制造过程信息的隐马尔可夫模型分析

Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91 Pub Date : 1991-11-03 DOI:10.1109/IROS.1991.174556

B. Hannaford

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引用次数: 7

摘要

提出了一种利用隐马尔可夫模型(hmm)分析制造机械中传感器的力、扭矩和位置信号的方法。HMM可以检测接触状态之间的转换，并使用由制造工程师开发并在训练数据上优化的任务模型计算任务质量的度量。HMM方法已经在远程操作的广泛实验中进行了评估，结果表明在自动化和制造应用中具有更高的有效性。

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Hidden Markov model analysis of manufacturing process information

A method is presented for using hidden Markov models (HMMs) for the analysis of force, torque, and position signals from sensors in manufacturing machines. The HMM can detect the transitions between contact states and compute a measure of the task quality using a model of the task developed by the manufacturing engineer and optimized on training data. The HMM method has been evaluated in extensive experimentation with teleoperation and the results suggest even higher effectiveness in automation and manufacturing applications.<>

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来源期刊

Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91

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