Collaborative Research: Compressive Robotic Systems: Gaining Efficiency Through Sparsity in Dynamic Environments
协作研究:压缩机器人系统:通过动态环境中的稀疏性提高效率
基本信息
- 批准号:1562031
- 负责人:
- 金额:$ 30万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-04-01 至 2020-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project investigates autonomous control and coordination of a group of robots that are tasked to explore, map, or monitor the environment they are in. The project aims to enhance the capabilities of such a group of robots by integrating Compressive Sensing for data compression. Compressive sensing enables robots to quickly extract information from their environment, efficiently communicate that information to each other over a wireless network, and intelligently direct their motion to obtain relevant sensing data in the future. Significant theoretical and technical challenges must be addressed in this project to realize the potential of a compressive robotic sensing system. The project will demonstrate results in two specific applications, (i) driving a group of aerial robots to monitor their environment, (ii) driving robotic micro-probes to measure processes inside a living cell. The project also seeks to disseminate its findings through educational and outreach activities. Results will be incorporated into undergraduate and graduate level courses in control theory at both Boston University and Stanford University. The researchers will also work with high school students and undergraduates through research mentorship programs and through lab demonstrations for visitors.The fundamental goal of the project is to create rigorously analyzed algorithms that take advantage of sparse signal descriptions to create efficient motion plans for a team of sensing robots that monitor the environment. The driving hypothesis is that sparsity can greatly extend the performance of robotic sensing systems by saving battery power, computation, storage, and communication bandwidth---all critically limited resources for robotic platforms. The research team will take a Bayesian approach to Compressive Sensing, which allows for sensing quality to be quantified with information theoretic metrics such as entropy. A receding horizon control approach will be developed for driving robotic sensors to collect the most valuable sensor data, in order to reconstruct a sparse representation of their environment using Compressive Sensing. Such control strategies will be adapted to both static and dynamic environments, and both centralized and distributed solutions will be sought. The concepts developed in this project will be applied to two specific sensing domains: (i) networks of quadrotor sensing robots sensing environmental data and (ii) confocal fluorescence microscopy for three-dimensional imaging of dynamics in bio-molecular systems. These two application domains have radically different length and time scales, dynamical properties, and information content. A successful application of the ideas developed in this project to both these domains will prove the generality of the Compressive Robotic Sensing System concept.
这个项目研究一组机器人的自主控制和协调,这些机器人的任务是探索、绘制地图或监控它们所处的环境。该项目旨在通过集成压缩传感进行数据压缩来增强这样一组机器人的能力。压缩感知使机器人能够快速从环境中提取信息,通过无线网络高效地相互通信,并智能地指导它们的运动,以在未来获得相关的传感数据。在这个项目中必须解决重大的理论和技术挑战,以实现可压缩的机器人传感系统的潜力。该项目将在两个具体应用中展示成果,(I)驱动一组空中机器人来监测它们的环境,(Ii)驱动机器人微探头来测量活细胞内的过程。该项目还寻求通过教育和外联活动传播其调查结果。结果将被纳入波士顿大学和斯坦福大学控制理论的本科生和研究生课程。研究人员还将通过研究指导计划和面向来访者的实验室演示与高中生和本科生合作。该项目的基本目标是创建经过严格分析的算法,利用稀疏信号描述来为监测环境的传感机器人团队创建高效的运动计划。驱动假设是,稀疏性可以通过节省电池电量、计算、存储和通信带宽来极大地提高机器人传感系统的性能-所有这些对于机器人平台来说都是极其有限的资源。研究小组将采用贝叶斯方法进行压缩感知,允许使用信息理论指标(如信息熵)来量化感知质量。将开发一种滚动时间控制方法来驱动机器人传感器收集最有价值的传感器数据,以便利用压缩感知重建其环境的稀疏表示。这样的控制策略将适用于静态和动态环境,并将寻求集中式和分布式解决方案。本项目中提出的概念将应用于两个具体的传感领域:(1)四旋翼传感机器人传感环境数据的网络;(2)用于生物分子系统动力学三维成像的共聚焦荧光显微镜。这两个应用领域具有完全不同的长度和时间尺度、动态特性和信息内容。将本项目中提出的想法成功地应用于这两个领域,将证明压缩机器人传感系统概念的普遍性。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Scheduling Multiple Agents in a Persistent Monitoring Task Using Reachability Analysis
使用可达性分析在持久监控任务中调度多个代理
- DOI:10.1109/tac.2019.2922506
- 发表时间:2019
- 期刊:
- 影响因子:6.8
- 作者:Yu, Xi;Andersson, Sean B.;Zhou, Nan;Cassandras, Christos G.
- 通讯作者:Cassandras, Christos G.
Optimal Threshold-Based Distributed Control Policies for Persistent Monitoring on Graphs
- DOI:10.23919/acc.2019.8814440
- 发表时间:2019-07
- 期刊:
- 影响因子:0
- 作者:Nan Zhou;C. Cassandras;Xi Yu;S. Andersson
- 通讯作者:Nan Zhou;C. Cassandras;Xi Yu;S. Andersson
Reconstruction of ultrasound signals using randomly acquired samples in a sparse environment
在稀疏环境中使用随机采集的样本重建超声信号
- DOI:
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Pinto, Samuel;Sanchez, Sean R;Doran, Liam;Ryan, Aidan;Andersson, Sean B
- 通讯作者:Andersson, Sean B
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Sean Andersson其他文献
Underwater robots: Motion and force control of vehicle manipulator systems, Gianluca Antonelli (Ed.); Springer, Berlin, Heidelberg, 2003, ISBN: 3-540-00054-2
- DOI:
10.1016/j.automatica.2005.10.003 - 发表时间:
2006-02-01 - 期刊:
- 影响因子:
- 作者:
Sean Andersson - 通讯作者:
Sean Andersson
Sean Andersson的其他文献
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{{ truncateString('Sean Andersson', 18)}}的其他基金
Decentralized optimal control of cooperating networked multi-agent systems
协作网络多智能体系统的分散最优控制
- 批准号:
1931600 - 财政年份:2019
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: Dynamic Control and Separation of Microparticles in Fluids using Optical Whispering Gallery Mode Resonant Forces
合作研究:利用光学回音壁模式共振力动态控制和分离流体中的微粒
- 批准号:
1661586 - 财政年份:2017
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Detection and Tracking of Multiple Dynamic Targets with Cooperating Networked Agents
通过协作网络代理检测和跟踪多个动态目标
- 批准号:
1509084 - 财政年份:2015
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
IDBR: Type A: Collaborative research: High-speed AFM imaging of dynamics on biopolymers through non-raster scanning
IDBR:A 型:合作研究:通过非光栅扫描对生物聚合物动力学进行高速 AFM 成像
- 批准号:
1352729 - 财政年份:2014
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
Collaborative Research: High-Speed AFM through Compressed Sensing
合作研究:通过压缩感知实现高速 AFM
- 批准号:
1234845 - 财政年份:2012
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
CAREER: Nonlinear Control for Single Molecule Tracking
职业:单分子追踪的非线性控制
- 批准号:
0845742 - 财政年份:2009
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
DynSyst_Special_Topics: A formal approach to the control of stochastic dynamic systems
DynSyst_Special_Topics:随机动态系统控制的形式化方法
- 批准号:
0928776 - 财政年份:2009
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
IDBR: Simultaneous Tracking of Multiple Particles in Confocal Microscopy
IDBR:在共焦显微镜中同时跟踪多个粒子
- 批准号:
0649823 - 财政年份:2007
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
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