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arXiv · 2309.06395

Human-Centered Autonomy for UAS Target Search

Hunter M. Ray·Zakariya Laouar·Zachary Sunberg·Nisar Ahmed
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Abstract

Current methods of deploying robots that operate in dynamic, uncertain environments, such as Uncrewed Aerial Systems in search \& rescue missions, require nearly continuous human supervision for vehicle guidance and operation. These methods do not consider high-level mission context resulting in cumbersome manual operation or inefficient exhaustive search patterns. We present a human-centered autonomous framework that infers geospatial mission context through dynamic feature sets, which then guides a probabilistic target search planner. Operators provide a set of diverse inputs, including priority definition, spatial semantic information about ad-hoc geographical areas, and reference waypoints, which are probabilistically fused with geographical database information and condensed into a geospatial distribution representing an operator's preferences over an area. An online, POMDP-based planner, optimized for target searching, is augmented with this reward map to generate an operator-constrained policy. Our results, simulated based on input from five professional rescuers, display effective task mental model alignment, 18\% more victim finds, and 15 times more efficient guidance plans then current operational methods.

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Hunter M. Ray, Zakariya Laouar, Zachary Sunberg, Nisar Ahmed. 2024-03-07. Human-Centered Autonomy for UAS Target Search. https://arxiv.org/abs/2309.06395

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