目的 为了提高电池生产企业的检测效率，避免误检，以降低企业生产成本。方法 分析传统分拣方式中存在的不足之处，以 ABB 并联机器人和美国康纳智能相机为平台，搭建一个基于机器视觉的工业机器人分拣系统。运用经典的 SIFT 算法对分拣对象的图片信息进行处理，克服了光照和位置变化对视觉系统造成的不良影响，提高分拣了效率。结果 ABB 并联机器人根据图像信息能识别出待分拣的物体，且工作稳定、可靠，分拣成功率为 100%。结论 该分拣系统软硬件设计合理，满足了电池生产企业的要求。

The work aims to improve the detection efficiency of battery production enterprises and avoid false detection to reduce production cost of the enterprise. The deficiencies in traditional sorting methods were analyzed. An industrial robot sorting system based on machine vision was constructed with ABB parallel robot and American Connal intelligent camera as platform. The classic SIFT algorithm was used to process the image information of sorted objects, which overcame the adverse impacts of illumination and position change on visual system and improved the sorting efficiency. ABB parallel robot could identify objects to be sorted according to image information. It worked stably and reliably and enjoyed success rate of 100%. The software and hardware of the sorting system are reasonably designed and meet the requirements of battery manufacturers.