Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. 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 UniSaT (Unified Search and Track), a novel unified-objective formulation for the search and track problem based on Random Finite Sets (RFS). Our approach models unknown and known objects using a combined generalized labeled multi-Bernoulli (GLMB) filter. For unseen objects, UniSaT leverages both cardinality and spatial prior distributions, allowing it to operate without prior knowledge of the exact number of objects in the search 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, presenting both qualitative results and quantitative improvements over a multi-objective method.

翻译：自主搜索与跟踪多目标的路径规划是侦察、监视与数据收集等应用中的关键问题。由于搜索新目标与维持已发现目标航迹这两个目标之间存在固有的竞争关系，当前大多数方法依赖于多目标规划技术，需要用户基于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）的搜索与跟踪问题的新型统一目标建模框架。我们的方法采用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，UniSaT同时利用基数先验分布与空间先验分布，使其无需预先获知搜索空间内目标的确切数量即可运行。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。