CAREER: Leveraging Signal Structure for Cost-Sensitive Adaptive Sampling

科学家和工程师面临的一个持续的挑战是快速感知环境的能力。无论是监测有害的空气污染物、野火还是湖泊中的低氧水平，一项基本任务是确定所有感兴趣的因素达到临界水平的位置。为了解决这个问题，从业者越来越多地利用移动传感器，比如那些部署在无人驾驶车辆上的传感器。虽然这些提供了一种探索大空间区域和危险环境的安全手段，但它们也需要广泛的规划，以确保收集最相关的测量数据并管理电池寿命。该项目将通过设计自适应采样算法来克服这些问题，该算法将自动引导采样车辆到达最重要的区域，同时考虑到与测量过程相关的实际成本。由此产生的方法将为环境采样提供通用的解决方案，可以很容易地被从业者使用，同时也考虑到通常阻止适应性方法转化为实践的现实挑战。此外，这项研究将支持教育和多样性，通过开发无人驾驶飞行器的高中课程，以及利用自适应抽样的公民科学运动，使全国最大的城市公园之一受益。本课题的目标是设计和分析针对水平集估计问题的成本敏感自适应采样算法。新的自适应采样技术将在三种形式的水平集结构下开发：(1)边界平滑，其中没有可用的领域或侧面知识；(2)已知的相似性结构，表明哪些位置应该具有相似的测量值；(3)未知的聚类结构，其中感兴趣的信号在每个位置簇中是恒定的，目标是同时学习聚类结构和测量值。对于每种情况，原则性算法将基于主动学习，多臂强盗和强化学习领域的最新发展而衍生，其目标是提供改进的经验性能，严格的理论表征，并结合现实成本，如传感时行进的距离。算法将在真实世界的数据集上进行评估，包括测量空气质量和地热能源前景的数据集。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 persistent challenge to scientists and engineers is the ability to rapidly sense the environment. Whether monitoring for harmful air pollutants, wildfires, or low oxygen levels in lakes, an essential task is to determine all locations where a factor of interest reaches a critical level. To solve this problem, practitioners are increasingly utilizing mobile sensors such as those deployed on unmanned vehicles. While these provide a safe means of exploring large spatial regions and hazardous environments, they also require extensive planning to ensure the most relevant measurements are collected and to manage battery life. This project will overcome these issues through the design of adaptive sampling algorithms, which automatically guide sampling vehicles to the most important regions while accounting for the realistic costs associated with the measurement process. The resulting approaches will provide general-purpose solutions to environmental sampling that can be easily utilized by practitioners while also accounting for the realistic challenges that typically prevent adaptive methods from translating into practice. Furthermore, this research will support education and diversity through the development of curriculum for a high school course on unmanned aerial vehicles as well as a citizen science campaign that leverages adaptive sampling to benefit one of the nation's largest urban parks.The objective of this project is to design and analyze cost-sensitive adaptive sampling algorithms for the problem of level set estimation. Novel adaptive sampling techniques will be developed under three forms of level set structure: (1) boundary smoothness, where no domain or side knowledge is available, (2) known similarity structure that indicates which locations should have similar measurement values, and (3) unknown cluster structure, where the signal of interest is constant within each cluster of locations, and the goal is to simultaneously learn the cluster structure and measurement values. For each case, principled algorithms will be derived based on recent developments from the fields of active learning, multi-armed bandits, and reinforcement learning, with the goal of providing improved empirical performance, rigorous theoretical characterization, and incorporating realistic costs such as the distance traveled while sensing. Algorithms will be evaluated on real-world datasets including those measuring air quality and geothermal energy prospects.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.