Human intention prediction is vital for the efficiency of human-robot collaboration (HRC) and is usually modeled based on data-driven methods. However, due to the complexity and diverse nature of HRC, data collection for human intention prediction suffers from low sampling efficiency which restricts the application of HRC in manufacturing. Different from traditional real world data collection, a digital modeling method for HRC is proposed in this paper to generate virtual HRC data. The dynamic musculoskeletal model of human is adopted to simulate the musculoskeletal dynamics of human. The metabolic energy consumption of human is computed and used as an indicator to evaluate the reality of the generated virtual data. The virtual data are used to train human intention prediction model and compared with experimental data. Experimental results show the reality of virtual data and its effectiveness for human intention modeling in human-robot collaborative assembly. The proposed method has potential for reducing the cost of data collection compared with purely experiments.

人类意图预测对于人机协作（HRC）的效率至关重要，通常基于数据驱动的方法进行建模。然而，由于HRC的复杂性和多样性，用于人类意图预测的数据采集采样效率较低，限制了HRC在制造业中的应用。与传统的现实世界数据采集不同，本文提出了一种HRC数字建模方法来生成虚拟HRC数据。采用人体动态肌肉骨骼模型来模拟人体肌肉骨骼动力学。计算人体的代谢能量消耗并将其用作评估生成的虚拟数据的真实性的指标。虚拟数据用于训练人类意图预测模型并与实验数据进行比较。实验结果表明了虚拟数据的真实性及其在人机协作装配中人类意图建模的有效性。与纯粹的实验相比，所提出的方法有可能降低数据收集的成本。