ReSPIRe: 未知環境におけるロボット確率探索・追跡のための情報的で再利用可能な信念木探索
ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments
未知の雑然とした環境で不正確な事前情報と限られた視野のもと、相互情報量を効率的に近似し階層的粒子構造と再利用可能な信念木探索で目標探索・追跡を行う手法を提案。
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著者: Kangjie Zhou, Zhaoyang Li, Han Gao, Yao Su, Hangxin Liu, Junzhi Yu, Chang Liu
分類: cs.RO
原文アブストラクト
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.
