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Autonomous UAV Systems · Free resource

Edge AI & UAV Field Checklist

Pre-deployment gate for teams shipping edge inference, ROS 2 / PX4 autonomy, and field telemetry — from mission constraints to research→product handoff.

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1. Mission & constraints

Success metrics defined (latency, accuracy, uptime, mission range)
Operating environment documented (GPS-denied? weather? compute budget?)
Fail-safe behavior agreed (RTL, hold, land, human takeover)
Data ownership & privacy constraints written down

2. Sensing & perception

Sensor suite matched to task (RGB / depth / IMU / baro)
Calibration & sync strategy for multi-sensor fusion
Model input resolution vs. FPS trade-off measured on target hardware
False-positive / false-negative cost understood for the use case

3. Edge compute

Target device chosen (Jetson class / MCU+accelerator / industrial PC)
Thermal & power envelope validated under continuous load
Model optimized (TensorRT / quantization) with accuracy regression tests
Watchdog + restart policy for inference process crashes

4. Autonomy & control (UAV)

Flight stack clear (PX4 / ArduPilot) and offboard API stable
MAVLink / ROS 2 bridge latency budgeted end-to-end
Communication-loss handling tested (timeouts, RTL, geofence)
Simulation (Gazebo / SITL) covers nominal + loss-of-link cases

5. Telemetry & ops

MQTT / telemetry topics versioned; schema documented
Ground dashboards show health, not only pretty maps
Logging retention enough for incident replay
OTA / update path defined (or explicitly out of scope)

6. Research → product handoff

Prototype code separated from notebook/one-off scripts
Benchmarks reproducible on clean hardware images
Acceptance tests written before “field trial”
Owner named for post-pilot maintenance

Want this applied to your stack? Book a discovery call or request an async audit at edgefield-consulting.pages.dev · aykumar2106@gmail.com

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