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).