Getting started¶

Requirements¶

SafeDrones is a Python package with numerical and symbolic modelling dependencies. A dedicated virtual environment is recommended.

Installation¶

pip from GitHublocal development

python -m pip install "git+https://github.com/Dependable-Intelligent-Systems-Lab/SafeDrones.git"

git clone https://github.com/Dependable-Intelligent-Systems-Lab/SafeDrones.git
cd SafeDrones
python -m pip install -e .

First reliability assessment¶

Import the model class, configure the mission state, and calculate the combined propulsion, battery, and processor risk:

from SafeDrones.core.SafeDrones import SafeDrones

model = SafeDrones()
model.Set_Variables(
    MotorStatus=[1, 1, 1, 1, 1, 1],
    Motors_Configuration="PNPNPN",
    Motors_Lambda=0.001,
    BatteryLevel=80,
    time=100,
)

probability_of_failure, mttf = model.Drone_Risk_Calc()
print(float(probability_of_failure), float(mttf))

Units must be consistent

Failure rates and mission time must use compatible units. For example, if the failure rate is expressed per hour, provide mission time in hours and interpret MTTF in hours.

Assess one subsystem¶

You can call a model directly when only one subsystem is relevant:

motor_risk, motor_mttf = model.Motor_Failure_Risk_Calc(
    MotorStatus=[1, 1, 1, 1, 1, 1],
    Motors_Configuration="PNPNPN",
    Motors_Lambda=0.001,
    time=10,
)

Each reliability method returns a tuple:

Value	Meaning
`P_Fail`	Estimated probability of subsystem failure at the requested mission time
`MTTF`	Estimated mean time to failure

Explore the notebooks¶

The repository's example/ directory contains Jupyter notebooks for complete and subsystem-focused workflows.

Build these docs locally¶

python -m pip install -r docs/requirements.txt
mkdocs serve

Open the local URL printed by MkDocs. Changes under docs/ reload automatically.