```html Seastead Open Source Software Recommendations

Open Source Software Recommendations for Seastead Operations

1. Navigation & Autopilot Systems

PyPilot (Highly Recommended)

Website: github.com/pypilot/pypilot

Description: Open source marine autopilot system designed for low-power consumption. Excellent choice for your differential thrust steering setup.

✓ Pros:
  • Modular Python architecture
  • Low power consumption (solar-friendly)
  • Active development community
  • Supports custom motor controllers
  • Works with standard marine instruments
⚠ Cons:
  • Will need customization for differential thrust
  • Primarily designed for rudder-based steering
  • Requires some Python knowledge
Note: PyPilot is designed to work with SignalK (see below). You'll need to modify the motor control module to handle dual thrusters instead of a rudder. The compass and heading control logic should work as-is.

OpenPlotter

Website: openplotter.readthedocs.io

Description: Complete navigation platform built on Raspberry Pi. Includes PyPilot integration and chart plotting.

✓ Pros:
  • All-in-one solution
  • Easy installation on Raspberry Pi
  • Includes weather routing
  • Integrates multiple tools
  • Good documentation
⚠ Cons:
  • Can be resource-intensive
  • More complex than needed for basic operations

SignalK

Website: signalk.org

Description: Modern data exchange format and server for marine systems. Acts as the "nervous system" connecting all instruments.

✓ Pros:
  • Standard protocol for marine data
  • Excellent for integrating multiple sensors
  • Web-based interface
  • Growing plugin ecosystem
  • Works with PyPilot
⚠ Cons:
  • Requires Node.js setup
  • Learning curve for configuration

2. Security Camera Systems

Frigate NVR (Highly Recommended)

Website: frigate.video

Description: Modern NVR with AI object detection optimized for low power systems. Uses Google Coral for efficient AI processing.

✓ Pros:
  • Excellent AI object detection (boats, people)
  • Low power with Coral accelerator
  • Modern web interface
  • MQTT integration for alerts
  • Works with standard IP cameras
  • Active development
⚠ Cons:
  • Requires Google Coral TPU for best performance
  • Docker-based (adds complexity)
Perfect for seasteads: Can detect approaching vessels, send alerts when people approach, and only record when motion is detected to save storage.

MotionEye

Website: github.com/motioneye-project/motioneye

Description: Simple, lightweight camera monitoring system with web interface.

✓ Pros:
  • Very lightweight
  • Simple setup
  • Good for Raspberry Pi
  • Motion detection built-in
  • Multiple camera support
⚠ Cons:
  • No AI object detection
  • Basic feature set
  • Development has slowed

ZoneMinder

Website: zoneminder.com

Description: Mature, full-featured video surveillance system.

✓ Pros:
  • Very mature and stable
  • Extensive features
  • Large user community
  • Good documentation
⚠ Cons:
  • Resource-heavy
  • Complex setup
  • Older interface
  • May be overkill for small systems

Shinobi

Website: shinobi.video

Description: Modern, Docker-friendly NVR with plugin support.

✓ Pros:
  • Modern interface
  • Plugin architecture
  • Good performance
  • Docker support
⚠ Cons:
  • Smaller community than alternatives
  • Can be resource-intensive

3. Additional Useful Software

OpenCPN

Website: opencpn.org

Description: Chart plotter and navigation software. Essential for planning routes.

Why you need it: Plan your route, avoid shipping lanes, track position, and integrate with weather data.

Node-RED

Website: nodered.org

Description: Flow-based programming for IoT and automation.

Why you need it: Create automation rules like "if wind speed > X, send alert" or "if approaching shallow water, slow down." Perfect for integrating all your systems.

Grafana + InfluxDB

Website: grafana.com | influxdata.com

Description: Time-series database and visualization dashboard.

Why you need it: Monitor solar power generation, battery levels, GPS track, speed, motor current, water temperature, etc. All in beautiful graphs.

Home Assistant

Website: home-assistant.io

Description: Home automation platform.

Why you need it: Control lighting, monitor environmental sensors, integrate security cameras, and create automation routines. Works great on Raspberry Pi.

4. Recommended System Architecture

Hardware Recommendation

  • Main Controller: Raspberry Pi 4 (4GB or 8GB) - runs PyPilot, SignalK, Node-RED
  • Camera/Security System: Separate Raspberry Pi 4 (4GB) with Google Coral USB accelerator - runs Frigate
  • Backup System: Additional Raspberry Pi for redundancy
  • Motor Controllers: Arduino or ESP32 for thruster control (interfaced with PyPilot)
  • Network: Ethernet switch connecting all systems

Software Stack

Navigation & Control (Pi #1):

  • Raspberry Pi OS Lite
  • SignalK Server (data hub)
  • PyPilot (autopilot with custom differential thrust)
  • OpenCPN (chart plotting)
  • Node-RED (automation)
  • InfluxDB (data logging)
  • Grafana (monitoring dashboards)

Security & Monitoring (Pi #2):

  • Raspberry Pi OS Lite
  • Frigate NVR (camera system)
  • MQTT broker (for alerts)
Power Consumption Note: This setup should run on approximately 20-35W total, very manageable with solar power. The dual-Pi setup provides redundancy and prevents camera processing from interfering with navigation.

5. Custom Development Needed

Differential Thrust Steering Module

You'll need to create a Python module that:

  • Takes heading commands from PyPilot
  • Calculates differential thrust needed (left vs right thruster speeds)
  • Sends commands to motor controllers via serial/I2C/GPIO
  • Monitors motor current and RPM feedback
  • Handles eddy current optimization (reduce power when currents are favorable)
Tip: This is approximately 200-400 lines of Python. The PyPilot community is helpful and the codebase is well-structured for this type of modification.

Efficiency Optimizer

Given your 1 MPH speed goal and eddy current usage:

  • Monitor GPS speed and power consumption
  • Detect when currents are favorable (faster speed with less power)
  • Automatically reduce motor power when possible
  • Log efficiency data for route planning

This could be built in Node-RED or as a Python script.

6. Getting Started Recommendations

Phase 1: Basic Navigation (Weeks 1-2)

  1. Install Raspberry Pi OS on your Pi
  2. Install SignalK Server
  3. Connect GPS via USB or serial
  4. Install OpenCPN and connect to SignalK
  5. Verify you can see position on charts

Phase 2: Motor Control (Weeks 3-4)

  1. Set up motor controllers with Arduino/ESP32
  2. Create basic differential thrust test script
  3. Test steering in both directions
  4. Implement emergency stop functionality

Phase 3: Autopilot (Weeks 5-6)

  1. Install PyPilot
  2. Modify for differential thrust
  3. Calibrate compass
  4. Test heading hold in calm conditions
  5. Tune PID parameters

Phase 4: Security Cameras (Weeks 7-8)

  1. Set up second Raspberry Pi
  2. Install Frigate NVR
  3. Configure IP cameras
  4. Set up object detection zones
  5. Configure alerts via MQTT to main system

Phase 5: Monitoring & Automation (Weeks 9-10)

  1. Install Node-RED
  2. Set up InfluxDB and Grafana
  3. Create monitoring dashboards
  4. Build automation flows
  5. Test full system integration

Final Recommendation Summary

For Autopilot: PyPilot + SignalK (with custom differential thrust module)

For Security Cameras: Frigate NVR (best AI detection, low power)

For Integration: Node-RED + MQTT

For Monitoring: Grafana + InfluxDB

For Charts: OpenCPN

Total Cost: $0 for software (all open source), ~$500-800 for hardware (2x Raspberry Pi 4, Google Coral, GPS, compass, cameras, motor controllers)

Questions or Need More Details?

This is a fascinating project! If you need more specific guidance on any of these components, or want help with the custom differential thrust steering code, feel free to ask. The seastead design with angled columns is unique and should handle wave action well.

Good luck with your build! 🌊

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