SRRS — Self-Replicating Robot System

Simulation and Reliability, Availability, Maintainability (RAM) analysis of self-replicating robotic systems across 6 configurations.

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Overview

SRRS models robot colonies that can collect materials, print components, and assemble new robots. Six configurations are studied, varying replication strategy (Homogeneous vs Heterogeneous) and robot mix (One type vs External specialists):

Config	Description
CHO	Collect-Homogeneous-One — single replicator builds Normal workers
DHO	Deterministic-Homogeneous-One — replicator clones itself
HHO	Heterogeneous-Homogeneous-One — replicator alternates Normal/Replicator
CHE	Collect-Heterogeneous-External — Assembler + Printer team builds Normal workers
DHE	Deterministic-Heterogeneous-External — Assembler + Printer alternate clones
HHE	Heterogeneous-Heterogeneous-External — rotating 3-type build cycle

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Package Structure

python/
├── models/
│   ├── types.py        ← RobotType, TaskType, Config, SimMode enums
│   ├── robot.py        ← Robot base class + Replicator/Normal/Assembler/Printer
│   └── resources.py    ← Simulation resource state (materials, env_materials, …)
├── simulation/
│   ├── config.py       ← SimulationParams (all tunable parameters)
│   └── runner.py       ← Core simulation loop (deterministic + MC)
├── analysis/
│   ├── ram.py          ← System-level MTBF / MTTR / MDT / Aoss
│   └── stats.py        ← MC aggregation + confidence intervals
├── visualization/
│   └── plots.py        ← Plotly HTML output (6-panel summary + MC distributions)
└── main.py             ← CLI entrypoint

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Installation

# From source
pip install -e ".[dev]"

# From built wheel
pip install dist/srrs-*.whl

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Usage

CLI

# Deterministic — single config, 100 timesteps
python -m python.main --config CHO --mode D --timesteps 100

# Deterministic — with Plotly HTML output
python -m python.main --config CHO --mode D --timesteps 250 --plot --output-dir output/

# Monte Carlo — 6 configs, 500 runs each
python -m python.main --config CHO DHO HHO CHE DHE HHE --mode MC --mc-runs 500 --timesteps 250 --plot

# All options
python -m python.main --help

Python API

from python.models.types import Config, SimMode
from python.models.resources import Resources
from python.simulation.config import SimulationParams
from python.simulation.runner import run_simulation

params = SimulationParams(
    config=Config.CHO,
    mode=SimMode.DETERMINISTIC,
    timesteps=100,
    quality_threshold=0.5,
    risk_threshold=3.0,
)

df, ram = run_simulation(params)
print(f"Final robots: {df['#In'].iloc[-1]}")
print(f"System Aoss: {ram.aoss:.4f}")

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Testing

# Run all tests
pytest

# Run tests for a specific config with coverage
pytest tests/test_CHO.py -v --cov=python --cov-report=term-missing

# Run all configs
pytest tests/ -v --cov=python --cov-report=term-missing

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CI

GitHub Actions runs on every push and PR:

1. build — installs build, runs python -m build --wheel, uploads the .whl as an artifact
2. test / CHO … HHE — 6 parallel jobs, each downloads the wheel, installs it, and runs pytest tests/test_<CONFIG>.py with coverage

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RAM Metrics

Metric	Description
MTBF	Mean Time Between Failures
MTTR	Mean Time To Repair
MDT	Mean Down Time
Aoss	Operational Steady-State Availability

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Dependencies

numpy
pandas
plotly
pydantic>=2.0
python-dotenv

Dev: pytest, pytest-cov