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SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities

Koorosh Aslansefat, Panagiota Nikolaou, Martin Walker, Mohammed Naveed Akram, Ioannis Sorokos, Jan Reich, Panayiotis Kolios, Maria K. Michael, Theocharis Theocharides, Georgios Ellinas, Daniel Schneider, Yiannis Papadopoulos

TL;DR

The paper tackles safety and reliability for UAVs operating in dynamic environments where design-time models alone are insufficient. SafeDrones combines fault-tree-based design-time knowledge with runtime monitoring, using a symptom layer and Semi-Markov Process–based evaluation within an Executable Digital Dependable Identity (EDDI) to provide real-time reliability and risk estimates. It integrates Arrhenius temperature effects via $AF= \exp\left(\frac{E_a}{k}\bigl(\frac{1}{T_r}-\frac{1}{T_a}\bigr)\right)$ to adjust $MTTF$ as $MTTF_{final}=\frac{MTTF_{ref}}{AF}$ and demonstrates runtime action guidance (e.g., mission reconfiguration or emergency landing) in a UAV inspection scenario. Using the ICARUS-based setup with a DJI Matrice 300 RTK and an NVIDIA Xavier NX, the authors show fault-free and faulty scenarios where reliability estimates trigger decisions and a GitHub repository provides the implementation.

Abstract

The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner.

SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities

TL;DR

The paper tackles safety and reliability for UAVs operating in dynamic environments where design-time models alone are insufficient. SafeDrones combines fault-tree-based design-time knowledge with runtime monitoring, using a symptom layer and Semi-Markov Process–based evaluation within an Executable Digital Dependable Identity (EDDI) to provide real-time reliability and risk estimates. It integrates Arrhenius temperature effects via to adjust as and demonstrates runtime action guidance (e.g., mission reconfiguration or emergency landing) in a UAV inspection scenario. Using the ICARUS-based setup with a DJI Matrice 300 RTK and an NVIDIA Xavier NX, the authors show fault-free and faulty scenarios where reliability estimates trigger decisions and a GitHub repository provides the implementation.

Abstract

The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner.
Paper Structure (13 sections, 7 equations, 5 figures, 1 table)

This paper contains 13 sections, 7 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Fault Tree Analysis
  4. Reliability Modelling using Semi-Markov Processes (SMP)
  5. Reliability Modeling using Arrhenius Equation
  6. The Executable Digital Dependable Identity (EDDI)
  7. Methodology
  8. Experimental Implementation
  9. Experimental Results
  10. Reliability Analysis of the Fault-Free Scenario
  11. Reliability Analysis of the Faulty Scenario
  12. Conclusion and Future Work
  13. Proposed Fault Tree of a generic UAV

Figures (5)

  • Figure 1: Small FTA of a UAV considering complex basic events with failure symptoms and three different types of propulsion system reconfiguration
  • Figure 2: Overall view on merging real-time monitoring and diagnosis system with Fault Tree Analysis
  • Figure 3: Inspection procedure using ICARUS toolkit savva2021icarus for pole detection
  • Figure 4: Fault-Free Scenario: (a) Battery degradation (battery level in percentage), (b) Processor Temperature (c) Probability of failure (d) Mean Time to failure -- Faulty Battery Scenario: (e) Battery degradation (battery level in percentage), (f) Processor Temperature (g) Probability of failure (h) Mean Time to failure -- (i) Processor’s MTTF and temperature for the Fault Free Scenario.
  • Figure 5: Proposed Fault Tree of a generic UAV