Fuzzy logic boosts fault detection in drone safety testing
Targeting the Right Situations: Mutation Testing of Fuzzy-Powered Situation Hyperspace
Robotics
Summary
Testing safety for drones flying underground is tricky because there are many unpredictable situations to check. The authors studied two ways to test the drone software: one picks test scenarios randomly, and the other picks them based on fuzzy logic, which guesses which situations might be riskier. They found the fuzzy logic method finds more hidden bugs and is better at catching tough faults. This means smarter testing can help make drones safer in complex environments.
What this means in practice
- •For drone developers: Test underground drone software to find more safety-critical faults using fuzzy logic guided scenario selection.
- •For autonomous vehicle testers: Improve fault detection efficiency by prioritizing high-risk test situations with fuzzy risk estimates during simulation.
Authors
Nawshin Mannan Proma
Abstract
Ensuring the safety of autonomous Unmanned Aerial Vehicles (UAVs) operating in underground mines is challenging due to the vast and uncertain Operational Design Domain (ODD). Traditional validation approaches, such as random testing, often result in inefficient situation coverage, allocating computational resources to low-risk scenarios while failing to expose critical edge cases. Building on prior work in risk-prioritized Situation Coverage, this paper investigates whether Fuzzy Logic-based risk estimation effectively correlates with real system failures. To this end, we conduct a mutation testing study within our SCALOFT simulation framework. A fixed set of software mutants is systematically injected and evaluated under two testing strategies: (i) uniformly random situation generation and (ii) Fuzzy-Guided risk-prioritized situation generation. Experimental results show that the Fuzzy-Guided approach achieves a higher mutation score and is more effective at exposing difficult-to-detect faults compared to random testing.