NRI: A Model based Approach to Distributed Adaptive Sampling of Spatio-Temporally Varying Fields

NRI：基于模型的时空变化场分布式自适应采样方法

• 批准号: 1637889
• 负责人: Suman Chakravorty
• 金额: $ 50万
• 依托单位: Texas A&M Engineering Experiment Station
• 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: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637889&HistoricalAwards=false
• 关键词: NRI; Model; based; Approach; Distributed

This research project studies the problem of designing active sensing systems for monitoring dynamically evolving spatial fields using mobile robotic sensor networks. As a particular motivating problem, we consider fields governed by advection-diffusion equations, a model sufficiently general to cover a huge range of important phenomena: from the recent Aliso Canyon gas leak in California and the volcanic ash clouds of Eyjafjallajokull, to the temperature profile within a building. The development of a realistic open-source simulation toolbox for the active sensing problem will allow the assimilation of K-12/undergraduate/graduate students, and high school teachers in projects related to the research, and also allow a broader dissemination of the research to the general public at the annual TAMU Physics and Engineering fair while educating them about the benefits of the project, for instance, in response to a hazardous situation such as a chemical leak or an oil spill.In the current literature, statistical black-boxes (such as Gaussian Processes), which were originally developed for (quasi)-static spatial fields, are being used to model fields with structured temporal dynamics. In this process, two issues which ought to be distinct, the correctness of the model, and considerations of computational efficiency, have become entangled and the consequences can be dangerous: state-of-the-art methods may provide cheap but drastically wrong estimates, along with error bounds that are grossly over-confident when the spatial fields are temporally varying. The investigators will seek to produce adaptive estimation techniques for dynamic spatial fields that are optimal and correct. In particular, randomized model reduction techniques shall be used to attain computational tractability whilst preserving correctness. Further, the project shall seek to develop receding horizon sensor tasking strategies that can drastically outperform greedy strategies in terms of the information content of the estimated field.

本研究项目研究利用移动机器人传感器网络来设计主动传感系统来监测动态变化的空间场的问题。作为一个特殊的激励问题，我们考虑了由平流-扩散方程控制的场，这个模型足够普遍，可以涵盖一系列重要现象：从最近加州Aliso Canyon气体泄漏和Eyjafjallajokull的火山灰云，到建筑物内的温度分布。为主动传感问题开发一个现实的开源模拟工具箱将允许K-12/本科生/研究生和高中教师吸收与研究相关的项目，并允许在一年一度的TAMU物理和工程博览会上向公众更广泛地传播研究，同时教育他们关于该项目的好处，例如，应对危险情况，如化学品泄漏或石油泄漏。在当前的文献中，最初为(准)静态空间场开发的统计黑盒(如高斯过程)正被用于对具有结构化时间动态的场进行建模。在这个过程中，两个本应截然不同的问题--模型的正确性和对计算效率的考虑--已经纠缠在一起，后果可能是危险的：最先进的方法可能提供廉价但严重错误的估计，以及当空间场在时间上变化时过于自信的误差界。研究人员将寻求为动态空间场产生最优和正确的自适应估计技术。特别是，应使用随机化模型简化技术来获得计算可操纵性，同时保持正确性。此外，该项目将寻求开发滚动地平线传感器任务策略，在估计的领域的信息内容方面，这些策略可以大大超过贪婪策略。

项目成果

Efficient distributed state estimation of hidden Markov Models over unreliable networks

不可靠网络上隐马尔可夫模型的高效分布式状态估计

• DOI: 10.1109/mrs.2017.8250939
• 发表时间: 2017
• 期刊: 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS
• 作者: Tamjidi, Amirhossein;Oftadeh, Reza;Chakravorty, Suman;Shell, Dylan
• 通讯作者: Shell, Dylan

MT-LQG: Multi-agent planning in belief space via trajectory-optimized LQG

MT-LQG：通过轨迹优化的 LQG 在置信空间中进行多智能体规划

• DOI: 10.1109/icra.2017.7989658
• 发表时间: 2017
• 期刊: ICRA
• 作者: Rafieisakhaei, Mohammadhussein;Chakravorty, Suman;Kumar, P. R.
• 通讯作者: Kumar, P. R.

Unifying consensus and covariance intersection for decentralized state estimation

• DOI: 10.1109/iros.2016.7759044
• 发表时间: 2016-10
• 期刊: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: A. Tamjidi;S. Chakravorty;Dylan A. Shell