SafeDrones

Real-time reliability and safety evaluation for multicopters and eVTOL aircraft.

SafeDrones turns current vehicle health, configuration, and mission conditions into interpretable failure probability and mean-time-to-failure estimates.

Get started View on GitHub

What can SafeDrones evaluate?

• Propulsion reliability

  ---

  Estimate failure probability and MTTF for quadcopter, hexacopter, and octocopter motor arrangements using configuration-aware Markov models.

  Propulsion model →

• Battery health

  ---

  Model battery failure from charge level, degradation, charge/discharge rates, component failure rate, and mission duration.

  Component models →

• Processor reliability

  ---

  Account for temperature, utilization, and Weibull lifetime behaviour using an Arrhenius-based processor model.

  Component models →

• GPS and collision risk

  ---

  Evaluate satellite-availability reliability and measure trajectory samples that enter configured danger or collision zones.

  Python API →

How it fits into an autonomous system

Health monitoring and diagnosis
              ↓
Vehicle state + mission time + failure rates
              ↓
        SafeDrones models
              ↓
Probability of failure + mean time to failure
              ↓
Continue mission · return to base · land safely

Tip

Use SafeDrones alongside a fault-detection and diagnosis system. The models consume observations such as motor status, battery charge, temperature, and visible GPS satellites; they do not replace the sensors or diagnosis layer.

Supported implementations

Interface	Best for	Included resources
Python	Integration, notebooks, automated analysis	Package source and example notebooks
MATLAB	Model exploration and engineering workflows	Propulsion, battery, and GPS scripts

Start here

1. Install SafeDrones and run a first assessment.
2. Choose the reliability model for your subsystem.
3. Review the Python API or MATLAB guide.
4. Cite the relevant work from the research page.