Vision Latency Diagnostic Toolkit (Python Scripts + Oscilloscope Sync Protocol)

Vision latency—the total delay between a physical scene change and the corresponding robot motion response—is a critical performance bottleneck in real-time vision-guided robotics. This toolkit addresses the challenge by instrumenting the entire pipeline: Python scripts (using OpenCV, NumPy, and hardware I/O libraries like PySerial or libgpiod) generate TTL sync pulses marking key events (e.g., frame exposure start, inference completion, trajectory command dispatch), while an oscilloscope captures these pulses alongside hardware reference signals (e.g., camera strobe output, robot controller enable line). Time-of-flight measurements between synchronized edges yield precise, traceable latency breakdowns per subsystem—enabling root-cause analysis of bottlenecks in GPU inference, network transport, motion controller buffering, or mechanical response. The protocol enforces deterministic timing via hardware-triggered acquisition (e.g., GPIO-interrupt-driven timestamping with nanosecond-resolution clocks via Linux PTP or hardware timers) and supports calibration against known-delay reference circuits. Crucially, it bridges software-defined timestamps with hardware-observable signals, satisfying ISO/IEC 15067-2 traceability requirements for industrial automation diagnostics.