SCALOFT: An Initial Approach for Situation Coverage-Based Safety Analysis of an Autonomous Aerial Drone in a Mine Environment
Nawshin Mannan Proma, Victoria J Hodge, Rob Alexander
TL;DR
The paper tackles safety testing for autonomous aerial drones operating in hazardous mine environments. It introduces SCALOFT, a situation-coverage based testing framework built on an Operational Domain Model (ODM) to organize target scenarios into a situation hyperspace and leverages the ALOFT simulation suite for execution. SCALOFT generates a finite set of test cases, monitors real-time drone behavior, logs safety violations with unique identifiers, and computes a final situation-coverage metric as a measure of testing thoroughness. Preliminary evaluation using HAZOP-guided fault injection demonstrates detection of small faults, underscoring the method's potential while highlighting the need to scale the hyperspace and fault-generation schemes for broader applicability.
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.