基于ROS2的自适应系统测试平台，支持多故障并发模拟与评估
SUNSET - A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation
- 构建基于传感器融合的语义分割流水线，可注入真实性能退化
- 支持五种可观测故障，可并发触发自愈与自优化任务
- 提供完整代码与文档，便于复现和对比实验
随着机器人在动态环境中部署增多，其软件系统复杂度提升，亟需自适应方法。此类系统常面临两类挑战：（1）症状明显但根因模糊的故障；（2）多个故障同时发生。本文提出SUNSET，一个基于ROS2的示范系统，用于严格、可重复地评估基于架构的自适应能力。该系统实现了一条由训练好的机器学习模型驱动的传感器融合语义分割流水线，可通过扰动输入预处理引发真实性能退化。系统暴露五种可观测故障，每种可由不同故障引发，并支持覆盖自愈与自优化的并发故障。SUNSET包含分割流水线、训练好的ML模型、故障注入脚本、基线控制器及分步集成与评估文档，代码已开源（https://github.com/XITASO/sunset），便于开展可复现研究。
原文摘要 · Abstract (English)
The fact that robots are getting deployed more often in dynamic environments, together with the increasing complexity of their software systems, raises the need for self-adaptive approaches. In these environments robotic software systems increasingly encounter (1) failures whose symptoms are easy to observe but root causes might be ambiguous or (2) multiple failures appearing concurrently. We present SUNSET, a ROS2-based exemplar that enables rigorous, repeatable evaluation of architecture-based self-adaptation in such conditions. It implements a sensor fusion semantic-segmentation pipeline driven by a trained Machine Learning (ML) model whose input preprocessing can be perturbed to induce realistic performance degradations. The exemplar exposes five observable failures, each of which can be caused by different faults and supports concurrent failures spanning self-healing and self-optimisation. SUNSET includes the segmentation pipeline, a trained ML model, fault-injection scripts, a baseline controller for further comparisons, and step-by-step integration and evaluation documentation to facilitate reproducible studies. The code is available at https://github.com/XITASO/sunset.
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