[AZ-626] Decompose complete: 47 tasks + docs + module layout

Greenfield Steps 1-6 baseline for the autopilot rewrite from legacy
Qt/C++ to a Rust workspace.

- Remove legacy Qt/C++ tree (ai_controller, drone_controller,
  misc/camera, python_scaffold, root Dockerfile, autopilot.pro,
  legacy main.py / requirements.txt).
- Add _docs/00_problem (problem, restrictions, acceptance criteria,
  security approach, input data + fixtures).
- Add _docs/01_solution/solution_draft01.
- Add _docs/02_document (architecture, system-flows, data_model,
  glossary, decision-rationale, deployment, 13 component descriptions,
  tests/ specs, FINAL_report, module-layout).
- Add _docs/02_tasks/todo with 47 task specs (AZ-640..AZ-686, one
  bootstrap + 46 component tasks) and _dependencies_table.md.
- Add .cursor/rules/artifact-srp.mdc (single-responsibility rule for
  canonical _docs artifacts).
- Track autodev state in _docs/_autodev_state.md (Step 6 completed,
  ready for Step 7 Implement).

Jira: bootstrap AZ-626; component epics AZ-627..AZ-639; tasks
AZ-640..AZ-686. Total complexity 173 points across 12 epics.

Co-authored-by: Cursor <cursoragent@cursor.com>

_docs/02_document/components/semantic_analyzer/description.md

@@ -0,0 +1,70 @@
+# Component — `semantic_analyzer`
+
+**Layer**: Perception (data plane in)
+**Status**: forward-looking design (Rust + ONNX/TensorRT bindings)
+
+## 1. Purpose
+
+Tier 2 of the perception pipeline. Reasons over zoom-in crops using a primitive graph plus a lightweight ROI CNN. Active only when `scan_controller` is in `ZoomedIn`. Owns path-freshness scoring, endpoint scoring, branch choice at intersections, and concealment-POI scoring. Operates on bounded ROIs only — never full frames.
+
+## 2. Inputs
+
+| Input | Source | Cadence | Notes |
+|---|---|---|---|
+| `DetectionBatch` (Tier 1 primitives) | `detection_client` | per zoom-in frame | Used for primitive-graph construction (paths, branches, entrances, trees). |
+| Zoom-in frame + ROI selection | `frame_ingest` (frame), `scan_controller` (ROI bounds) | per zoom-in hold | Bounded crop only; full frame is not consumed. |
+| Per-class config | startup config | once | Confidence floors, freshness thresholds, branch-priority rules. |
+
+## 3. Outputs
+
+| Output | Consumer | Shape |
+|---|---|---|
+| `Tier2Evidence` | `scan_controller` | `{ roi_id, path_freshness, endpoint_score, concealment_score, recommended_next_action: PanFollowFootpath \| HoldEndpoint \| PanBroad \| ReturnToZoomOut, source_detections: Vec<DetectionId> }` |
+| `Pan plan` | `scan_controller` (then `gimbal_controller`) | sequence of pan goals for footpath following |
+| Health metric | health aggregator | `tier2_latency_p50/p99`, `roi_size_bytes_p99`, `errors_total`. |
+
+## 4. Key Responsibilities
+
+- Build a small primitive graph from Tier-1 detections inside the ROI: path nodes (footpaths, roads), endpoint nodes (branch piles, dark entrances, dugouts), context nodes (trees, tree blocks).
+- Score path freshness using the freshness model (texture, edge clarity, undisturbed-surroundings cues).
+- Score concealment for endpoint candidates.
+- At intersections, recommend the freshest / most-promising branch for `gimbal_controller` to pan toward; emit a follow plan that keeps the path centered while the UAV moves.
+- Bound every inference call by a strict ROI size and timeout. Never run on a full frame.
+
+## 5. Internal State
+
+- ROI-scoped primitive graphs (per-ROI lifetime; dropped on zoom-in exit).
+- Lightweight CNN session (ONNX/TensorRT engine).
+
+State is in-process only.
+
+## 6. Failure Modes
+
+| Failure | Detection | Behaviour |
+|---|---|---|
+| ROI size exceeds limit | pre-decode size check | Reject the ROI; surface to `scan_controller` as `tier2_oversize`; do not decode. |
+| Inference timeout (>200 ms) | wall-clock | Return `Tier2Evidence` with `status: timeout`; `scan_controller` decides to skip VLM and surface a low-evidence POI. |
+| CNN session OOM or hardware error | inference call error | Health → red on sustained errors; `scan_controller` falls back to Tier-1-only POI surfacing. |
+| Inconsistent primitive graph (e.g., disconnected paths) | graph validation step | Emit `Tier2Evidence` with `recommended_next_action: ReturnToZoomOut` and `path_freshness: undefined`. |
+
+## 7. Dependencies
+
+**In-process**: `detection_client`, `frame_ingest`, `scan_controller`.
+
+**External**: ONNX Runtime / TensorRT (whichever the lightweight CNN ships with), OpenCV (preprocessing).
+
+## 8. Non-Functional Targets
+
+| Concern | Target |
+|---|---|
+| Per-ROI latency | ≤200 ms p99 |
+| Concealed-position recall | ≥60 % |
+| Concealed-position precision | ≥20 % (operators filter) |
+| Footpath detection recall | ≥70 % |
+| ROI memory footprint | bounded; no unbounded buffering |
+
+## 9. References
+
+- `architecture.md §3`, `§7.6 Tier 2 semantic analyzer`, `§7.5 Training Data`.
+- `system-flows.md §F1 Frame pipeline`, `§F4 Scan controller behaviour tree`.
+- `data_model.md §Tier2Evidence`.