Abstract
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.
Abstract (translated)
机器人在动态环境中部署得越来越频繁，加之其软件系统的复杂性不断增加，这提高了自适应方法的需求。在这种环境下，机器人的软件系统越来越多地在以下两种不确定性中运行：（1）不确定性存在于症状易于观察但根本原因模糊的情况；或者（2）多种不确定性同时出现。我们提出了SUNSET，这是一个基于ROS2的示例，旨在支持在这些条件下对架构驱动自适应进行严格且可重复的评估。该示例实现了一个由训练好的机器学习(ML)模型驱动的传感器融合语义分割流水线，并可通过扰动输入预处理来诱导现实中的性能下降。SUNSET展示了五种可观测的症状，每种症状可能有多种根本原因，并支持同时存在的不确定性和自我恢复及自我优化问题。该示例包含一个分割管道、一个训练好的ML模型、不确定性注入脚本、基线控制器以及详细的集成和评估文档，以促进可重复研究和公平比较。
URL
https://arxiv.org/abs/2601.13732
