ROS 2 Real-Time Robotics Performance

The ROS 2 Real-Time Robotics Performance article outlines a comprehensive approach to optimizing AI systems in robotics for predictable latency, controlled memory use, reasonable power consumption, and reliable behavior. The key steps include establishing a baseline by recording performance metrics such as end-to-end latency, processing FPS, CPU and GPU utilization, network RTT, dropped frames, temperature, and power impact. It's crucial to keep the test scenario identical across benchmark runs.

Next, identify bottlenecks by measuring each stage separately - Capture, Transfer, Decode, Preprocess, Inference, Postprocess, and UI. Optimize the stage contributing the most latency instead of optimizing arbitrary code. Control the processing rate by not automatically processing every sensor event. Instead, use a strategy like checking if the system is busy and returning if it is.

Separate workloads into different priority paths - High for robot commands and safety, Medium for perception and navigation, and Low for analytics, logging, and cloud upload. This ensures that a large video upload, for instance, won't block a safety command. Reduce unnecessary data copies by watching for pipelines that convert data multiple times, such as Camera → YUV → RGB → Bitmap → JPEG → Base64. Keep data in its appropriate native representation for as long as possible.

Bounded queues are essential to prevent temporary overloads from turning into seconds of stale latency. Use a small buffer or latest-frame strategy for time-sensitive perception. Profile the target hardware thoroughly, whether it's an Android/Flutter or Jetson system, measuring CPU, GPU, memory, temperature, and sustained behavior under the complete robotics workload.

Validate sustained performance by running longer tests that record the performance curve, rather than just a 30-second benchmark that can hide issues like thermal throttling or memory pressure. Test failure conditions, including weak network, disconnected devices, high CPU load, GPU memory pressure, low battery, thermal throttling, robot safety stops, and cloud unavailability. The system should degrade gracefully under these conditions.

Document your findings in a benchmark table, including the version, FPS, latency, RAM, GPU utilization, and temperature. Before and after measurements are essential to understand the improvements made. Finally, apply domain-specific optimizations like removing unnecessary work, reducing data movement, bounding queues, moving expensive work away from UI/control threads, using hardware acceleration where supported, and re-measuring accuracy and latency. Always run a sustained test and test failure behavior, then document the improvement.