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 operate amid (1) uncertainties, where symptoms are easy to observe but root causes are ambiguous, or (2) multiple uncertainties appear 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 symptoms, where each can be caused by different root causes and supports concurrent uncertainties spanning self-healing and self-optimisation. SUNSET includes the segmentation pipeline, a trained ML model, uncertainty-injection scripts, a baseline controller, and step-by-step integration and evaluation documentation to facilitate reproducible studies and fair comparison.

翻译：随着机器人在动态环境中部署日益频繁，其软件系统复杂性不断提升，对自适应方法的需求愈发迫切。在这些环境中，机器人软件系统越来越多地面临以下情况：(1) 存在不确定性，其症状易于观察但根本原因难以确定；(2) 多种不确定性同时出现。本文提出SUNSET——一个基于ROS2的范例系统，可在上述条件下实现对基于架构的自适应方法进行严谨、可重复的评估。该系统实现了由训练完成的机器学习模型驱动的传感器融合语义分割流程，通过扰动其输入预处理可诱发真实的性能退化。该范例呈现五种可观测症状，每种症状可由不同根本原因引发，并支持涵盖自修复与自优化的并发不确定性场景。SUNSET包含完整分割流程、训练完成的机器学习模型、不确定性注入脚本、基线控制器以及逐步集成与评估文档，以促进可复现研究和公平比较。