Achieving persistent tracking of multiple dynamic targets over a large spatial area poses significant challenges for a single-robot system with constrained sensing capabilities. As the robot moves to track different targets, the ones outside the field of view accumulate uncertainty, making them progressively harder to track. An effective path planning algorithm must manage uncertainty over a long horizon and account for the risk of permanently losing track of targets that remain unseen for too long. However, most existing approaches rely on short planning horizons and assume small, bounded environments, resulting in poor tracking performance and target loss in large-scale scenarios. In this paper, we present a hierarchical planner for tracking multiple moving targets with an aerial vehicle. To address the challenge of tracking non-static targets, our method incorporates motion models and uncertainty propagation during path execution, allowing for more informed decision-making. We decompose the multi-target tracking task into sub-tasks of single target search and detection, and our proposed pipeline consists a novel low-level coverage planner that enables searching for a target in an evolving belief area, and an estimation method to assess the likelihood of success for each sub-task, making it possible to convert the active target tracking task to a Markov decision process (MDP) that we solve with a tree-based algorithm to determine the sequence of sub-tasks. We validate our approach in simulation, demonstrating its effectiveness compared to existing planners for active target tracking tasks, and our proposed planner outperforms existing approaches, achieving a reduction of 11-70% in final uncertainty across different environments.

翻译：在空间广阔区域对多个动态目标实现持续跟踪，对于感知能力受限的单机器人系统构成显著挑战。当机器人移动以跟踪不同目标时，视野外的目标会积累不确定性，使其跟踪难度逐渐增加。有效的路径规划算法必须管理长时域的不确定性，并考虑目标因长时间未被观测而永久丢失的风险。然而，现有方法大多依赖短时域规划并假设环境规模有限且边界明确，导致在大规模场景中跟踪性能不佳且易丢失目标。本文提出一种用于无人机跟踪多个运动目标的分层规划器。为应对跟踪非静态目标的挑战，本方法在路径执行过程中融合了运动模型与不确定性传播机制，从而实现更具信息依据的决策。我们将多目标跟踪任务分解为单目标搜索与检测子任务，所提出的流程包含一个创新的底层覆盖规划器——可在动态演化的置信区域内搜索目标，以及一种评估各子任务成功概率的估计方法。这使得主动目标跟踪任务可转化为马尔可夫决策过程（MDP），我们通过基于树的算法进行求解以确定子任务执行序列。我们在仿真环境中验证了所提方法，证明其在主动目标跟踪任务中相较于现有规划器的有效性。实验表明，所提出的规划器在不同环境中均优于现有方法，最终不确定性降低了11-70%。