CRII: CIF: Robust, Principled, and Practical Adaptive Sampling with Mobile Sensors

快速准确地感知环境是现代科学和工程面临的一个基本挑战。有害藻类的大量繁殖破坏了饮用水的获取，野火对美国西部的安全构成了持续的威胁，交通和工业相关的空气污染物对不断增长的城市人口构成了威胁。为了解决这些问题，研究人员越来越多地转向移动采样设备，以提供安全、持久的环境现象估计。然而，鉴于这些设备的任务是覆盖广阔的空间区域，一个关键的开放性问题是设计采样策略，以在现有资源的情况下提供最高质量的信息。该项目旨在使移动采样设备能够对此类现象进行有效、自主的监测，从而使科学家和其他相关行为者能够做出明智的、数据驱动的决策。该项目还通过在无人驾驶飞机系统上部署开发的算法，促进大学生的参与，并举办研讨会，让代表性不足的社区大学生参与项目目标。环境感知的一个关键任务是确定某个现象在某个阈值之上（例如，污染水平高于安全限度的地区），这个问题被称为水平集估计（LSE）。现有的LSE算法存在以下缺陷：(1)未能纳入与移动传感器相关的采样成本，(2)缺乏理论保证，或(3)依赖于强大的建模假设。该项目的技术目标是为LSE开发具有良好理解的理论性质的实用自适应采样算法。该项目的第一个重点将考虑估计一维阶跃函数变化点的问题，利用主动学习和机器人路径规划之间的联系，开发和分析能够整合以前忽略的成本和处理噪声测量的算法。第二个要点将通过基于图的方法直接考虑二维LSE问题。通过将基于图的主动学习与马尔可夫决策过程和强化学习的最新技术相结合，基于行驶距离、测量次数和返回基站充电需求的实际采样成本将被纳入其中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A fundamental challenge to modern science and engineering is that of rapidly and accurately sensing the environment. Harmful algae blooms impair access to drinking water, wildfires present a persistent threat to safety in the western United States, and traffic and industry-related air pollutants pose a risk to growing urban populations. To combat these issues, researchers are turning increasingly to mobile sampling devices to provide safe, persistent estimation of environmental phenomena. However, given the vast spatial regions these devices are tasked with covering, a key open problem is that of designing sampling strategies to provide the highest quality of information given the available resources. This project aims to enable mobile sampling devices to perform efficient, autonomous monitoring of such phenomena, thereby permitting scientists and other concerned actors to make informed, data-driven decisions. The project also facilitates undergraduate involvement through the deployment of the developed algorithms on an unmanned aerial system, as well as workshops to engage underrepresented community college students in the project goals.A key task in environmental sensing is that of determining where a phenomenon lies above a certain threshold (for example, regions where the pollution level is above a safe limit), a problem known as level-set estimation (LSE). Existing LSE algorithms fall short by either (1) failing to incorporate sampling costs associated with mobile sensors, (2) lacking theoretical guarantees, or (3) relying on strong modeling assumptions. The technical aim of this project is the development of practical adaptive sampling algorithms for LSE with well-understood theoretical properties. The first thrust of the project will consider the problem of estimating the change point of a step function in one dimension, drawing on connections between active learning and robotic path planning to develop and analyze algorithms capable of incorporating previously-ignored costs and handling noisy measurements. The second thrust will consider the two-dimensional LSE problem directly via a graph-based approach. By combining recent techniques from graph-based active learning with Markov decision processes and reinforcement learning, realistic sampling costs based on the distance traveled, the number of measurements taken, and the need to return to a base station for battery recharging will be incorporated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.