Scalable Randomized Scheduling of Mobile Sensors with Observability Guarantees

具有可观测性保证的移动传感器的可扩展随机调度

• 批准号: 2030556
• 负责人: Shaunak Bopardikar
• 金额: $ 36万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030556&HistoricalAwards=false
• 关键词: Scalable; Randomized; Scheduling; Mobile; Sensors; Scalable; Randomized; Scheduling; Mobile; Sensors

Autonomous aerial, ground, and underwater robots have emerged as promising platforms for a myriad of sensing applications. Aerial drones can monitor natural calamities like tornadoes and forest fires, and underwater robots can detect harmful algal blooms and track invasive fish species. The information to be gathered is typically governed by some nonlinear dynamic processes. A natural question to ask is, given a limited number of mobile sensors, how should they be dynamically placed to best observe the quantity of interest, especially with limited energy while requiring the robots to remain connected? The space of possible sensor placements is vast and constraints on energy and connectivity add to the challenges. This project will develop efficient algorithms to schedule the mobile sensors under these constraints and evaluate their performance in tracking a moving target through field experiments. The interdisciplinary nature of this research will be integrated with outreach and educational activities to broaden participation of K-12 and undergraduate students, especially from underrepresented groups.The project combines the investigators’ complementary expertise in control, network theory and fast randomized computation as the project will result in a fresh perspective and a generalizable, principled framework for scalable scheduling of mobile sensors with observability guarantees. The project goals will be realized through four integrated research thrusts that span theoretical investigation, algorithmic development, and experimental validation. Thrust 1 focuses on integrating a Gramian-based nonlinear observability metric with randomized sampling for efficient computation of near-optimal sensor placements under sensing and communication uncertainty. Thrust 2 extends the framework to accommodate energy and connectivity constraints, where special emphasis will be on distributed approaches for computation. Motivated by the fish-tracking application, the theory and algorithms developed in Thrusts 1 and 2 will be validated experimentally with a fleet of autonomous surface vehicles tracking a moving acoustic tag. Thrust 3 of the project involves the development of the experimental testbed, including the robots and their associated models and controllers, while Thrust 4 evaluates the developed mobile sensor scheduling algorithms via both simulation and field experiments in Higgins Lake, Michigan.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.

自主空中，地面和水下机器人已成为多种灵敏度应用的承诺平台。空中无人机可以监测龙卷风和森林大火等自然灾害，水下机器人可以检测有害的藻类血液并追踪侵入性鱼类。要收集的信息通常由一些非线性动态过程控制。一个自然的问题是，鉴于数量有限的移动传感器，应该如何动态地放置它们以最佳观察兴趣的数量，尤其是在有限的能量的同时，在要求机器人保持连接的同时，它们是有限的？可能的传感器放置空间是巨大的，并且对能量和连通性的限制增加了挑战。该项目将开发有效的算法，以在这些约束下安排移动传感器，并评估其通过现场实验跟踪移动目标的性能。这项研究的跨学科性质将与外展和教育活动相结合，以扩大K-12和本科生的参与，尤其是从代表性不足的小组中。该项目结合了研究人​​员在控制，网络理论和快速随机计算方面的完整专业知识，因为该项目将在一个全新的角度和一般性的框架中，并确保了量表的范围，可确保量表的范围，并确保型号的全新框架。项目目标将通过跨越理论投资，算法开发和实验验证的四个综合研究推力来实现。推力1的重点是将基于Gramian的非线性观察度量与随机采样进行整合，以有效地计算感觉和通信不确定性下的近距离传感器位置。推力2扩展了框架以适应能量和连通性约束，其中将特别重点放在分布式计算方法上。在推力1和2中开发的理论和算法是由鱼类跟踪应用的促进的，将通过跟踪移动的声学标签的自动表面车辆进行实验验证。该项目的推力3涉及实验测试的开发，包括机器人及其相关的模型和控制器，而Throust 4通过模拟和密歇根州希金斯湖的模拟和现场实验评估了开发的移动传感器调度算法。该奖项颁发了NSF的法定任务，并通过评估了Intervisiation the Intellitial and Foundation and Foundation and Intfortial and Infceptial and Infcordial and Intfortial and Intfortial and Inthlit and Intfortial and Inthlit and Intfortial and Inthforiatial的支持。