ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments
提出一种名为ReSPIRe的轨迹规划方法，通过sigma点近似互信息奖励和可复用信念树搜索，在未知杂乱环境中高效完成目标搜索与跟踪，尤其适用于先验信息不准确和视野受限的场景。
Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This article proposes an informative trajectory planning approach, namely, reusable belief tree search with sigma point-based mutual information reward approximation (ReSPIRe), for SAT in unknown cluttered environments under considerably inaccurate prior target information and a limited sensing field of view (FOV). We first develop a novel sigma point (SP)-based approximation approach to fast and accurately estimate mutual information (MI) reward under non-Gaussian belief distributions, utilizing informative sampling in state and observation spaces to mitigate the computational intractability of integral calculation. To tackle the 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 (RBTS) 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.