Assembly process identification and prediction based on behavioural feature parameter extraction
针对航空航天人机协作装配场景，提出一种结合行为特征参数提取与帧匹配的装配过程识别与预测方法，构建GCSM网络提升预测精度和速度，实验显示识别准确率超97%。
Human-robot collaboration in industrial assembly demands real-time sensing for seamless actions. However, existing methods for perceiving industrial assembly behaviours, particularly in aerospace applications, still suffer from insufficient prediction accuracy, slow processing speeds, and limited transferability. This study proposes an assembly process recognition and prediction method tailored for aerospace human-robot collaborative assembly scenarios. By integrating behavioural feature parameter extraction techniques with frame-to-frame matching strategies, it effectively mitigates parameter complexity and reduces data bias introduced by human and environmental factors. An assembly behaviour prediction network named GCSM is constructed, incorporating attention mechanisms and skip connections to accelerate network training and prediction speeds while enhancing prediction accuracy. The experiments prove that the neighbour frame matching method can effectively identify the assembly progress with more than 97% accuracy. The prediction accuracy and real-time performance of the GCSM network are significantly improved, and it has a good migration ability, with about 50% improvement in RSE, 4% improvement in Correlation, 10% improvement in prediction speed, and 65% improvement in model training speed compared with the traditional prediction network. This method is applicable for assembly process recognition and prediction in complex human-robot collaborative assembly scenarios within the aerospace industry. See https://github.com/WeiZihan5/Human-Robot-Collaboration-Assembly-Dataset for dataset details.