On The Use of Small-Scale Unmanned Autonomous Systems for Decision-Support in Subterranean Environments: The Case of Operator Situational Awareness Assessment

Abstract

Small-scale Unmanned Autonomous Systems (sUASs) such as Drones are becoming an integral part of the next-gen decision support landscape of defense and homeland security. Given the critical nature of the associated decisions, regardless of fully- or semi-autonomous operation, any viable sUAS platform must have the technical capability to provide optimal levels of Situational Awareness (SA) to its human operators or teammates. This paper presents our early efforts on developing test procedures for evaluating sUASs for their capacity to provide adequate levels of Operator SA (OSA) in subterranean environments. In particular, we focus on adapting existing quantitative SA evaluation methods (utilized in aviation industry) for sUAS operated in subterranean environments. First, our initial impressions on the use and adoption of quantitative SA methods for Level 1 SA (i.e., Perception) are presented. For this, the use Situation Awareness Global Assessment Technique SAGAT) questionnaires for assessing the operator perception level of situation elements is presented in a manner suitable for quick assessment in field tests. Second, test procedure design for quantitative OSA evaluation of sUASs is presented, with a focus on both Level 1 and Level 2 SA (i.e., Comprehension) in subterranean environments via the use of Attention Allocation Model. The paper concludes by providing a basic analysis in terms of test parameters and procedures utilized in both simulations and real-life testing, along with our insights on future research directions.