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A language theoretic framework for decision and control of autonomous systems.

机译:用于自治系统决策和控制的语言理论框架。

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摘要

Autonomous systems are increasingly prevalent in many application areas such as military Command and Control, industrial manufacturing, and medical systems. However, analysis of such systems becomes intractable when modelled by classical methods like differential/difference equations. Discrete-event systems are increasingly being used to model and control such systems and a body of literature called supervisory control theory (SCT) has emerged. Traditionally, SCT uses a qualitative method called maximal permissiveness to arrive at optimal supervisors. A quantitative performance index based on a measure of formal languages has been proposed to overcome the shortcomings of qualitative design in SCT. The research reported in this thesis has adopted the concept of language measure to develop a comprehensive framework for decision & control of autonomous systems. A consistent framework based on language theory is built to address sensing, planning and action aspects of autonomous systems. The proposed framework is demonstrated by its application to well known problems in robotics such as obstacle avoidance and path planning. The discrete-event models are constructed and pertinent parameters have been identified from the physical dynamics of the system via a system identification. This approach creates a seamless transition from the continuous dynamics to the discrete-event control by eliminating the need to manually design the discrete-event models of the plant dynamics and control specifications. In the language-theoretic setting, autonomous perception is also addressed using a tool called symbolic dynamic filtering (SDF). Application of SDF as a feature extractor in a domain independent manner is demonstrated and pattern classifiers are constructed to recognize behaviours autonomously. A new type of pattern classifier based on the language measure is proposed and is shown to be faster than traditional Bayesian classifier with comparable robustness. The proposed feature extraction and classification methods are validated on a distributed network of piezoelectric sensors to classify the behaviors of two types of robots in real time.
机译:自治系统在许多应用领域中越来越普遍,例如军事指挥与控制,工业制造和医疗系统。但是,通过经典方法(如微分/差分方程)对此类系统进行分析变得棘手。离散事件系统正越来越多地用于建模和控制此类系统,并且出现了称为监督控制理论(SCT)的大量文献。传统上,SCT使用一种称为最大允许性的定性方法来得出最佳主管。为了克服SCT中定性设计的缺点,提出了一种基于形式语言的定量性能指标。本文所报道的研究采用了语言度量的概念,为自治系统的决策和控制开发了一个综合框架。建立了基于语言理论的一致框架,以解决自治系统的感知,计划和动作方面的问题。通过将其应用于机器人技术中的众所周知的问题(例如避障和路径规划)来证明所提出的框架。构建了离散事件模型,并已通过系统标识从系统的物理动力学中确定了相关参数。这种方法无需手动设计工厂动态和控制规范的离散事件模型,从而实现了从连续动态到离散事件控制的无缝过渡。在语言理论的环境中,还使用称为符号动态过滤(SDF)的工具解决自主感知的问题。演示了SDF作为领域独立的特征提取器的应用,并构建了模式分类器以自主识别行为。提出了一种基于语言测度的新型模式分类器,并显示出比传统贝叶斯分类器更快的鲁棒性。在压电传感器的分布式网络上对提出的特征提取和分类方法进行了验证,以对两种类型的机器人的行为进行实时分类。

著录项

  • 作者

    Mallapragada, Goutham.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Electronics and Electrical.;Engineering Mechanical.;Engineering Robotics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 157 p.
  • 总页数 157
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;机械、仪表工业;
  • 关键词

  • 入库时间 2022-08-17 11:37:38

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