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.

翻译：共享空间中全自主空中作业需求的快速增长，要求开发能够在拥挤动态环境中安全无缝导航的可信智能体。本文提出社交机器人树搜索算法（SoRTS），一种用于社交领域移动机器人安全导航的算法。SoRTS旨在通过蒙特卡洛树搜索规划器增强现有社交感知轨迹预测策略，提升移动机器人的下游导航性能。为评估该方法，我们选取通用航空社交导航作为应用场景。为此，本工作同时引入高保真空中模拟器X-PlaneROS，以推动全规模空中自主性的进一步研究。通过基于26名FAA认证飞行员评估的用户研究，SoRTS表现与人类熟练飞行员相当，且显著优于基线算法。我们进一步通过递增复杂场景中的自博弈实验补充验证了这些结果。