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

2026-01-20

AI Summary

This work addresses the challenge of root cause identification and effective adaptation in robotic software systems operating under single or concurrent uncertainties in dynamic environments. To this end, we present a ROS2-based semantic segmentation experimental platform that integrates multi-sensor data with a perturbable machine learning preprocessing pipeline. By employing a controlled uncertainty injection mechanism, the platform realistically emulates performance degradation while enabling reproducible evaluation under concurrent root causes—a capability not previously supported. The framework explicitly distinguishes observable symptoms from underlying root causes and provides a complete toolchain alongside baseline controllers covering five representative symptom categories. This standardized and comparable experimental foundation significantly advances research on architecture-level self-healing and self-optimization for autonomous robotic systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• self-adaptation
• uncertainty
• robotic software systems
• dynamic environments
• semantic segmentation

Innovation

• sensor fusion
• semantic segmentation
• self-adaptation
• uncertainty injection
• ROS2