sl-webui
f71dad5344
Architecture change (2026-04-03): Mamba F722S (STM32F722) and BlackPill replaced by ESP32 BALANCE (PID loop) and ESP32 IO (motors/sensors/comms). - Update CLAUDE.md, docs, chassis BOM/ASSEMBLY, pinout, power-budget, wiring-diagram, TEAM.md, AUTONOMOUS_ARMING.md, docker-compose - Update all ROS2 package comments, config labels, launch args (stm32_port→esp32_port, /dev/stm32-bridge→/dev/esp32-bridge) - Update WebUI: stm32Mode→esp32Mode, stm32Version→esp32Version, "STM32 State/Mode" labels → "ESP32 State/Mode" (ControlMode, SettingsPanel) - Add TODO(esp32-migration) markers on stm32_protocol.py and mamba_protocol.py binary frame layouts — pending ESP32 protocol spec from max - Fix roslib CDN 1.3.0→1.4.0 in all 11 HTML panels (fixes ROS2 Humble rosbridge "Received a message without an op" incompatibility) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
201 lines
6.0 KiB
Markdown
201 lines
6.0 KiB
Markdown
# Jetson Orin Nano Super Power Budget Analysis
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## Self-Balancing Robot — 25W Envelope
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Last updated: 2026-02-28
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Target: Operate within 25W SoC power envelope (MAXN 25W mode)
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---
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## Power Modes
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Jetson Orin Nano Super supports multiple NVPModel power modes:
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| Mode | ID | CPU | GPU | TDP |
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|------|-----|-----|-----|-----|
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| **MAXN** | 0 | 6× A78AE @ 1.5GHz | 1024-core Ampere | **25W** |
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| 15W | 1 | 6× A78AE @ 1.2GHz | 1024-core Ampere | 15W |
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| 10W | 2 | 4× A78AE @ 1.2GHz | 1024-core Ampere | 10W |
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| 7W | 3 | 4× A78AE @ 0.8GHz | 1024-core Ampere | 7W |
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For this robot, we target **MAXN 25W mode** — a significant upgrade from the previous Nano 10W budget.
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```bash
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# Check current mode
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sudo nvpmodel -q
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# Set 25W MAXN mode
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sudo nvpmodel -m 0
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# Set 15W mode (thermal / battery save)
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sudo nvpmodel -m 1
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# Monitor power in real time
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sudo tegrastats
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# or via jtop
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sudo jtop
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```
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---
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## Component Power Budget
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### SoC (Jetson Orin Nano Super Module)
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| Component | Idle (W) | Load (W) | Peak (W) | Notes |
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|-----------|----------|----------|----------|-------|
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| CPU (6× A78AE) | 1.5 | 6.0 | 8.0 | ROS2, SLAM, Nav2 |
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| GPU (1024-core Ampere) | 0.8 | 5.0 | 7.0 | Depth processing, DNN inference |
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| LPDDR5 RAM (8GB) | 0.4 | 0.8 | 1.0 | |
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| NVMe SSD (M.2) | 0.2 | 0.5 | 0.8 | Map storage, rosbags |
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| Video encoder / ISP | 0.0 | 1.5 | 2.5 | 4× IMX219 ISP processing |
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| **SoC Subtotal** | **2.9** | **13.8** | **19.3** | |
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### Peripherals
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| Peripheral | Idle (W) | Active (W) | Peak (W) | Interface | Notes |
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|-----------|----------|------------|----------|-----------|-------|
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| RealSense D435i | 0.3 | 1.5 | 3.5 | USB 3.1 | Peak during boot/init |
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| RPLIDAR A1M8 | 0.4 | 2.6 | 3.0 | USB (UART adapter) | Motor spinning |
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| ESP32 bridge | 0.0 | 0.0 | 0.0 | USB CDC | Self-powered from robot 5V |
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| 4× IMX219 cameras | 0.2 | 2.0 | 2.4 | MIPI CSI-2 | ~0.5W per camera active |
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| **Peripheral Subtotal** | **0.9** | **6.1** | **8.9** | | |
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### Total System (from Jetson 5V barrel jack)
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| Scenario | SoC (W) | Peripherals (W) | **Total (W)** | Margin vs 25W |
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|----------|---------|-----------------|---------------|----------------|
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| Idle | 2.9 | 0.9 | **3.8** | +21.2W |
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| Nominal (SLAM + cameras) | 13.8 | 6.1 | **19.9** | **+5.1W ✅** |
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| Peak (DNN + all sensors) | 19.3 | 8.9 | **28.2** | **-3.2W ⚠️** |
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---
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## Budget Analysis vs Previous Platform
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| Metric | Jetson Nano | Jetson Orin Nano Super |
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|--------|------------|------------------------|
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| TDP | 10W | 25W |
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| CPU | 4× Cortex-A57 @ 1.43GHz | 6× A78AE @ 1.5GHz |
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| GPU | 128-core Maxwell | 1024-core Ampere |
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| RAM | 4GB LPDDR4 | 8GB LPDDR5 |
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| AI TOPS | ~0.5 | 67 |
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| Nominal load | 11.4W (over budget) | 19.9W (5W headroom) |
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| Cameras | 0 CSI | 4× IMX219 CSI |
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| Storage | microSD | NVMe M.2 |
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**The Orin Nano Super has 2.5× more thermal headroom at nominal load.** No aggressive power-gating needed for normal operation.
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---
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## Power Compliance Strategy
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### Nominal Operation (SLAM + cameras) — ✅ Within 25W
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At 19.9W nominal, we have 5W headroom. No mitigation required for normal robot operation.
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### Peak Operation (DNN inference) — ⚠️ Briefly exceeds 25W
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When running DNN inference (e.g., object detection) simultaneously with full sensor suite:
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**Mitigation 1: Thermal throttling (automatic)**
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The Orin's DVFS will automatically throttle CPU/GPU when temperature exceeds threshold.
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No explicit action needed — the Orin handles this gracefully.
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**Mitigation 2: Switch to 15W mode during high-load phases**
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```bash
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sudo nvpmodel -m 1 # 15W mode: reduces peak to ~22W
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sudo nvpmodel -m 0 # return to MAXN when cooling
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```
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**Mitigation 3: RPLIDAR motor gating**
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Stop RPLIDAR motor between scan cycles: saves ~2.2W average.
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Handled automatically by `rplidar_ros` driver via DTR line control.
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**Mitigation 4: Camera resolution reduction**
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For compute-heavy phases, drop from 640×480 to 424×240 per camera: saves ~0.6W.
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---
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## CSI Camera Bandwidth
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4× IMX219 cameras at 640×480@30fps:
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| Parameter | Value |
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|-----------|-------|
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| Per-camera raw bandwidth | 640×480×30×10bpp = 92.16 Mb/s |
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| Total 4 cameras | ~369 Mb/s |
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| MIPI CSI-2 capacity (Orin) | 40 Gb/s total (2× 4-lane) |
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| ISP processing overhead | ~1.5W (all 4 cameras active) |
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**CSI bandwidth is well within capacity.** The Orin Nano Super's ISP handles 4 cameras simultaneously.
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---
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## Input Power Requirements
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### Jetson Orin Nano Super Power Input
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| Spec | Value |
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|------|-------|
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| Input connector | 5.5mm / 2.5mm barrel jack |
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| Input voltage | 5V DC |
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| Recommended current | ≥6A (30W supply for headroom) |
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| Absolute max | 5.25V |
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> **Use a 5V 6A supply minimum.** A 4A supply may brownout under DNN peak load.
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### Robot Power Architecture (Recommended)
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```
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LiPo 4S (16.8V max)
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│
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├─► DC-DC Buck → 5V 6A ──► Jetson Orin barrel jack (30W)
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│ (e.g., XL4016E1)
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│
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├─► DC-DC Buck → 5V 3A ──► ESP32 + logic 5V rail
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│
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└─► Hoverboard ESC ──► Hub motors (48V loop)
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```
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Using a 4S LiPo (vs 3S previously) gives better efficiency for the 5V buck converter
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at Orin's higher power draw.
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---
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## Real-Time Monitoring
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```bash
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# Live power telemetry (tegrastats)
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sudo tegrastats --interval 500
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# Key fields:
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# POM_5V_IN X/Y — total input power (current mW / average mW)
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# POM_5V_GPU X/Y — GPU power
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# POM_5V_CPU X/Y — CPU power
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# Interactive monitoring (jtop — recommended)
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sudo pip3 install jetson-stats
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sudo jtop
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# Log power to file
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sudo tegrastats --interval 1000 --logfile /tmp/power_log.txt &
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# Parse log
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grep "POM_5V_IN" /tmp/power_log.txt | \
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awk '{for(i=1;i<=NF;i++) if($i=="POM_5V_IN") print $(i+1)}' | \
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awk -F'/' '{sum+=$1; count++} END {print "Avg:", sum/count/1000, "W"}'
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```
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---
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## Summary
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| Metric | Value |
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|--------|-------|
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| Target envelope | 25W (MAXN) |
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| Nominal (SLAM + 4 cameras) | ~19.9W |
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| Peak (DNN inference) | ~28.2W (briefly) |
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| Compliant scenario | All sensors + SLAM (no DNN) |
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| Recommended PSU | 5V 6A (30W) |
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| Power mode | nvpmodel MAXN (Mode 0) |
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| Upgrade from Nano | +150% TDP, +13,300% AI TOPS |
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