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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

基本信息

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

项目摘要

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.

自主的空中、地面和水下机器人已经成为无数传感应用的有前途的平台。空中无人机可以监测龙卷风和森林火灾等自然灾害，水下机器人可以探测有害的藻华和追踪入侵的鱼类。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，给定有限数量的移动传感器，它们应该如何动态地放置以最好地观察感兴趣的数量，特别是在有限的能量下，同时要求机器人保持连接？传感器的可能放置空间很大，能源和连接方面的限制也增加了挑战。本项目将开发有效的算法来调度这些约束下的移动传感器，并通过现场实验评估其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，特别是来自代表性不足的群体。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互补专业知识，因为该项目将为具有可观察性保证的移动传感器的可扩展调度提供新的视角和可推广的原则框架。该项目的目标将通过四个综合研究重点来实现，包括理论研究、算法开发和实验验证。在传感和通信不确定的情况下，将基于gramian的非线性可观测性度量与随机抽样相结合，有效地计算传感器的近最优位置。Thrust 2扩展了框架，以适应能源和连接约束，其中特别强调分布式计算方法。受鱼类跟踪应用的启发，在推力1和推力2中开发的理论和算法将通过一队自动水面车辆跟踪移动的声学标签进行实验验证。该项目的推力3包括实验测试平台的开发，包括机器人及其相关模型和控制器，而推力4通过模拟和密歇根州希金斯湖的现场实验来评估开发的移动传感器调度算法。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。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Scenario Approach to Robust Simulation-based Path Planning

基于仿真的鲁棒路径规划的场景方法

DOI：
10.23919/acc53348.2022.9867194
发表时间：
2022
期刊：
Proceedings of the American Control Conference
影响因子：
0
作者：
Bopardikar, Shaunak D.;Srivastava, Vaibhav
通讯作者：
Srivastava, Vaibhav

Optimal Control of Active Drifter Systems

主动漂移系统的优化控制

DOI：
10.1109/cdc51059.2022.9993037
发表时间：
2022
期刊：
Proceedings of 2022 IEEE 61st Conference on Decision and Control
影响因子：
0
作者：
Gaskell, Eric;Tan, Xiaobo
通讯作者：
Tan, Xiaobo

Incorporating Observability via Control Barrier Functions with Application to Range-based Target Tracking

DOI：
10.1109/aim46487.2021.9517467
发表时间：
2021-07
期刊：
2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
影响因子：
0
作者：
Demetris Coleman;S. Bopardikar;Xiaobo Tan
通讯作者：
Demetris Coleman;S. Bopardikar;Xiaobo Tan

Numerical and Topological Conditions for Sub-Optimal Distributed Kalman Filtering

次优分布式卡尔曼滤波的数值和拓扑条件