针对大梁焊接时难以通过传统的识别方法实现实时而精确的障碍物识别的问题，提出一种优化模糊C均值实时聚类(RNGK-FCM)的大梁焊接障碍物识别方法. 引入实时聚类策略，替换核化距离函数，全局快速优化. 通过MATLAB平台进行仿真对比分析各类FCM算法聚类性能，RNGK-FCM相比于传统FCM算法，能实时获取聚类数；对噪声点具有较好的鲁棒性；降低了对初值的敏感性，聚类识别精度高. 在某公司大梁自动焊生产线进行障碍物识别试验. 结果表明，各类障碍物聚类数准确，实时性优良，障碍物规避动作精准，为实现大梁自动焊打下了坚实的基础.
To solve the problem that conventional obstacle identification methods cannot be adopted for real-time and precise obstacle identification during girder welding, obstacle detection of complicated seam track based on RNGK-FCM was put forward. Real-time clustering strategy was introduced, distance function was replaced, and consequently global fast optimization was realized. Through the comparison and analysis of the clustering performances of FCM algorithm on MATLAB platform, it was proved that RNGK-FCM obstacle detection method achieved the real-time clustering strategy to gain large number of clusters, and had better robustness to ignition noise. The sensitiveness to initial value was reduced and the high accuracy of clusters identification was boasted compared to traditional FCM. A real-time obstacles recognition experiment was carried out in the automatic grinder welding production line of a certain company and it was accurate in clusters calculation, good in real time and precise in obstacles avoidance, providing a strong foundation for automatic welding of girder.
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