| name | ardupilot-diagnostics |
| description | Diagnose ArduPilot .BIN flight logs to determine the root cause of crashes or failures using the Hybrid AI Engine. |
| risk | safe |
| source | ArduPilot GSoC 2026 Project (MIT) |
| date_added | 2026-03-30 |
ArduPilot Log Diagnostics Skill
Overview
This skill grants an AI agent the ability to autonomously diagnose ArduPilot .BIN flight telemetry logs. Instead of manually inspecting graphs or MAVLink messages, the agent can feed the log into the Hybrid Causal Arbiter (Rule Engine + XGBoost ML) to extract the primary failure reason, 3D trajectory evidence, and actionable maintenance recommendations.
When to Use This Skill
- When a user provides a
.BIN file and asks "Why did my drone crash?"
- When analyzing flight performance degradation (e.g., high vibrations, compass interference).
- When generating a post-flight maintenance report for a fleet of autonomous vehicles.
Prerequisites
- The system must have the
ardupilot-log-diagnosis Python environment active.
- The
.BIN file must be accessible on the local filesystem.
Workflow Instructions
1. Execute the Diagnostic Engine CLI
To analyze a .BIN file, execute the built-in command-line interface:
python -m src.cli.main analyze /path/to/flight.bin
2. Parse the output
Read the resulting JSON or terminal output carefully. You must extract:
- Decision: (Healthy, Warning, or Critical Crash)
- Top Root Cause: (e.g., Compass Interference, Motor Imbalance, EKF Failsafe)
- Confidence/ECE: State the ML calibration confidence mathematically (e.g., F1 1.0, 99.8% confident).
- Evidence: Which specific parameters/thresholds were violated?
- Recommendations: What physical repairs or tuning steps should the pilot take?
3. Present the Findings
Do not dump raw JSON logic to the user. Synthesize a professional Drone Mechanic Report:
- Summary: Briefly explain what happened to the vehicle.
- Causal Chain: Detail the timeline of the failure (if available).
- Prescription: Give the user the exact steps to prevent this in the future.
Failure Shields (Important)
- If the tool reports an unknown file parsing error, inform the user that their
.BIN file may be corrupted or truncated mid-air.
- If the tool falls back to the Rule Engine Only, inform the user that the ML model abstained due to low confidence on this specific edge case in order to preserve diagnostic integrity.
