Contextual Autonomy Evaluation of Unmanned Aerial Vehicles in Subterranean Environments

TL;DR

This paper introduces a fuzzy framework to evaluate the contextual autonomy of small UAVs in subterranean environments, considering task complexity and environmental factors, with experiments demonstrating its effectiveness.

Contribution

It proposes a cascaded Fuzzy Inference System to quantify autonomy, incorporating a predictive measure for future mission performance based on past data.

Findings

Framework successfully evaluated autonomy in various contexts

Predictive measure improves future performance prediction

Experiments with seven different sUAS validate approach

Abstract

In this paper we focus on the evaluation of contextual autonomy for robots. More specifically, we propose a fuzzy framework for calculating the autonomy score for a small Unmanned Aerial Systems (sUAS) for performing a task while considering task complexity and environmental factors. Our framework is a cascaded Fuzzy Inference System (cFIS) composed of combination of three FIS which represent different contextual autonomy capabilities. We performed several experiments to test our framework in various contexts, such as endurance time, navigation, take off/land, and room clearing, with seven different sUAS. We introduce a predictive measure which improves upon previous predictive measures, allowing for previous real-world task performance to be used in predicting future mission performance.