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、不确定环境中部署机器人的方法（例如，在搜救任务中使用无人机系统）需要近乎连续的人工监督以引导和操作飞行器。这些方法未考虑高层任务上下文，导致笨重的手动操作或低效的穷举搜索模式。我们提出一种面向人机的自主框架，该框架通过动态特征集推断地理空间任务上下文，进而指导概率性目标搜索规划器。操作员可提供多种输入（包括优先级定义、临时地理区域的语义信息及参考航点），这些输入与地理数据库信息进行概率融合，并压缩为表征操作员在区域上偏好的地理空间分布。基于部分可观测马尔可夫决策过程（POMDP）的在线规划器针对目标搜索进行了优化，该规划器通过奖励地图增强后生成受操作员约束的策略。基于五位专业救援人员输入进行的仿真结果表明，与传统操作方法相比，我们的方法实现了有效的任务心智模型对齐，多发现18%的遇难者，并生成效率提高15倍的引导方案。