to enhance spatial perception and improve large models'understanding and transferability of manufacturing knowledge.A diverse operator intention prediction model was constructed to strengthen process analysis and decision reasoning by deepening human-robot interaction understanding.Furthermore,a deep reinforcement learning-based approach was designed to improve task execution through continual learning and task planning.In a human-robot collaborative assembly case,the proposed method enabled robots to successfully complete tasks by interpreting operator instructions and perceiving the assembly space,demonstrating its feasibility and effectiveness.

Key words: Industry 5.0, smart manufacturing, human-centric smart manufacturing, proactive human-robot collaboration