SCALOFT: An Initial Approach for Situation Coverage-Based Safety Analysis of an Autonomous Aerial Drone in a Mine Environment

TL;DR

This paper introduces SCALOFT, a systematic safety testing framework for autonomous aerial drones in mine environments, focusing on situation coverage, real-time monitoring, and fault detection to enhance safety assurance.

Contribution

SCALOFT is a novel safety analysis approach that integrates situation coverage monitoring and fault detection for autonomous drones in hazardous environments.

Findings

SCALOFT successfully detects seeded faults in the drone system.

The approach provides a measurable safety coverage metric.

SCALOFT facilitates detailed safety violation analysis.

Abstract

The safety of autonomous systems in dynamic and hazardous environments poses significant challenges. This paper presents a testing approach named SCALOFT for systematically assessing the safety of an autonomous aerial drone in a mine. SCALOFT provides a framework for developing diverse test cases, real-time monitoring of system behaviour, and detection of safety violations. Detected violations are then logged with unique identifiers for detailed analysis and future improvement. SCALOFT helps build a safety argument by monitoring situation coverage and calculating a final coverage measure. We have evaluated the performance of this approach by deliberately introducing seeded faults into the system and assessing whether SCALOFT is able to detect those faults. For a small set of plausible faults, we show that SCALOFT is successful in this.