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.

翻译：当前在动态不确定环境下（如搜救任务中的无人机系统）部署机器人的方法，需要操作人员近乎持续进行人工监督以完成飞行器引导与操作。这些方法未考虑高层任务场景，导致繁琐的人工操作或低效的穷举搜索模式。我们提出一种面向人类中心的自主框架，该框架通过动态特征集推断地理空间任务场景，进而指导概率性目标搜索规划器。操作人员可提供多样化输入，包括优先级定义、临时地理区域的语义空间信息及参考航点，这些信息与地理数据库信息进行概率融合，形成表征操作人员偏好区域的地理空间分布。基于部分可观测马尔可夫决策过程的在线规划器经目标搜索优化后，利用此奖励地图生成受操作人员约束的策略。基于五名专业救援人员输入数据的模拟结果表明，相较现有操作方法，本系统实现了有效的任务心智模型对齐、多发现18%受害者的效率，以及15倍的引导规划效率提升。