  The fast-growing demand for fully autonomous aerial operations in shared
spaces necessitates developing trustworthy agents that can safely and
seamlessly navigate in crowded, dynamic spaces. In this work, we propose Social
Robot Tree Search (SoRTS), an algorithm for the safe navigation of mobile
robots in social domains. SoRTS aims to augment existing socially-aware
trajectory prediction policies with a Monte Carlo Tree Search planner for
improved downstream navigation of mobile robots. To evaluate the performance of
our method, we choose the use case of social navigation for general aviation.
To aid this evaluation, within this work, we also introduce X-PlaneROS, a
high-fidelity aerial simulator, to enable more research in full-scale aerial
autonomy. By conducting a user study based on the assessments of 26 FAA
certified pilots, we show that SoRTS performs comparably to a competent human
pilot, significantly outperforming our baseline algorithm. We further
complement these results with self-play experiments in scenarios with
increasing complexity.
