sl-mechanical
fabfd5e974
Implement context-aware text-to-speech with emotion-driven expression for SaltyBot. Features: ✓ Context-aware greetings (time of day, person names, emotion) ✓ Priority queue management (safety > social > idle) ✓ Emotion-based rate/pitch modulation (happy: faster+higher, sad: slower+lower) ✓ Integration with emotion engine (Issue #429) and TTS service (Issue #421) ✓ Configurable personality parameters ✓ Person recognition for personalized responses ✓ Queue management with 16-item buffer Architecture: Node: tts_personality_node - Subscribes: /saltybot/tts_request, /saltybot/emotion_state, /saltybot/person_detected - Publishes: /saltybot/tts_command (formatted for TTS service), /saltybot/personality_state - Runs worker thread for asynchronous queue processing Personality Parameters: - Name: "Luna" (default, configurable) - Speed modulation: happy=1.1x, sad=0.9x, neutral=1.0x - Pitch modulation: happy=1.15x, sad=0.85x, neutral=1.0x - Time-based greetings for 4 periods (morning, afternoon, evening, night) - Known people mapping for personalization Queue Priority Levels: - SAFETY (3): Emergency/safety messages - SOCIAL (2): Greetings and interactions - IDLE (1): Commentary and chatter - NORMAL (0): Default messages Files Created: - saltybot_tts_personality package with main personality node - config/tts_personality_params.yaml with configurable parameters - launch/tts_personality.launch.py for easy startup - Unit tests for personality context and emotion handling - Comprehensive README with usage examples Integration Points: - Emotion engine (Issue #429): Listens to emotion updates - TTS service (Issue #421): Publishes formatted commands - Jabra SPEAK 810: Output audio device - Person tracking: Uses detected person names Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
Jetson Nano — AI/SLAM Platform Setup
Self-balancing robot: Jetson Nano dev environment for ROS2 Humble + SLAM stack.
Stack
| Component | Version / Part |
|---|---|
| Platform | Jetson Nano 4GB |
| JetPack | 4.6 (L4T R32.6.1, CUDA 10.2) |
| ROS2 | Humble Hawksbill |
| DDS | CycloneDDS |
| SLAM | slam_toolbox |
| Nav | Nav2 |
| Depth camera | Intel RealSense D435i |
| LiDAR | RPLIDAR A1M8 |
| MCU bridge | STM32F722 (USB CDC @ 921600) |
Quick Start
# 1. Host setup (once, on fresh JetPack 4.6)
sudo bash scripts/setup-jetson.sh
# 2. Build Docker image
bash scripts/build-and-run.sh build
# 3. Start full stack
bash scripts/build-and-run.sh up
# 4. Open ROS2 shell
bash scripts/build-and-run.sh shell
Docs
docs/pinout.md— GPIO/I2C/UART pinout for all peripheralsdocs/power-budget.md— 10W power envelope analysis
Files
jetson/
├── Dockerfile # L4T base + ROS2 Humble + SLAM packages
├── docker-compose.yml # Multi-service stack (ROS2, RPLIDAR, D435i, STM32)
├── README.md # This file
├── docs/
│ ├── pinout.md # GPIO/I2C/UART pinout reference
│ └── power-budget.md # Power budget analysis (10W envelope)
└── scripts/
├── entrypoint.sh # Docker container entrypoint
├── setup-jetson.sh # Host setup (udev, Docker, nvpmodel)
└── build-and-run.sh # Build/run helper
Power Budget (Summary)
| Scenario | Total |
|---|---|
| Idle | 2.9W |
| Nominal (SLAM active) | ~10.2W |
| Peak | 15.4W |
Target: 10W (MAXN nvpmodel). Use RPLIDAR standby + 640p D435i for compliance.
See docs/power-budget.md for full analysis.