RINGS: A Deep Reinforcement Learning Enabled Large-scale UAV Network with Distributed Navigation, Mobility Control, and Resilience

RINGS：深度强化学习支持的大规模无人机网络，具有分布式导航、移动控制和弹性

基本信息

批准号：
2148253
负责人：
Yingbin Liang
金额：
$ 100万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148253&HistoricalAwards=false
关键词：
RINGS Deep Reinforcement Learning Enabled

项目摘要

Thanks to the significant technological advances in unmanned aerial vehicles (UAVs), the past few years have witnessed an explosive growth of UAVs in civilian and commercial applications. To ensure efficient and reliable auto-navigation and planning in these applications, there is an urgent need to develop the foundational technology that enables UAVs to have strong sensing, communications, and on-board computing capabilities, to adapt rapidly to the dynamically changing environment, to be resilient to extreme environmental conditions, and to be secure against data contamination and malicious attacks. The interdisciplinary nature of the proposed research will provide valuable research opportunities and hands-on projects for a diverse group of students.The goal of this project is to leverage and significantly advance the recent breakthroughs in NextG wireless communications, deep machine learning, hardware-aware model generation, and robust and trustworthy artificial intelligence, to enable the design of an intelligent and resilient UAV navigation and planning system. More specifically, this project will develop: (a) real-time communication assisted ambient sensing with multi-modality data fusion and machine learning assisted fast processing for global state tracking; (b) a multi-agent decentralized reinforcement learning (RL) framework with highly scalable computations and flexible latency tolerance; (c) deep learning based message passing for efficient communication and powerful hardware-aware neural architecture search for efficient on-board computation; and (d) comprehensive robustness and security design for system protection from outlier data, malicious poisoning attacks, and RL system attacks. The project will also conduct extensive performance evaluations to validate the developed approaches and algorithms.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.

由于无人机（UAV）技术的显着进步，过去几年见证了无人机在民用和商业应用中的爆炸性增长。为了确保这些应用中高效可靠的自动导航和规划，迫切需要开发基础技术，使无人机具有强大的传感、通信和机载计算能力，以快速适应动态变化的环境，能够适应极端环境条件，并防止数据污染和恶意攻击。拟议研究的跨学科性质将为不同的学生群体提供宝贵的研究机会和实践项目。该项目的目标是利用并显着推进 NextG 无线通信、深度机器学习、硬件感知方面的最新突破模型生成以及强大且值得信赖的人工智能，以实现智能且有弹性的无人机导航和规划系统的设计。更具体地说，该项目将开发：（a）实时通信辅助环境传感，具有多模态数据融合和机器学习辅助快速处理，以进行全局状态跟踪； (b) 具有高度可扩展计算和灵活延迟容忍度的多智能体去中心化强化学习（RL）框架； (c) 基于深度学习的消息传递以实现高效通信，以及强大的硬件感知神经架构搜索以实现高效的机载计算； (d) 全面的稳健性和安全性设计，可保护系统免受异常数据、恶意中毒攻击和强化学习系统攻击。该项目还将进行广泛的绩效评估，以验证所开发的方法和算法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。