RRA: Models and tools for robotics run-time adaptation
RRA: Models and tools for robotics run-time adaptation
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RRA：机器人运行时适应的模型和工具
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
N. Hochgeschwender
中科院分区：
文献类型：
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作者：
Luca Gherardi;N. Hochgeschwender
机器人应用的特点是具有巨大的可变性。他们的设计要求开发人员在几个变种中进行选择，这些变种既涉及功能，也涉及硬件。其中一些选择可以在部署时进行，而其他选择则应该在运行时进行，因为有关上下文的更多信息已知。要实现这一点，软件系统需要能够对其当前状态进行推理，并调整其体系结构以提供最适合上下文的配置。提出了一种基于模型的机器人系统运行时自适应方法。它定义了一组表示系统体系结构、其可变性和上下文状态的正交模型。此外，它还引入了一组算法，这些算法对我们的模型中表示的知识进行推理，以解决运行时的可变性并适应系统架构。本文通过两个案例对该方法进行了讨论和评价。
Robotics applications are characterized by a huge amount of variability. Their design requires the developers to choose between several variants, which relate to both functionalities and hardware. Some of these choices can be taken at deployment-time, however others should be taken at run-time, when more information about the context is known. To make this possible, a software system needs to be able to reason about its current state and to adapt its architecture to provide the configuration that best suites the context. This paper presents a model-based approach for run-time adaptation of robotic systems. It defines a set of orthogonal models that represent the system architecture, its variability, and the state of the context. Additionally it introduces a set of algorithms that reason about the knowledge represented in our models to resolve the run-time variability and to adapt the system architecture. The paper discusses and evaluates the approach by means of two case studies.