SUNSET -- A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation

TL;DR

SUNSET is a ROS2-based exemplar for evaluating self-adaptation in robotic systems operating under uncertainties, using sensor fusion and ML-driven semantic segmentation with uncertainty injection for realistic testing.

Contribution

It introduces a comprehensive, reproducible framework for testing self-adaptive robotic software under multiple uncertainties with realistic performance degradations.

Findings

Supports concurrent uncertainties in self-healing and self-optimisation

Provides a modular pipeline with uncertainty-injection capabilities

Facilitates fair comparison and evaluation of self-adaptive approaches

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.