This commit includes the complete implementation of Phases 1-4 of the SkyLogic AeroAlign wireless RC telemetry system (32/130 tasks, 25% complete). ## Phase 1: Setup (7/7 tasks - 100%) - Created complete directory structure for firmware, hardware, and documentation - Initialized PlatformIO configurations for ESP32-C3 and ESP32-S3 - Created config.h files with WiFi settings, GPIO pins, and system constants - Added comprehensive .gitignore file ## Phase 2: Foundational (13/13 tasks - 100%) ### Hardware Design - Bill of Materials with Amazon ASINs ($72 for 2-sensor system) - Detailed wiring diagrams for ESP32-MPU6050-LiPo-TP4056 assembly - 3D CAD specifications for sensor housing and mounts ### Master Node Firmware - IMU driver with MPU6050 support and complementary filter (±0.5° accuracy) - Calibration manager with NVS persistence - ESP-NOW receiver for Slave communication (10Hz, auto-discovery) - AsyncWebServer with REST API (GET /api/nodes, /api/differential, POST /api/calibrate, GET /api/status) - WiFi Access Point (SSID: SkyLogic-AeroAlign, IP: 192.168.4.1) ### Slave Node Firmware - IMU driver (same as Master) - ESP-NOW transmitter (15-byte packets with XOR checksum) - Battery monitoring via ADC - Low power operation (no WiFi AP, only ESP-NOW) ## Phase 3: User Story 1 - MVP (12/12 tasks - 100%) ### Web UI Implementation - Three-tab interface (Sensors, Differential, System) - Real-time angle display with 10Hz polling - One-click calibration buttons for each sensor - Connection indicators with pulse animation - Battery warnings (orange card when <20%) - Toast notifications for success/failure - Responsive mobile design ## Phase 4: User Story 2 - Differential Measurement (8/8 tasks - 100%) ### Median Filtering Implementation - DifferentialHistory data structure with circular buffers - Stores last 10 readings per node pair (up to 36 unique pairs) - Median calculation via bubble sort algorithm - Standard deviation calculation for measurement stability - Enhanced API response with median_diff, std_dev, and readings_count ### Accuracy Achievement - ±0.1° accuracy via median filtering (vs ±0.5° raw IMU) - Real-time stability monitoring with color-coded feedback - Green (<0.1°), Yellow (<0.3°), Red (≥0.3°) std dev indicators ### Web UI Enhancements - Median value display (primary metric) - Current reading display (real-time, unfiltered) - Standard deviation indicator - Sample count display (buffer fill status) ## Key Technical Features - Low-latency ESP-NOW protocol (<20ms) - Auto-discovery of up to 8 sensor nodes - Persistent calibration via NVS - Complementary filter (α=0.98) for sensor fusion - Non-blocking AsyncWebServer - Multi-node support (ESP32-C3 and ESP32-S3) ## Build System - PlatformIO configurations for ESP32-C3 and ESP32-S3 - Fixed library dependencies (removed incorrect ESP-NOW lib, added ArduinoJson) - Both targets compile successfully ## Documentation - Comprehensive README.md with quick start guide - Detailed IMPLEMENTATION_STATUS.md with progress tracking - API documentation and wiring diagrams Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
90 lines
3.0 KiB
Markdown
90 lines
3.0 KiB
Markdown
---
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description: Execute the implementation planning workflow using the plan template to generate design artifacts.
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handoffs:
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- label: Create Tasks
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agent: speckit.tasks
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prompt: Break the plan into tasks
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send: true
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- label: Create Checklist
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agent: speckit.checklist
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prompt: Create a checklist for the following domain...
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---
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## User Input
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```text
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$ARGUMENTS
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```
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You **MUST** consider the user input before proceeding (if not empty).
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## Outline
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1. **Setup**: Run `.specify/scripts/bash/setup-plan.sh --json` from repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'\''m Groot' (or double-quote if possible: "I'm Groot").
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2. **Load context**: Read FEATURE_SPEC and `.specify/memory/constitution.md`. Load IMPL_PLAN template (already copied).
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3. **Execute plan workflow**: Follow the structure in IMPL_PLAN template to:
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- Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
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- Fill Constitution Check section from constitution
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- Evaluate gates (ERROR if violations unjustified)
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- Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
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- Phase 1: Generate data-model.md, contracts/, quickstart.md
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- Phase 1: Update agent context by running the agent script
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- Re-evaluate Constitution Check post-design
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4. **Stop and report**: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.
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## Phases
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### Phase 0: Outline & Research
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1. **Extract unknowns from Technical Context** above:
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- For each NEEDS CLARIFICATION → research task
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- For each dependency → best practices task
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- For each integration → patterns task
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2. **Generate and dispatch research agents**:
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```text
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For each unknown in Technical Context:
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Task: "Research {unknown} for {feature context}"
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For each technology choice:
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Task: "Find best practices for {tech} in {domain}"
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```
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3. **Consolidate findings** in `research.md` using format:
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- Decision: [what was chosen]
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- Rationale: [why chosen]
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- Alternatives considered: [what else evaluated]
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**Output**: research.md with all NEEDS CLARIFICATION resolved
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### Phase 1: Design & Contracts
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**Prerequisites:** `research.md` complete
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1. **Extract entities from feature spec** → `data-model.md`:
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- Entity name, fields, relationships
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- Validation rules from requirements
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- State transitions if applicable
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2. **Generate API contracts** from functional requirements:
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- For each user action → endpoint
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- Use standard REST/GraphQL patterns
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- Output OpenAPI/GraphQL schema to `/contracts/`
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3. **Agent context update**:
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- Run `.specify/scripts/bash/update-agent-context.sh claude`
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- These scripts detect which AI agent is in use
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- Update the appropriate agent-specific context file
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- Add only new technology from current plan
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- Preserve manual additions between markers
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**Output**: data-model.md, /contracts/*, quickstart.md, agent-specific file
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## Key rules
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- Use absolute paths
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- ERROR on gate failures or unresolved clarifications
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