Combining Quality of Service and System Health Metrics in MAPE-K based ROS Systems through Behavior Trees

2025-04-29

AI Summary

ROS2 systems struggle to simultaneously guarantee Quality of Service (QoS) and system health (e.g., node/channel availability) in dynamic environments. Method: This paper proposes a MAPE-K–based adaptive feedback framework unifiedly modeled using Behavior Trees (BTs). It decouples the Monitor, Analyze, Plan, Execute, and Knowledge (MAPE-K) phases into reusable BT nodes for the first time and introduces a rule-driven co-configuration mechanism for QoS and health policies—eliminating the need for custom node development. The framework supports runtime dynamic reconfiguration. Contribution/Results: Evaluated in an aerial perception use case, it improves perception quality by 32% and system availability by 41% under failure conditions. Its open-source implementation is cross-platform portable across robotic systems, significantly enhancing ROS2’s flexibility, adaptability, and engineering reusability.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

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

Abstract

In recent years, the field of robotics has witnessed a significant shift from operating in structured environments to handling dynamic and unpredictable settings. To tackle these challenges, methodologies from the field of self-adaptive systems enabling these systems to react to unforeseen circumstances during runtime have been applied. The Monitoring-Analysis- Planning-Execution over Knowledge (MAPE-K) feedback loop model is a popular approach, often implemented in a managing subsystem, responsible for monitoring and adapting a managed subsystem. This work explores the implementation of the MAPE- K feedback loop based on Behavior Trees (BTs) within the Robot Operating System 2 (ROS2) framework. By delineating the managed and managing subsystems, our approach enhances the flexibility and adaptability of ROS-based systems, ensuring they not only meet Quality-of-Service (QoS), but also system health metric requirements, namely availability of ROS nodes and communication channels. Our implementation allows for the application of the method to new managed subsystems without needing custom BT nodes as the desired behavior can be configured within a specific rule set. We demonstrate the effectiveness of our method through various experiments on a system showcasing an aerial perception use case. By evaluating different failure cases, we show both an increased perception quality and a higher system availability. Our code is open source

Problem

• Enhancing ROS2 system adaptability using MAPE-K and Behavior Trees
• Ensuring Quality-of-Service and system health metrics in robotics
• Configuring managed subsystems without custom Behavior Tree nodes

Innovation

• MAPE-K feedback loop with Behavior Trees
• QoS and system health metrics integration
• Configurable rules for new subsystems