AROSpect: A ROS 2 Timing Introspection Framework
Lukas Dust, Christopher Steven Timperley, Rong Gu
AI summary
Problem
Developers lack tools for systematic, controlled timing experimentation in ROS 2, making it difficult to predict end-to-end latencies, identify bottlenecks, and ensure timing correctness in safety-critical robotic applications.
Approach
The framework models ROS 2 architectures using standardized node templates, injects controlled delays, and tracks message timestamps across processing paths to enable reproducible timing analysis and iterative parameter refinement.
Key results
- Systematic framework for ROS 2 timing introspection and controlled experimentation
- Executable node templates (sensors, filters, fusions, actuators) for reproducible modeling
- Standardized message abstraction enabling precise end-to-end latency tracking
- Case study demonstrating misconfiguration detection and iterative timing optimization in multi-agent turtlesim
Why it matters
Provides robotics developers with a practical tool to predict, analyze, and optimize timing behavior in distributed ROS 2 systems before deployment.
Abstract
This paper introduces AROSpect, a framework for timing introspection and controlled experimentation for ROS 2-based applications. AROSpect enables developers to model system components using standardized templates, inject synthetic delays, and measure end-to-end latencies across mes- sage paths. Through instrumentation, the framework supports iterative refinement of timing parameters and identification of misconfigurations. A case study using a multi-agent turtlesim system demonstrates how AROSpect can guide developers to understand the effects of adapting timing parameters, con- tributing toward more predictable robotic systems.