MLWiNS: Ultra-Reliable Collaborative Computing for Autonomous Unmanned Aerial Vehicles

无人驾驶汽车（UAV）预计将在城市和基础设施监测、精准农业和送货服务等许多重要应用中发挥核心作用。然而，使这些机载平台的操作自主需要执行用于周围环境的实时分析和使命规划的算法。这些算法的复杂性与无人机的固有约束相冲突，无人机的嵌入式系统具有有限的计算能力和能源供应。拟议的研究旨在开发技术，使分布式计算超可靠的无人机系统的背景下。该项目建立了一个智能层，实时控制信息如何在系统各层之间传播和处理，将感官输入转换为决策，以及语义形式的神经压缩，以显着减少通过弱无线链路传输的数据量。该项目将在教育、辅导和外联方面产生广泛影响。这项研究工作的影响将通过向大学和高中学生提供独特的外联和教育机会来扩大。拟议的项目从两个互补的角度处理这个问题：（1）设计深度强化学习（RL）代理，其学习跨噪声信道最佳地传送数据，以及（2），构建基于概率深度学习的新型有损压缩算法，这些算法专为分布式机器学习而设计，无需人工参与。对于（1），关键挑战之一在于驱动系统动力学的随机过程的多尺度性质，这些随机过程在不同尺度上呈现重要的地理和时间趋势。一种创新的分层学习方法将被用来使RL代理有效的系统动态演变跨越时间和空间。对于（2），将设计基于可变自动编码器（VAE）的新型极端神经有损压缩算法。而不是专注于信号重建，由此产生的压缩器将旨在只保留指定的监督学习任务所需的相关信息，导致监督（或语义）压缩的新范式。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

Unmanned Autonomous Vehicles (UAV) are expected to play a central role in many applications of great interest, including urban and infrastructure monitoring, precision agriculture and delivery services. However, making the operations of these airborne platforms autonomous requires the execution of algorithms for the real-time analysis of the surrounding environment and mission planning. The complexity of these algorithms clashes with the inherent constraints of UAVs, whose embedded systems have limited computing power and energy supply. The proposed research aims at the development of techniques to make distributed computing ultra reliable in the context of UAV systems. The project establishes a layer of intelligence that controls in real-time how information is propagated and processed across the layers of the systems to transform sensorial input into decisions, as well as a semantic form of neural compression to significantly reduce the amount of data transported over weak wireless links. This project will have a broad impact in terms of education, mentorship and outreach. The impact of this research effort will be broadened by providing unique outreach and educational opportunities to students at the collegiate and high school level.The proposed project approaches the problem from two complementary angles: (1) designing deep reinforcement learning (RL) agents that learn to optimally communicate data across noisy channels, and (2), building novel lossy compression algorithms based on probabilistic deep learning that are specifically designed for distributed machine learning without a human in the loop. For (1), one of the key challenges resides in the multi-scale nature of the stochastic processes driving the system dynamics that present important geographical and temporal trends at different scales. An innovative hierarchical learning approach will be used to make the RL agent effective as the system dynamics evolve across time and space. For (2), novel kinds of extreme neural lossy compression algorithms based on Variational AutoEncoders (VAE) with additional supervision will be designed. Instead of focusing on signal reconstruction, the resulting compressor will aim at preserving only relevant information needed for a specified supervised learning task, leading to the new paradigm of supervised (or semantic) compression.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.