sl-controls
fd7eddd44d
Implements PID auto-tuning ROS2 node using relay feedback (Astrom-Hagglund) method. Service-triggered relay oscillation measures ultimate gain (Ku) and ultimate period (Tu), then computes Ziegler-Nichols PD gains. Safety abort on >25deg tilt. Features: - Service /saltybot/autotune_pid (std_srvs/Trigger) starts tuning - Relay oscillation method for accurate gain measurement - Measures Ku (ultimate gain) and Tu (ultimate period) - Computes Z-N PD gains: Kp=0.6*Ku, Kd=0.075*Ku*Tu - Real-time safety abort >25° tilt angle - JSON telemetry on /saltybot/autotune_info - Relay commands on /saltybot/autotune_cmd_vel Tuning Process: 1. Settle phase: zero command, allow oscillations to die 2. Relay oscillation: apply +/-relay_magnitude commands 3. Measure peaks: detect zero crossings, record extrema 4. Analysis: calculate Ku from peak amplitude, Tu from period 5. Gain computation: Ziegler-Nichols formulas 6. Publish results: Ku, Tu, Kp, Kd Safety Features: - IMU tilt monitoring (abort >25°) - Max tuning duration timeout - Configurable settle time and oscillation cycles Published Topics: - /saltybot/autotune_info (std_msgs/String) - JSON with Ku, Tu, Kp, Kd - /saltybot/autotune_cmd_vel (geometry_msgs/Twist) - Relay control Subscribed Topics: - /imu/data (sensor_msgs/Imu) - IMU tilt safety check - /saltybot/balance_feedback (std_msgs/Float32) - Balance feedback Package: saltybot_pid_autotune Entry point: pid_autotune_node Service: /saltybot/autotune_pid Tests: 20+ unit tests covering: - IMU tilt extraction - Relay oscillation analysis - Ku/Tu measurement - Ziegler-Nichols gain computation - Peak detection and averaging - Safety limits (tilt, timeout) - State machine transitions - JSON telemetry format 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.