Modeling, Identification, and Estimation of Distributed Parameter Systems Using Mobile Sensor Networks

使用移动传感器网络对分布式参数系统进行建模、识别和估计

• 批准号: 1917300
• 负责人: Wencen Wu
• 金额: $ 29.82万
• 依托单位: San Jose State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917300&HistoricalAwards=false
• 关键词: Modeling; Identification; Estimation; Distributed; Parameter

This project will formulate a general purpose mathematical framework using mobile sensor networks (MSNs) that will allow an efficient and accurate prediction of information about behavior of complex real world systems such as weather forecasting, wildfire control, disaster recovery, explosive materials detection, etc. Such systems are known to be distributed parameter systems (DPS) and modeling of such systems and accurately predicting their behavior in real-time is highly complex and computationally challenging. The current state-of-the-art techniques use static sensor network to help obtain solutions from complex mathematical models which are often inaccurate and cannot provide timely information. The mobility and adaptability of MSNs make them great candidates for overcoming these challenges. This project will develop a unified framework for modeling, identifying, estimating and predicting behavior of such distributed parameter systems. The project will also develop a strongly integrated research, educational, and outreach program by providing graduate students with interdisciplinary and challenging research experiences, by providing undergraduate students with the opportunity of early involvement in research activities through algorithm development and test-bed experiments, and by motivating K-12 students by giving them hands-on experiences through university's Engineering Ambassadors(EA) program.The outcomes of this project are expected to advance the modeling, identification, and state estimation techniques for distributed parameter systems, offer comprehensive scientific understanding of the connections between DPS and mobile sensor networks, and contribute to generic engineering principles for designing cooperative control and distributed sensing strategies for mobile sensor networks. In particular, the work will: develop novel approaches for online parameter identification and state estimation of DPS using mobile sensor networks with reduced computational and communication cost compared to existing methods developed for static sensor networks; determine information-rich and energy-saving optimal trajectories of mobile sensor networks moving in DPS for simultaneously system identification and state prediction; and design a multi-robot test-bed with controllable advection-diffusion fields for the validation of the strategies, and conduct field experiments to test the strategies under realistic uncertainties and variations. The project also offers great educational opportunities for graduate, undergraduate, as well as K-12 students.

该项目将使用移动传感器网络（MSN）制定通用数学框架，该框架将允许高效准确地预测复杂现实世界系统的行为信息，例如天气预报、野火控制、灾难恢复、爆炸材料检测等。此类系统被称为分布式参数系统（DPS），对此类系统进行建模并实时准确预测其行为非常复杂且计算上具有挑战性。当前最先进的技术使用静态传感器网络来帮助从复杂的数学模型中获得解决方案，这些模型通常不准确并且无法提供及时的信息。 MSN 的移动性和适应性使其成为克服这些挑战的最佳选择。该项目将开发一个统一的框架，用于建模、识别、估计和预测此类分布式参数系统的行为。该项目还将开发一个高度综合的研究、教育和推广计划，为研究生提供跨学科和具有挑战性的研究经验，为本科生提供通过算法开发和测试台实验早期参与研究活动的机会，并通过通过大学的工程大使 (EA) 计划为 K-12 学生提供实践经验，从而激励他们。该项目的成果预计将推进分布式参数系统的建模、识别和状态估计技术，提供对分布式参数系统的全面科学理解DPS 和移动传感器网络之间的连接，并为设计移动传感器网络的协作控制和分布式传感策略的通用工程原理做出贡献。具体来说，这项工作将：开发使用移动传感器网络进行 DPS 在线参数识别和状态估计的新方法，与为静态传感器网络开发的现有方法相比，减少计算和通信成本；确定移动传感器网络在 DPS 中移动的信息丰富且节能的最佳轨迹，以同时进行系统识别和状态预测；设计具有可控平流扩散场的多机器人试验台来验证策略，并进行现场实验以在现实的不确定性和变化下测试策略。该项目还为研究生、本科生以及 K-12 学生提供良好的教育机会。

项目成果

Design and Implementation of a Small-scale Autonomous Vehicle for Autonomous Parking

• DOI: 10.1109/cacre52464.2021.9501311
• 发表时间: 2021-07
• 期刊: 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE)
• 作者: Tianhao Yu;W. Lu;Yanshen Luo;Chenguang Niu;Wencen Wu

Cooperative Level Curve Tracking in Advection-Diffusion Fields

• DOI: 10.1109/cacre50138.2020.9230059
• 发表时间: 2020-09
• 期刊: 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE)
• 作者: Sachin Guruswamy;Wencen Wu

Parameter Identification of Spatial–Temporal Varying Processes by a Multi-Robot System in Realistic Diffusion Fields

真实扩散场中多机器人系统时空变化过程的参数识别

• DOI: 10.1017/s0263574720000788
• 发表时间: 2021
• 期刊: Robotica
• 影响因子: 2.7
• 作者: Wu, Wencen;You, Jie;Zhang, Yufei;Li, Mingchen;Su, Kun
• 通讯作者: Su, Kun

LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots

• DOI: 10.1115/detc2021-68554
• 发表时间: 2021-08
• 期刊: Volume 7: 17th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)
• 作者: Kunj J. Parikh;Wencen Wu

Reinforcement Learning for Multi-robot Field Coverage Based on Local Observation

基于局部观测的多机器人领域覆盖强化学习

• DOI: 10.1109/sose50414.2020.9130535
• 发表时间: 2020
• 期刊: 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE
• 作者: Zhu, Matthew;Simon, Dennis;Rajpurohit, Nachiketa;Kalathia, Sagar Jayantkumar;Wu, Wencen
• 通讯作者: Wu, Wencen