The problem of path planning for autonomously searching and tracking multiple objects is important to reconnaissance, surveillance, and many other data-gathering applications. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce $\textit{UniSaT}$ ($\textit{Unified Search and Track}$), a unified-objective formulation for the search and track problem based on Random Finite Sets (RFS). This is done by modeling both the unknown and known objects through a combined generalized labeled multi-Bernoulli (GLMB) filter. For the unseen objects, we can leverage both cardinality and spatial prior distributions, which means $\textit{UniSaT}$ does not rely on knowing the exact count of the expected number of objects in the space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment and show both qualitative results as well as quantitative improvements over a multi-objective method.

翻译：自主搜索与跟踪多个目标的路径规划问题在侦察、监视及许多其他数据采集应用中具有重要意义。由于搜索新目标与维持已发现目标航迹之间存在固有的竞争性目标，现有方法大多依赖多目标规划技术，需要用户根据启发式规则或试错来调整参数以平衡这两个目标。本文提出《UniSaT》（统一搜索与跟踪），一种基于随机有限集（RFS）的搜索跟踪问题统一目标建模框架。该方法通过组合广义标签多伯努利（GLMB）滤波器对未知与已知目标进行统一建模。对于未观测目标，我们可同时利用基数先验分布与空间先验分布，这意味着《UniSaT》无需预先获知空间中目标的确切数量。规划器通过最大化该统一信念模型的互信息，实现搜索与跟踪行为的动态平衡。我们在仿真环境中验证了所提方法，并展示了相较于多目标方法的定性结果与定量性能提升。