RI: Small: Collaborative Research: Extracting Dynamics from Limited Data for Modeling and Control of Unmanned Autonomous Systems

RI：小型：协作研究：从有限数据中提取动力学，用于无人自主系统的建模和控制

基本信息

批准号：
1910308
负责人：
Mongying Hsieh
金额：
$ 23万
依托单位：
University of Pennsylvania
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910308&HistoricalAwards=false
关键词：
RI Small Collaborative Research Extracting

项目摘要

Autonomous robots can contribute to many real-world applications, such as emergency response and search-and-rescue. For some applications, however, the cost of deploying large robots may be too high, and small robots are preferred. A common issue with such small robots is navigating reliably in the presence of uncertainty. Current robot modeling and control approaches cannot capture the intricacies imposed by the effect of uncertainty at small scales. In addition, the small size restricts sensor and computational payloads, which limit the robot's perceptual and control capabilities. This project introduces a data-driven modeling framework to quantify and exploit uncertainty via control for reliable navigation of small robots. The project enables undergraduate students to become involved in research, and capitalizes on the student diversity at UC Riverside, a Hispanic-serving Institution, to broaden participation of under-represented groups. This project investigates the mechanisms that uncertainties in robot-environment interactions affect robot behavior. Small robot motion is more stochastic since errors at the actuators and uncertain interactions with the environment amplify errors in pose. The goal is to introduce a platform-agnostic, data-driven modeling framework to quantify uncertainty and subsequently exploit it via control for reliable robot navigation under uncertainty. The specific aims are to: 1) extract dynamics using limited data for modeling uncertain systems; 2) synthesize uncertainty-aware model-based controllers based on derived reduced-order models; and 3) test and validate theoretical analysis and derived models and control algorithms with aerial, ground, and marine robots. Spectral methods are used to extract spatio-temporal dynamics and to quantify uncertainty. A model-reference adaptive control scheme utilizes extracted dynamics and uncertainty for reliable robot navigation. While the basic principles developed in this research are grounded on small robots, this project's findings may generalize to larger robots with limited sensing and noisy actuation.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.

自主机器人可以为许多现实世界的应用做出贡献，例如应急响应和搜救。然而，对于某些应用，部署大型机器人的成本可能太高，因此首选小型机器人。此类小型机器人的一个常见问题是在存在不确定性的情况下可靠地导航。当前的机器人建模和控制方法无法捕捉小规模不确定性影响所带来的复杂性。此外，小尺寸限制了传感器和计算负载，从而限制了机器人的感知和控制能力。该项目引入了一个数据驱动的建模框架，通过控制小型机器人的可靠导航来量化和利用不确定性。该项目使本科生能够参与研究，并利用加州大学河滨分校（一所西班牙裔服务机构）的学生多样性，扩大代表性不足群体的参与。该项目研究机器人与环境交互的不确定性影响机器人行为的机制。小型机器人的运动更加随机，因为执行器的误差和与环境的不确定交互会放大姿态误差。目标是引入一个与平台无关、数据驱动的建模框架来量化不确定性，并随后通过控制来利用它，以在不确定性下实现可靠的机器人导航。具体目标是： 1）使用有限的数据提取动力学以对不确定系统进行建模； 2）基于导出的降阶模型综合基于不确定性感知模型的控制器； 3) 使用空中、地面和海洋机器人测试和验证理论分析以及衍生模型和控制算法。谱方法用于提取时空动态并量化不确定性。模型参考自适应控制方案利用提取的动力学和不确定性来实现可靠的机器人导航。虽然本研究中开发的基本原理是基于小型机器人，但该项目的研究结果可以推广到传感有限和驱动噪音较大的机器人。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Bridging the gap: Machine learning to resolve improperly modeled dynamics

弥合差距：机器学习解决建模不当的动态问题

DOI：
10.1016/j.physd.2020.132736
发表时间：
2020-12
期刊：
Physica D: Nonlinear Phenomena
影响因子：
0
作者：
Qraitem, Maan;Kularatne, Dhanushka;Forgoston, Eric;Hsieh, M. Ani
通讯作者：
Hsieh, M. Ani

KNODE-MPC: A Knowledge-Based Data-Driven Predictive Control Framework for Aerial Robots

KNODE-MPC：基于知识的数据驱动的空中机器人预测控制框架

DOI：
10.1109/lra.2022.3144787
发表时间：
2021-09-10
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
K. Y. Chee;Tom Z. Jiahao;M. A. Hsieh
通讯作者：
M. A. Hsieh

Online Estimation of the Koopman Operator Using Fourier Features

使用傅里叶特征的库普曼算子在线估计

DOI：
发表时间：
2023-06
期刊：
Proceedings of The 5th Annual Learning for Dynamics and Control Conference
影响因子：
0
作者：
Salam, Tahiya;Li, Alice Kate;Hsieh, M. Ani
通讯作者：
Hsieh, M. Ani

Enhancing Sample Efficiency and Uncertainty Compensation in Learning-Based Model Predictive Control for Aerial Robots

提高空中机器人基于学习的模型预测控制的样本效率和不确定性补偿

DOI：
10.1109/iros55552.2023.10341774
发表时间：
2023-08-01
期刊：
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
K. Y. Chee;T. Silva;M. A. Hsieh;George Pappas
通讯作者：
George Pappas

NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration

NODEO：一种基于神经常微分方程的可变形图像配准优化框架