# Human-Centered Autonomy for UAS Target Search

arXiv: https://arxiv.org/abs/2309.06395 · HTML: https://mlsanity.com/paper/2309.06395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [Rao-Blackwellized POMDP Planning](https://mlsanity.com/paper/2409.16392/) (2024)

## Abstract (arXiv)

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.