Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informative trajectory planning approach, namely ReSPIRe, for SAT in unknown cluttered environments under considerably inaccurate prior target information and limited sensing field of view. We first develop a novel sigma point-based approximation approach to fast and accurately estimate mutual information reward under non-Gaussian belief distributions, utilizing informative sampling in state and observation spaces to mitigate the computational intractability of integral calculation. To tackle significant uncertainty associated with inadequate prior target information, we propose the hierarchical particle structure in ReSPIRe, which not only extracts critical particles for global route guidance, but also adjusts the particle number adaptively for planning efficiency. Building upon the hierarchical structure, we develop the reusable belief tree search approach to build a policy tree for online trajectory planning under uncertainty, which reuses rollout evaluation to improve planning efficiency. Extensive simulations and real-world experiments demonstrate that ReSPIRe outperforms representative benchmark methods with smaller MI approximation error, higher search efficiency, and more stable tracking performance, while maintaining outstanding computational efficiency.

翻译：目标搜索与跟踪是机器人应用（如搜救与环境探测）中的基础性问题。本文提出了一种信息化轨迹规划方法ReSPIRe，用于在未知杂乱环境中，在先验目标信息极不准确且传感器视场受限条件下的目标搜索与跟踪任务。我们首先提出了一种基于Sigma点的新型近似方法，通过在状态空间与观测空间进行信息性采样，快速精确地估计非高斯信念分布下的互信息奖励，从而缓解积分计算的计算难题。为应对因先验目标信息不足导致的显著不确定性，我们在ReSPIRe中提出了分层粒子结构，该结构不仅能提取关键粒子以提供全局路径引导，还能自适应调整粒子数量以提升规划效率。基于该分层结构，我们开发了可复用信念树搜索方法，通过构建策略树实现不确定性下的在线轨迹规划，并利用重复的推演评估提升规划效率。大量仿真与实物实验表明，ReSPIRe在保持优异计算效率的同时，以更小的互信息近似误差、更高的搜索效率和更稳定的跟踪性能，优于现有代表性基准方法。