ROSA：一种基于知识的机器人自适应解决方案
ROSA: A Knowledge-based Solution for Robot Self-Adaptation
AI总结：
本文提出了一种基于知识的机器人自适应框架ROSA，通过任务与架构协同适应（TACA）实现运行时自适应，并在水下机器人应用中验证了其可行性和性能优势。
摘要：
自主机器人必须在多样化的环境中运行，并在不确定性下处理多个任务。这给软件架构和任务决策算法的设计带来了挑战，因为不同的上下文可能需要不同的任务逻辑和架构配置。为了解决这个问题，机器人系统可以被设计为自适应系统，能够在运行时根据其上下文调整任务执行和软件架构。本文介绍了ROSA，一种用于机器人自适应（RObot Self-Adaptation）的新型基于知识的框架，它使机器人系统能够实现任务与架构协同适应（TACA）。ROSA通过提供一个捕获自适应所需的所有特定于应用知识的知识模型，并在运行时对该知识进行推理以确定何时以及如何进行自适应来实现这一点。除了概念框架之外，这项工作还提供了一个基于ROS 2的开源参考实现，并在水下机器人应用中评估了其可行性和性能。实验结果突出了ROSA在设计自适应机器人系统方面的可重用性和开发工作量的优势。
英文摘要：
Autonomous robots must operate in diverse environments and handle multiple tasks despite uncertainties. This creates challenges in designing software architectures and task decision-making algorithms, as different contexts may require distinct task logic and architectural configurations. To address this, robotic systems can be designed as self-adaptive systems capable of adapting their task execution and software architecture at runtime based on their context.This paper introduces ROSA, a novel knowledge-based framework for RObot Self-Adaptation, which enables task-and-architecture co-adaptation (TACA) in robotic systems. ROSA achieves this by providing a knowledge model that captures all application-specific knowledge required for adaptation and by reasoning over this knowledge at runtime to determine when and how adaptation should occur. In addition to a conceptual framework, this work provides an open-source ROS 2-based reference implementation of ROSA and evaluates its feasibility and performance in an underwater robotics application. Experimental results highlight ROSA's advantages in reusability and development effort for designing self-adaptive robotic systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
