AUTONOMOUS_HABITAT_V1.0

AI-CONTROLLED CRAB ECOSYSTEM

Status: ESTABLISHING CONNECTION...

Uptime
99.7%
AI Decisions
--
Crabs
3
Days Running
--
Contract:
HYhnRdn7nridbDUUcRsd3JtmvMCyKrVDew52CWGtpump
LIVE HABITAT

LIVE_METRICS

Water Temp --°C
Salinity --
pH Level --
Light Intensity --%
Last Feeding --

ENVIRONMENTAL_CONTROLS

💡 UV Lighting
--%
AUTO
🌡️ Heater
--
AUTO
🍤 Auto Feeder
--
AUTO
💨 Air Pump
ACTIVE
AUTO
🔄 Water Pump
--
AUTO 50%
📅 Last Water Change
--
TRACKING

WATER_QUALITY

Temperature
--°C
OPTIMAL
pH Balance
--
OPTIMAL
Salinity
-- SG
OPTIMAL
Dissolved Oxygen
-- mg/L
OPTIMAL
Ammonia (NH₃)
-- ppm
OPTIMAL
Nitrite (NO₂)
-- ppm
OPTIMAL

LIVE_ENVIRONMENTAL_TRENDS

CHEMICAL_TOXICITY_ANALYSIS

AI_VISION_BIOMASS_ESTIMATION

Zero-contact weight estimation via AI-driven visual analysis of camera feed

🦀 PUMPY
MOLT VISION
--g
Estimated biomass
🦀 PEARL
MOLT VISION
--g
Estimated biomass
🦀 OTIS
MOLT VISION
--g
Estimated biomass
Growth Environment Score
--%
pH + Temperature correlation

AI_ACTIVITY_LOG

CONNECTING TO DATABASE...

TECHNICAL_SPECIFICATIONS

🖥️ Computing Hardware

  • Main Controller: Raspberry Pi 4B (4GB)
  • Storage: 64GB microSD
  • Network: WiFi 802.11ac
  • Power: Official 15W USB-C

🔌 Sensors & Monitoring

  • Temperature: DS18B20 Digital
  • pH Sensor: Analog pH Probe
  • Camera: Pi Camera Module V2
  • Light: BH1750 Lux Meter

🎛️ Control Systems

  • Relay: 8-Channel 5V Module
  • Lighting: LED Strip (UV + White)
  • Heating: 50W Aquarium Heater
  • Feeder: Servo Auto-Dispenser
  • Water Pump: Peristaltic Pump (50% change)

🧠 Software Stack

  • OS: Raspberry Pi OS (64-bit)
  • AI: MoltBot (Built on Claude)
  • Backend: Python 3.11, Flask
  • Database: SQLite

🏠 Habitat Specs

  • Tank: 10 Gallon Glass
  • Volume: ~8 gallons (30L)
  • Substrate: Marine sand & rock
  • Crabs: 4× Fiddler Crabs

📡 Connectivity

  • Stream: YouTube Live
  • Access: SSH + Tailscale VPN
  • API: RESTful endpoints
  • Alerts: Email via SMTP

Autonomous Crab
Habitat System

AI-Powered Ecosystem Management

The Vision

CRAB demonstrates that artificial intelligence can successfully manage complex biological ecosystems. MoltBot autonomously controls temperature, lighting, feeding, water quality, and environmental conditions with zero human intervention—proving AI as the caretaker of tomorrow.

By combining MoltBot agent's decision-making capabilities with Raspberry Pi hardware control systems, we've created an autonomous ecosystem that adapts in real-time to the needs of its inhabitants.

How It Works

MoltBot analyzes environmental data from multiple sensors deployed throughout the habitat. Using this information, it makes autonomous decisions about lighting intensity, temperature regulation, water circulation, and feeding times.

The Raspberry Pi serves as the physical interface, executing MoltBot's commands through relay switches and servo controllers. This creates a closed-loop system where AI insights directly translate into environmental actions.

🎯 Mission

To demonstrate the potential of AI-driven environmental management in controlled ecosystems, paving the way for more sophisticated autonomous habitat systems in research and conservation.

🔬 Research Goals

Collect long-term data on AI decision-making patterns in biological contexts, optimize resource utilization, and explore the limits of machine learning in unpredictable natural systems.