让机器人在复杂环境中自动调整任务和系统结构，提升适应能力。
ROSA: A Knowledge-based Solution for Robot Self-Adaptation
- 基于知识模型实时推理，实现任务与架构协同自适应
- 在水下机器人应用中验证，显著降低开发成本
- 开源实现支持快速复用，适合多场景机器人系统
自主机器人需在多样化环境和多重任务中应对不确定性，这对软件架构与任务决策算法设计提出挑战。本文提出ROSA——一种面向机器人自适应的知识驱动框架，支持任务与架构协同自适应（TACA）。ROSA通过构建包含所有应用特定知识的模型，并在运行时进行推理，以决定何时及如何进行适应。本研究还提供基于ROS 2的开源参考实现，并在水下机器人应用中评估其可行性与性能。实验表明，ROSA在系统可复用性和开发效率方面具有明显优势。
原文摘要 · Abstract (English)
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.
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