SmartTune CLI: A repeatable local workflow for analyzing ArduPilot, Betaflight and PX4 flight logs

TL;DR in plain English

• What this does: run SmartTune CLI (command-line interface) to analyze flight logs and produce tuning guidance. The repository identifies SmartTune CLI as a multi-platform flight-log analysis and tuning advisor for ArduPilot, Betaflight and PX4 (https://github.com/raylanlin/smarttune-cli).
• Why use it: one CLI that targets three firmware families reduces tool switching. It centralizes log analysis and makes results easier to archive and audit.
• First actions: clone the repo, read the README, pick one representative log, and run one sample analysis. See the project at https://github.com/raylanlin/smarttune-cli.
• Safety note: treat automated tuning guidance as advisory. Always validate any parameter changes in a controlled environment before applying them to operational vehicles.

Quick starter checklist

• [ ] Clone repo (https://github.com/raylanlin/smarttune-cli)
• [ ] Choose one representative log
• [ ] Run one sample analysis and keep the generated artifacts (log + report)

Example scenario (concrete):

A small delivery-drone team sees a new customer log that shows a subtle roll oscillation. They clone https://github.com/raylanlin/smarttune-cli, pair the log with a minimal metadata file, run the CLI once, and get a readable report. The team uses the report to decide one bench test, then a short hover test, before applying a parameter change.

Plain-language explanation before advanced details:

This guide shows a repeatable, local workflow for running SmartTune CLI against flight logs. The goal is simple: get consistent, auditable tuning advice from logs, while keeping every change traceable. If you are not familiar with some terms: CLI means command-line interface; PID means proportional–integral–derivative (a common controller type); CI means continuous integration.

What you will build and why it helps

You will create a repeatable local workflow that runs SmartTune CLI against collected flight logs to produce consistent tuning guidance and saved artifacts. The repository describes SmartTune CLI as a multi-platform flight-log analysis and tuning advisor that covers ArduPilot, Betaflight and PX4 (https://github.com/raylanlin/smarttune-cli).

Why this helps

• Fewer tools: use one CLI for three common firmwares. That reduces context switching.
• Audit trail: each analysis produces a report plus the original log. That makes decisions reproducible.
• Simple automation: the process can be wrapped in one script or a CI job to run the same steps every time.

Primary outcomes

• A reproducible analysis artifact per log (report + original log).
• A short trace: log → metadata → analysis report → decision.
• A repeatable 3-step process you can run in a script or CI job.

Reference: https://github.com/raylanlin/smarttune-cli.

Before you start (time, cost, prerequisites)

Where to confirm core facts

• SmartTune CLI repo and README: https://github.com/raylanlin/smarttune-cli
• Supported firmwares noted in the repo README: ArduPilot, Betaflight, PX4
• Example usage and dependency notes: README and examples in the repo

Minimal practical prerequisites

• One workstation with command-line access and read permission to flight logs.
• At least one exported flight log (from your ground station) for the first run.
• Network access to clone https://github.com/raylanlin/smarttune-cli and fetch any dependencies listed in the README.

Estimated time and cost

• Plan 60–120 minutes for the first trial: clone, inspect README, install dependencies, run one sample.
• Software license cost: assumed $0 (open-source). Hardware, test flights, or pilot time are extra.

Step-by-step setup and implementation

Plain-language introduction before the commands:

The steps below are templates. Check the project README for exact flags, options, and required runtime versions before you run anything in production. Keep one canonical log and one metadata file for your first run so you can reproduce results.

1. Clone the repository and inspect the README

git clone https://github.com/raylanlin/smarttune-cli.git
cd smarttune-cli
less README.md

2. Check dependencies and runtimes referenced in the README. Install what the README requires. Confirm any required runtime versions before proceeding.

3. Prepare a minimal metadata file to pair with each log. Adapt to your conventions and verify required keys in the project README.

# example metadata.yaml — template only; verify required keys in the project README
airframe_id: AF-001
firmware: PX4
firmware_version: "<replace-with-version>"
notes: "initial-analysis"

4. Run a single sample analysis. Use the exact CLI invocation shown in the README at https://github.com/raylanlin/smarttune-cli. Keep the produced report next to the original log.

5. Store artifacts together and record provenance: include the repo URL and the working commit hash used for the run.

Rollout guardrails (process-level)

• Canary: limit initial parameter changes to one aircraft in a set of similar airframes.
• Rollback: export and keep previous parameter sets ready for immediate rollback.
• Verification: require at least one bench test and one controlled hover/flight test before fleet-wide changes.

Common problems and quick fixes

Reference: https://github.com/raylanlin/smarttune-cli for repo-related issues.

• Symptom: CLI errors when parsing a log.
  • Quick fix: re-export the log from your ground station. Ensure the log contains the expected telemetry messages and try again.
• Symptom: the output report is missing fields you expect.
  • Quick fix: add or correct metadata (firmware and version) and repeat the analysis.
• Symptom: permission denied reading files.
  • Quick fix: check file ownership and permissions. Run the process as the file owner if needed.

Quick decision table

| Symptom | First check | Quick action | |---|---:|---| | Unsupported log format | Log type / firmware | Re-export log with standard telemetry | | Missing timestamps | Logger settings | Reconfigure logging rate and retest | | Unexpected runtime error | Dependency list in README | Install or update required runtime/library |

When troubleshooting, keep both the raw log and the CLI report. This helps reproduce the issue and file a clear bug report at https://github.com/raylanlin/smarttune-cli if needed.

First use case for a small team

This section targets small teams (1–3 people). The goal: a lightweight path from "log received" to "safe, auditable tuning decision." Keep the loop small and fast.

Actionable steps for a solo founder or a 2–3 person team

1. Single-log canonical sample: pick one representative log per airframe type. Use that log as your baseline. Re-run analysis on it to validate your setup.

2. One-script automation: create a shell script that clones or updates the repo, runs the analysis on the canonical log, and outputs a ZIP containing the original log, metadata, report, and a short changelog. Keep the script runnable in three commands or fewer.

3. Minimal approval gate: require one sign-off (operator or lead) and one bench test before any parameter change leaves the lab. For small teams this keeps decisions quick and accountable.

4. Simple monitoring and rollback: after a canary update, monitor the vehicle for 24–72 hours for regressions. Keep the previous parameter set accessible for immediate rollback.

5. Record provenance: store the repo URL and commit hash used for each analysis so any result can be reproduced.

Practical artifacts to create

• One canonical log per airframe type.
• One metadata file paired with each log.
• One automation script that runs analysis → packages artifacts → archives results.

Reference repo: https://github.com/raylanlin/smarttune-cli.

Technical notes (optional)

Plain-language summary first:

SmartTune CLI analyzes flight logs to produce tuning advice. It supports multiple firmwares. The README has the definitive list of supported message types, flags, and dependency versions. Pin the repo commit you used so results are reproducible.

The repository describes SmartTune CLI as a multi-platform flight-log analysis and tuning advisor that covers ArduPilot, Betaflight and PX4 (https://github.com/raylanlin/smarttune-cli). Consult the README for exact flags, supported message types, and dependency versions.

Config keys you may want to track in metadata (pick and enforce team conventions):

• log_path
• airframe_id
• firmware
• firmware_version
• notes / tags

Example JSON metadata (template):

{
  "airframe_id": "AF-001",
  "firmware": "<ArduPilot|Betaflight|PX4>",
  "firmware_version": "<version>",
  "notes": "initial-analysis"
}

Ops note: pin the repository URL and commit hash used for production analyses so reports are reproducible. See: https://github.com/raylanlin/smarttune-cli.

What to do next (production checklist)

Assumptions / Hypotheses

• The repository README contains the authoritative dependency versions, exact command syntax, and examples; verify commands before production runs at https://github.com/raylanlin/smarttune-cli.
• Planning numeric targets used here (examples only):
  • 60–120 minutes: expected time for a first trial (clone + inspect + one sample run).
  • $0: assumed software license cost (open-source); hardware and flight costs are extra and not included.
  • 1 hover log and 1 aggressive-maneuver log: minimal set for broader initial coverage (2 logs total).
  • Canary size: 1 of 5 similar airframes for staged rollout.
  • Monitoring window after an update: 24–72 hours.
  • Team size example: 3 people (1 pilot, 1 analyst, 1 approver).
  • Example template PID placeholders used in planning (do not apply without validation): roll.p = 0.15, roll.i = 0.10, roll.d = 0.003.
  • Firmware example used in templates: 1.12.0.

These are hypotheses and planning targets; verify them with real data and the repository README before enforcing as policy.

Risks / Mitigations

• Risk: applying automated recommendations blindly can cause control oscillations or instability.
  • Mitigation: require bench validation and a controlled hover test; keep a fast rollback path.
• Risk: analysis results vary with firmware versions and log content.
  • Mitigation: record firmware version and pin the analysis to a repo commit hash for reproducibility (https://github.com/raylanlin/smarttune-cli).
• Risk: missing or corrupt logs reduce analysis accuracy.
  • Mitigation: enforce a minimal logger configuration and store raw logs as immutable artifacts for auditing.

Next steps

• Verify exact CLI flags and dependency versions in the project README at https://github.com/raylanlin/smarttune-cli.
• Run an initial analysis on one sample log, store the report with the original log and metadata, and record the repo commit hash.
• Publish a minimal rollout checklist and require sign-off before fleet-wide parameter changes.

Final production checklist

• [ ] Pin repo URL and commit hash used for analysis (https://github.com/raylanlin/smarttune-cli)
• [ ] Publish a team rollout checklist with approver names
• [ ] Run a canary on one airframe and monitor for 24–72 hours
• [ ] If canary passes, roll out to remaining airframes with staged gates

SmartTune CLI: A repeatable local workflow for analyzing ArduPilot, Betaflight and PX4 flight logs

Step-by-step guide to run SmartTune CLI locally to analyze ArduPilot, Betaflight and PX4 flight logs. Learn a repeatable, auditable workflow that produces tuni…

https://aisignals.dev/posts/2026-05-03-smarttune-cli-a-repeatable-local-workflow-for-analyzing-ardupilot-betaflight-and-px4-flight-logs

(Weekly: AI news, agent patterns, tutorials)