不稳定系统的粗信息主动定位
Active Localization of Unstable Systems with Coarse Information
AI总结：
本文提出了一种基于集估计器和Voronoi划分的主动定位算法，用于在粗略传感下恢复不稳定系统的初始状态并保证信息区域的稳定性。
摘要：
我们研究了在粗略、单比特传感下不稳定系统的定位与控制问题。受理解此类最小反馈所施加的根本限制的启发，我们确定了在存在不稳定性和极稀疏测量的情况下能够恢复初始状态的充分条件。基于这些条件，我们开发了一种主动定位算法，该算法集成了基于集的估计器和从Voronoi划分导出的控制策略，能够证明估计初始状态的同时确保代理保持在信息区域。在推导的条件下，所提出的方法保证了初始状态不确定性的指数收缩，并通过数值实验进一步支持了这一结果。这些发现可以为机器人学中的定位提供理论洞察，其中传感通常局限于粗略抽象，如关键帧、分段或基于线的特征。
英文摘要：
We study localization and control for unstable systems under coarse, single-bit sensing. Motivated by understanding the fundamental limitations imposed by such minimal feedback, we identify sufficient conditions under which the initial state can be recovered despite instability and extremely sparse measurements. Building on these conditions, we develop an active localization algorithm that integrates a set-based estimator with a control strategy derived from Voronoi partitions, which provably estimates the initial state while ensuring the agent remains in informative regions. Under the derived conditions, the proposed approach guarantees exponential contraction of the initial-state uncertainty, and the result is further supported by numerical experiments. These findings can offer theoretical insight into localization in robotics, where sensing is often limited to coarse abstractions such as keyframes, segmentations, or line-based features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
