Model-driven self-adaptation of robotics software using probabilistic approach

Arunkumar Ramaswamy, B. Monsuez, A. Tapus
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引用次数: 2

Abstract

A typical feature of robotic architectures are its reactivity and self-adaptivity. In practice, this is achieved by context-dependent dynamic invocation of software components in robotic architectures. In this paper, we specifically address how this self-adaptation capability can be formally defined and modeled in an architecture-independent way. We propose a probabilistic approach that facilitates system design and dynamic runtime adaptation satisfying the quality requirements. We also show how such techniques are incorporated in our model-driven framework: Self Adaptive Framework for Robotic Systems.
基于概率方法的机器人软件模型驱动自适应
机器人结构的一个典型特征是反应性和自适应性。在实践中,这是通过机器人体系结构中与上下文相关的软件组件的动态调用来实现的。在本文中,我们特别讨论了如何以一种与体系结构无关的方式正式定义和建模这种自适应能力。我们提出了一种概率方法,便于系统设计和动态运行时适应满足质量要求。我们还展示了如何将这些技术纳入我们的模型驱动框架:机器人系统的自适应框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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