任务(项目管理)
信号(编程语言)
计算机科学
瞬态(计算机编程)
扭矩
人工智能
接触力
还原(数学)
序列(生物学)
模拟
控制理论(社会学)
计算机视觉
控制工程
工程类
物理
数学
操作系统
热力学
生物
程序设计语言
系统工程
控制(管理)
量子力学
遗传学
几何学
作者
Andreas Stolt,Magnus Linderoth,Anders Robertsson,Rolf Johansson
标识
DOI:10.1109/icra.2015.7139293
摘要
A robotic assembly task is usually implemented as a sequence of simple motions, and the transitions between the motions are made when some events occur. These events can usually be detected with thresholds on some signal, but faster response is possible by detecting the transient on that signal. This paper considers the problem of detecting these transients. A force-controlled assembly task is used as an experimental case, and transients in measured force/torque data are considered. A systematic approach to train machine-learning based classifiers is presented. The classifiers are further implemented in the assembly task, resulting in a 15%reduction of the total assembly time.
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