Lands the first task of the implementation epic AZ-626: a cargo workspace
with 14 crates (shared + autopilot binary + 12 component crates), a
multi-stage Dockerfile + dev/test compose stacks, a Woodpecker CI pipeline,
the on-airframe systemd unit with flight-gate wiring, three environment
TOML configs, and the canonical entity catalogue from data_model.md as
`shared::models`.
Per-AC verification (full detail in
_docs/03_implementation/batch_01_cycle1_report.md):
- AC-1 cargo check --workspace clean
- AC-2 cargo test --workspace passes; per-crate it_compiles() <0.01 s
- AC-6 cargo build/test --no-default-features clean; VlmClient default
impl returns VlmAssessment::disabled()
- AC-9 tracing-subscriber emits JSON logs with ts/level/target/fields
- AC-10 runtime::ensure_state_directories creates mapobjects/, audit/,
pending_pushes/ under storage.state_dir
Deferred to external infra (artifacts written, verification re-runs in CI
and in downstream tasks):
- AC-3 Woodpecker runner; CI yml in place
- AC-4 docker-compose mocks land with AZ-660/AZ-644/AZ-675
- AC-5 SITL conformance lands with AZ-641/AZ-648/AZ-652
- AC-7 aarch64 cross-compile via cargo-zigbuild stage
- AC-8 systemd unit (Linux + systemd host)
Layering invariants from module-layout.md hold: shared (L1) imports
nothing; Layer 2 actor crates import only shared; Layer 3 coordinators
(operator_bridge, mission_executor) import only their documented Layer 2
deps; Layer 4 (scan_controller) imports its documented Layer 2 + Layer 3
deps; the autopilot binary (L5) is the only consumer of every component.
cargo fmt --all --check + cargo clippy --all-targets -- -D warnings both
clean. Jira AZ-640 transitioned to In Progress at the start of this batch;
the matching In Testing transition follows this commit.
Co-authored-by: Cursor <cursoragent@cursor.com>
Bring this repo's .cursor/ in line with the suite monorepo root .cursor/
so rules, skills, and autodev artifacts stay consistent across
submodules and sibling repos.
Co-authored-by: Cursor <cursoragent@cursor.com>
Replaced HARBOR_USER and HARBOR_TOKEN with REGISTRY_USER and REGISTRY_TOKEN for improved clarity and consistency. Adjusted the Docker login command and updated the image tagging to reflect the new registry path. This change enhances the pipeline's flexibility for different environments.
Add HARBOR_USER/HARBOR_TOKEN from Woodpecker secrets and a docker login
step before the existing build/push, so pipelines can push to the new
TLS-authenticated Harbor registry.
Pipeline reads $REGISTRY_HOST from the Woodpecker global secret
'registry_host' instead of hardcoding 'localhost:5000'. The full
host:port lives in the secret, so Harbor migration (AZ-205) becomes
one secret edit rather than editing every submodule pipeline.
Made-with: Cursor
- Updated coding rules to emphasize readability, meaningful comments, and maintainability.
- Adjusted test coverage thresholds to 75% for business logic and clarified expectations for test scenarios.
- Enhanced guidelines for handling skipped tests, emphasizing the need for investigation and resolution.
- Introduced a completeness audit for decomposition in research steps to ensure thoroughness in addressing problem dimensions.
Made-with: Cursor
- Introduced a matrix for building on both linux/arm64 and linux/amd64 platforms.
- Updated image tags to include platform-specific identifiers for better versioning.
- Enhanced the labels section to dynamically set the platform label based on the build matrix.
- Added a guideline to place all source code under the `src/` directory in `coderule.mdc`.
- Removed the outdated guideline regarding the `src/` layout in `python.mdc` to streamline project structure.
These updates improve project organization and maintainability by clarifying the structure for source code and project files.
Added functionality to set camera ready for bringing it down or up.
Camera will be made available for AI after bringCameraDown command is given via UDPSocket.
Camera will be made unavailable for AI after bringCameraUp command is given via UDPSocket.
Tool has three inputs map_tile_folder, current_latitude and current_longitude.
With these tool will got through map tiles in given folder and list tiles in order based on their distance from given location.
- uses MAVSDK::MissionRaw objects for missions
- added new state AZ_DRONE_STATE_MISSION_UPLOADED
- new state is used in AzDroneControllerPlane before waiting for AUTO switch
TODO!!
- move to AzMissionController
- use JSON file instead of hard coded mission items
- removed QtSerialPort from the Qt CONFIG parameters
- remove compiler warnings
- reduced logging
- fixed FPS to show what AI really analyzed
- RTSP reader tries to connect to the stream once per second until it succeeds
- removed unnecessary logging
- print start date and time when application starts
- use std::cout instead of qDebug()
- better logging in DroneController classes
- renamed Controller states for better readability
When the application is started with the command parameter "plane", the
application uses the AzDroneControllerPlane class to control
initialisation. It doesn't arm or takeoff the drone. Instead, it waits
for the user to mode to AUTO (in Ardupilot, Mission in MAVSDK) with the
RC controller. When AUTO mode has been detected, the application will
start a normal mission handling.
Save bmp images of inference results to /tmp as bmp files. BMP was
chosen to reduce encoding time. Saving is fully threaded. It can be
enable with qmake CONFIG+=save_images option
Also:
- use antialised fonts in RKNN inference
- moved class strings to inference base class
- fixed silly segfault in ONNX inference
- prevent writing results if class if exceeds valid values
Issue: https://denyspopov.atlassian.net/browse/AZ-38
Type: Improvement
Oleksandr Bezdieniezhnykh