Collaborative Research: CPS: Medium: Enabling Autonomous, Persistent, and Adaptive Mobile Observational Networks Through Energy-Aware Dynamic Coverage

这项研究将创建并验证优化管理移动观测网络的新方法，该网络由可再生动力的“主机”代理和从主机部署并由主机充电的“卫星”代理组成。这样的网络可以实现对气象、气候变化、侦察和监视应用的自主、长期测量，这是具有重大国家利益的。虽然存在这种网络的硬件，但绝大多数现有的任务规划和控制方法将能源视为有限资源，并将重点放在有限持续时间的任务上。这项研究将代表一种范式转变，其中网络可用的能源是可再生的，但即时可用的电力是有限的。这需要不断权衡能量收集和科学信息收集的策略。本研究将建立一个管理上述权衡的综合框架，包括基于模拟和实验的演示。在这项工作中考虑的具体观测框架将涉及太阳能自主水面舰艇，无人驾驶的飞行器和水下滑翔机，以表征北卡罗莱纳州外滩附近深海和近岸水域之间的大气和海洋相互作用。该研究将辅以有针对性的实习活动，在北卡州立大学工程场所的K-12外展活动，以及与底特律地区大学预科工程项目的外展活动。在观测网络中融合自主性、持久性和适应性需要在网络信息量和物理能量量之间进行正式表征和权衡。具体来说，使用可再生能源驱动的主机代理，能量不再是硬约束；相反，在随机环境中，信息获取和可用机载能量的使用之间存在着永久的权衡。为了解决这个问题，研究团队将创建：(i)一个科学定制的信息表征动态覆盖模型，（ii）一个统计能源资源/消耗模型，以及（iii）一个基于信息/能源权衡调整任务概况的多级预测控制器。主控制器将最大化一个由两部分目标函数组成的目标函数，该目标函数由有限地平线覆盖总和和基于称为“能量信息价值”的新数量的终端激励组成。该主控制器将由一系列卫星能量感知覆盖控制器进行补充，这些覆盖控制器最大限度地满足随机资源中的安全交会要求。该研究将在三个日益复杂的平台上进行验证——无人飞行器（UAV）网络（实验）、太阳能自主水面舰艇(ASV)/无人机联合网络（实验）和ASV/USV/水下滑翔机联合网络（模拟驱动）。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。

This research will create and validate new approaches for optimally managing mobile observational networks consisting of a renewably powered “host” agent and “satellite” agents that are deployed from and recharged by the host. Such networks can enable autonomous, long-term measurements for meteorological, climate change, reconnaissance, and surveillance applications, which are of significant national interest. While the hardware exists for such networks, the vast majority of existing mission planning and control approaches treat energy as a finite resource and focus on finite-duration missions. This research will represent a paradigm shift, wherein the energy resource available to the network is renewable, but the instantaneously available power is limited. This demands strategies that continuously trade off energy harvesting and scientific information gathering. This research will establish a comprehensive framework for managing the aforementioned tradeoffs, with both simulation-based and experimental demonstrations. The specific observational framework considered in this work will involve a fleet of solar-powered autonomous surface vessels, unoccupied aerial vehicles, and undersea gliders to for characterizing atmospheric and oceanic interactions between the deep-ocean and near-shore waters adjacent to North Carolina’s Outer Banks. The research will be complemented with targeted internship activities, K-12 outreach activities at The Engineering Place at NC State, and outreach activities with the Detroit Area Pre-College Engineering Program.Fusing autonomy, persistence, and adaptation in observational networks demands a formal characterization and tradeoff between the cyber quantity of information and physical quantity of energy. Specifically, with a renewably powered host agent, energy no longer serves as a hard constraint; instead, there exists a perpetual tradeoff between the acquisition of information and the use of available on-board energy in a stochastic environment. To address this, the research team will create: (i) a scientifically tailored dynamic coverage model for information characterization, (ii) a statistical energy resource/consumption model, and (iii) a multi-level predictive controller that adapts the mission profile based on the information/energy tradeoff. The host controller will maximize a two-part objective function consisting of a finite-horizon coverage summation and terminal incentive based on a novel quantity termed the “information value of energy.” This host controller will be complemented by a series of satellite energy-aware coverage controllers that maximize coverage subject to a safe rendezvous requirement in a stochastic resource. The research will be validated across three platforms of increasing complexity – an unoccupied aerial vehicle (UAV) network (experimental), a combined solar-powered autonomous surface vessel (ASV)/UAV network (experimental), and a combined ASV/USV/undersea glider network (simulation-driven).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.