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

由于无人驾驶飞行器（uav）的重大技术进步，在过去几年中，无人机在民用和商业应用中出现了爆炸式增长。为了确保在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，能够快速适应动态变化的环境，能够适应极端环境条件，并能够安全抵御数据污染和恶意攻击。拟议研究的跨学科性质将为不同群体的学生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著推进nexg无线通信、深度机器学习、硬件感知模型生成和健壮可靠的人工智能方面的最新突破，以实现智能和弹性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与多模态数据融合和机器学习辅助快速处理全球状态跟踪；(b)具有高度可扩展计算和灵活延迟容忍的多智能体分散强化学习（RL）框架；(c)基于深度学习的消息传递，实现高效的通信和强大的硬件感知神经架构搜索，实现高效的机载计算；(d)全面的鲁棒性和安全性设计，以保护系统免受离群数据，恶意中毒攻击和RL系统攻击。该项目还将进行广泛的性能评估，以验证开发的方法和算法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.