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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，例如紧急响应和搜救。然而，对于一些应用来说，部署大型机器人的成本可能太高，小型机器人是首选。这种小型机器人的一个共同问题是在存在不确定性的情况下可靠导航。目前的机器人建模和控制方法不能捕捉到小尺度不确定性的影响所带来的复杂性。此外，较小的尺寸限制了传感器和计算有效载荷，从而限制了机器人的感知和控制能力。这个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的学生多样性，这是一家为拉美裔服务的机构，以扩大代表不足的群体的参与。本项目研究机器人-环境相互作用中的不确定性影响机器人行为的机制。小型机器人的运动更具随机性，因为执行器的误差和与环境的不确定相互作用会放大姿态误差。其目标是引入一个平台无关的、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来实现不确定情况下的可靠机器人导航。其具体目标是：1)利用有限的数据提取动态特性，用于不确定系统的建模；2)基于导出的降阶模型综合不确定性感知的基于模型的控制器；3)用空中、地面和海洋机器人测试和验证理论分析和导出的模型和控制算法。谱方法被用来提取时空动力学和量化不确定性。一种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。虽然这项研究中制定的基本原则是基于小型机器人的，但该项目的发现可能会推广到感知有限和噪音驱动的大型机器人。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进行评估，被认为值得支持。

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）

Online Dynamics Learning for Predictive Control with an Application to Aerial Robots

用于预测控制的在线动力学学习及其在空中机器人中的应用

DOI：
发表时间：
2023
期刊：
Proceedings of The 6th Conference on Robot Learning
影响因子：
0
作者：
Jiahao, Tom Z.;Chee, Kong Yao;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

DOI：
10.1109/iros55552.2023.10341774
发表时间：
2023-08
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：
K. Y. Chee;T. Silva;M. A. Hsieh;George Pappas

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