Human-Centered Autonomy for UAS Target Search

TL;DR

This paper introduces a human-centered autonomous framework for UAS target search that integrates operator inputs and high-level context to improve search efficiency and effectiveness in dynamic environments.

Contribution

It presents a novel probabilistic, operator-informed planning approach that combines high-level mission context with autonomous search algorithms for UAS.

Findings

Achieved 18% more victim finds in simulations.

Generated guidance plans 15 times more efficient than current methods.

Effectively aligned task mental models with operator inputs.

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.