Trace-Enabled Timing Model Synthesis for ROS2-based Autonomous Applications

Autonomous applications are typically developed over Robot Operating System 2.0 (ROS2) even in time-critical systems like automotive. Recent years have seen increased interest in developing model-based timing analysis and schedule opti-mization approaches for ROS2-based applications. To complement these approaches, we propose a tracing and measurement framework to obtain timing models of ROS2-based applications. It offers a tracer based on extended Berkeley Packet Filter that probes different functions in ROS2 middleware and reads their arguments or return values to reason about the data flow in applications. It combines event traces from ROS2 and the operating system to generate a directed acyclic graph showing ROS2 callbacks, precedence relations between them, and their timing attributes. While being compatible with existing analyses, we also show how to model (i) message synchronization, e.g., in sensor fusion, and (ii) service requests from multiple clients, e.g., in motion planning. Considering that, in real-world scenarios, the application code might be confidential and formal models are unavailable, our framework still enables the application of existing analysis and optimization techniques. We demonstrate our framework's capabilities by synthesizing the timing model of a real-world benchmark implementing LIDAR-based localization in Autoware's Autonomous Valet Parking.