发表机构
The City College of New York, The City University of New York（纽约市立大学纽约城市学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种部分扫描与移动策略，利用置信集判断测量信息是否足够，使移动机器人在有限次移动内高概率到达源邻域。
AI 中文摘要
本文提出了一种部分扫描与移动策略，用于配备偏置标量传感器的移动机器人进行源搜索。在每个机器人位置，当机器人旋转时，传感器收集源场测量值。我们不需要在每次移动前进行完整的圆形扫描，而是询问仅覆盖圆的一部分的测量值何时已经足以确定下一个动作。我们开发了一种针对部分扫描的梯度估计方法，并附带一个置信集，该置信集考虑了测量噪声和局部场变化。机器人利用该置信集决定其是否足够接近源，或者是否有足够的信息沿下降方向移动。我们证明，在适当条件下，每个决策都可以在规定的部分扫描内做出，并且机器人以高概率在有限次移动内到达源的期望邻域。
英文摘要
This paper presents a partial-scan-and-move strategy for source seeking with a mobile robot equipped with an offset scalar sensor. At each robot position, the sensor collects source field measurements while the robot rotates. Instead of requiring a complete circular scan before every move, we ask when the measurements collected over only part of the circle are already sufficient to determine the next action. We develop a gradient estimation method for partial scans together with a confidence set that accounts for measurement noise and local field variation. The robot uses this confidence set to decide whether it is close enough to the source or has enough information to move in a descent direction. We show that, under suitable conditions, each decision can be made within a prescribed partial scan and that the robot reaches a desired neighborhood of the source in finitely many moves with high probability.
