S&AS: INT: COLLAB: Aerodynamic Intelligent Morphing System (A-IMS) for Autonomous Smart Utility Truck Safety and Productivity in Severe Environments

基本信息

批准号：
1849198
负责人：
Kyriakos G Vamvoudakis
金额：
$ 32.5万
依托单位：
Georgia Tech Research Corporation
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：
2019-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849198&HistoricalAwards=false
关键词：
S&amp INT COLLAB Aerodynamic Intelligent

项目摘要

Utility trucks are the first responders in areas of extreme weather situations for tasks such as rescuing people from disaster areas, cutting trees to restore traffic, and repairing electric posts and restoring power. This study establishes a scientific framework for maintaining the productivity and safety of emergency response vehicles while eliminating accidents. This is implemented via a novel integrated framework to monitor and predict weather conditions and feed that information into intelligent mechanisms that autonomously shape the aerodynamic surfaces of utility trucks. The project includes recruitment efforts and activities to integrate high-school students, as well as students from multiple cultures, and disciplines into autonomy research. This project is expected to contribute new scientific knowledge and engineering techniques for next generation transportation infrastructure resiliency, and to facilitate economic growth in the state of Alabama.Unlike conventional approaches, the A-IMS will integrate model-free shape-morphing learning mechanisms with model-based interactive design to manage air-fluid flows, based on the road conditions, meteorology, speed limit, wind speed, and direction. This potentially transformative framework for A-IMS will: (1) bring new perspectives of learning to enhance the adaptability and intelligence in natural-engineering systems that leverage physical and information processes; (2) establish an integrated design framework for hazardous environments to achieve resilience, and productivity through integrated adaptation of morphological properties while also mitigating the effects of potentially adversarial learning agents that can exist in the cloud; (3) investigate the interactive physical components of the A-IMS, that will simultaneously operate in two different mediums of multi-phase fluids, and solids (i.e., the air/fluid and road). The A-IMS framework will be evaluated through hardware/software implementation, as well as in real-world conditions in the unique test conditions available at Wall of Wind at Florida International University. The project's education and outreach component include integrated research and education plans that will lead to technology transfer and summer camps with a special focus on reaching out to underrepresented minorities and women.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.

多用途卡车是极端天气地区的第一响应者，执行从灾区救援人员、砍伐树木以恢复交通、修复电线杆和恢复电力等任务。这项研究建立了一个科学框架，用于维持应急响应车辆的生产力和安全性，同时消除事故。这是通过一个新颖的集成框架来实现的，该框架可以监测和预测天气状况，并将该信息输入到自动塑造多用途卡车空气动力学表面的智能机制中。该项目包括招募工作和活动，将高中生以及来自多种文化和学科的学生纳入自主研究。该项目预计将为下一代交通基础设施弹性贡献新的科学知识和工程技术，并促进阿拉巴马州的经济增长。与传统方法不同，A-IMS 将无模型形状变形学习机制与模型相结合基于交互设计，根据路况、气象、限速、风速和风向来管理气液流动。这种潜在的 A-IMS 变革框架将：（1）带来新的学习视角，以增强利用物理和信息过程的自然工程系统的适应性和智能；（2）建立针对危险环境的综合设计框架，通过形态属性的综合适应来实现弹性和生产力，同时减轻云中可能存在的潜在对抗性学习代理的影响； (3) 研究 A-IMS 的交互物理组件，该组件将同时在多相流体和固体两种不同介质（即空气/流体和道路）中运行。 A-IMS 框架将通过硬件/软件实施以及佛罗里达国际大学风之墙提供的独特测试条件在现实条件下进行评估。该项目的教育和外展部分包括综合研究和教育计划，这些计划将导致技术转让和夏令营，特别关注代表性不足的少数族裔和妇女。该奖项反映了 NSF 的法定使命，并通过使用评估结果被认为值得支持。基金会的智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量（35）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Intermittent Learning Through Operant Conditioning for Cyber-Physical Systems

通过网络物理系统的操作性调节进行间歇性学习

DOI：
10.1109/tnnls.2021.3111826
发表时间：
2021-10-04
期刊：
IEEE Transactions on Neural Networks and Learning Systems
影响因子：
10.4
作者：
P. P. Sahoo;Aris Kanellopoulos;K. Vamvoudakis
通讯作者：
K. Vamvoudakis

Game Theory for Autonomy: From Min-Max Optimization to Equilibrium and Bounded Rationality Learning

自主博弈论：从最小-最大优化到均衡和有限理性学习

DOI：
10.23919/acc55779.2023.10156432
发表时间：
2023-05-31
期刊：
2023 American Control Conference (ACC)
影响因子：
0
作者：
K. Vamvoudakis;Filippos Fotiadis;J. Hespanha;Raphael Chinchilla;Guosong Yang;Mushuang Liu;J. Shamma;Lacra Pavel
通讯作者：
Lacra Pavel

On-Off Adversarially Robust Q-Learning

开-关对抗性鲁棒 Q-Learning

DOI：
10.1109/lcsys.2020.2979572
发表时间：
2020-03-10
期刊：
IEEE Control Systems Letters
影响因子：
3
作者：
P. P. Sahoo;K. Vamvoudakis
通讯作者：
K. Vamvoudakis

Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation

通过核化 Lipschitz 估计进行安全近似动态规划

DOI：
10.1109/tnnls.2020.2978805
发表时间：
2019-07-03
期刊：
IEEE Transactions on Neural Networks and Learning Systems
影响因子：
10.4
作者：
A. Chakrabarty;Devesh K. Jha;G. Buzzard;Yebin Wang;K. Vamvoudakis
通讯作者：
K. Vamvoudakis

Non-Equilibrium Learning in Stochastic Games

随机博弈中的非平衡学习

DOI：
10.23919/acc55779.2023.10156061
发表时间：
2023-05
期刊：
2023 American Control Conference (ACC
影响因子：
0
作者：
Vamvoudakis; Kyriakos G.
通讯作者：
Kyriakos G.

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Kyriakos G Vamvoudakis其他文献

Dynamic Intermittent Feedback Design for H∞ Containment Control on a Directed Graph

有向图上 H 遏制控制的动态间歇反馈设计

DOI：
10.1109/tcyb.2019.2933736
发表时间：
2020
期刊：
IEEE Transactions on Cybernetics
影响因子：
11.8
作者：
Yongliang Yang;Hamidreza Modares;Kyriakos G Vamvoudakis;Yixin Yin;Donald C Wunsch
通讯作者：
Donald C Wunsch