在废旧锂电池模组的自动化拆解过程中,需要快速地对其表面数量众多的各类螺纹紧固件进行精准位姿识别。针对已有特征匹配方法难以适应紧固件周围复杂背景环境及深度学习方法无法实现紧固件中心精确定位与姿态识别的现状,基于轻量化深度...在废旧锂电池模组的自动化拆解过程中,需要快速地对其表面数量众多的各类螺纹紧固件进行精准位姿识别。针对已有特征匹配方法难以适应紧固件周围复杂背景环境及深度学习方法无法实现紧固件中心精确定位与姿态识别的现状,基于轻量化深度学习模型SqueezeNet与紧固件BLOB(Binary Large Object)特征分析,以由粗到精的识别策略将上述两类方法结合,快速实现紧固件的种类判别与精确定位。并在此基础上进一步提出区域相交法用于准确识别各类紧固件的头部姿态角。实验结果表明:所提方法与其他现有识别模型相比,不仅获得了较高的粗定位精度(94.9%),并且紧固件中心精定位误差与头部姿态角误差分别在0.3 mm与3°之内,能够很好地满足机器人拆卸紧固件的应用需求。展开更多
This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm...This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.展开更多
文摘在废旧锂电池模组的自动化拆解过程中,需要快速地对其表面数量众多的各类螺纹紧固件进行精准位姿识别。针对已有特征匹配方法难以适应紧固件周围复杂背景环境及深度学习方法无法实现紧固件中心精确定位与姿态识别的现状,基于轻量化深度学习模型SqueezeNet与紧固件BLOB(Binary Large Object)特征分析,以由粗到精的识别策略将上述两类方法结合,快速实现紧固件的种类判别与精确定位。并在此基础上进一步提出区域相交法用于准确识别各类紧固件的头部姿态角。实验结果表明:所提方法与其他现有识别模型相比,不仅获得了较高的粗定位精度(94.9%),并且紧固件中心精定位误差与头部姿态角误差分别在0.3 mm与3°之内,能够很好地满足机器人拆卸紧固件的应用需求。
文摘This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.
