RECUV

RECUV focuses on the design of new UAS for communication and sensing applications. Examples include the Tempest UAS for studying severe storms and the AresMax which integrates a wireless communication antenna into the aircraft wing.

RECUV deploys delay-tolerant, ad-hoc protocols that allow unmanned aircraft systems (UAS) to operate in stressed or fractured networks. Cognitive radios are being developed that scan the RF spectrum to select frequencies with the best performance.

Teams of cooperating UAS can perform missions better, faster, and more efficiently than larger single vehicle systems. RECUV focuses on net-centric architectures and algorithms for autonomous control of multiple small unmanned aircraft.

Payloads that have been integrated into UAS by RECUV include a laser altimeter, synthetic aperture radar, wing-integrated antenna, IP-based video camera, ELINT package, atmospheric sensors, phased array antenna, dropsonde, and gimbaled camera.

This area combines work in networked unmanned systems, cooperative control, controlled mobility in ad hoc networks, and optimal distributed sensing to develop heterogeneous robot sensor networks for in situ science applications.

Safety regulations designed by the FAA for large aircraft in populated areas are not appropriate for many UAS applications. RECUV works with the FAA to help them characterize UAS operation and to develop new safety technology for the aviation sector.

A key challenge for unmanned aircraft is to have sufficient radio spectrum for command, control, and communications. Though all radio spectrum is allocated, much of it goes unused at any time. This work investigates cognitive radio technologies that allow aircraft to exploit unused spectrum bands while protecting spectrum access by incumbant radio users.

The University of Colorado and Brigham Young University are establishing an Industry/University Cooperative Research Center for Unmanned Aircraft Systems to address the issues common to the UAS industry that limit widespread application across military, civil, and commercial domains.

The objective of this project is to develop fundamental understanding of control strategies that can exploit the complimentary computation, sensing, and communication capabilities of the members of a mothership/daughtership robotic sensor network performing in situ volumetric sensing.

Soil moisture is fundamental to land surface hydrology, so global distribution and temporal variations of soil moisture are needed both for analyses and modeling purposes. To date, a global soil moisture dataset that fulfills the needs of the hydrology and climate communities does not exist: in situ measurements are sparse and useful satellite-derived methods are still being developed. My group has shown that GPS receivers can be used to estimate fluctuations in near surface soil moisture over an area of ~1000 sq. meters. We are validating this method to provide near-real time estimates of soil moisture for hydrology, climate, and ecology studies.

Snow is an important component of the climate system and a critical storage component in the hydrologic cycle. However, in situ observations of snow distribution are sparse, and remotely sensed products are imprecise and only available at a coarse spatial scale.In collaboration with Eric Small (CU Geological Sciences) and Mark Williams (CU INSTAAR), my group is validating GPS ground receivers to measure snow depth. We have demonstrated that GPS receivers installed in the Rocky Mountains to measure plate tectonics can be used in the winter to simultaneously measure snow depth.

Fully exploiting aerial communication and surveillance networks requires holistic, autonomous decision-making that reasons about the joint effects of sensing, mobility, and communication on information-gathering tasks. Coordinated, distributed, hierarchical planning algorithms are developed for optimizing the collection and dissemination of information in connected networks and for generating aircraft trajectories that physically carry and deliver data in stressed or fractured networks.

MIZOPEX will employ UAS to assess ocean and ice variability during the melt season, exploiting unique capabilities of multiple classes of unmanned systems (the NASA Ikhana, Insitu ScanEagles, and a CU microUAS) combined with UAS- and ship-deployed buoys and satellite observations. Flights will take place over the Beaufort Sea in summer 2013. The measurement strategy has three basic aspects: extensive airborne surface mapping repeated frequently over large areas for comparisons with satellite-derived sea surface temperature and sea ice data sets; sustained observations of ocean surface and subsurface conditions over 10's of hours; and repeated visitation to locations within the ice pack, allowing Lagrangian observations over a period of weeks to assess how specific portions of the ice pack and open ocean evolve over summer.

This project develops a framework for the control of robot sensor networks that integrates sensing, communication, and actuation into a single approach based on a new concept of quality of service of information. By controlling the mobility and communication of some nodes in the system, quality of service of information, in the form of probabilistic performance guarantees based on information-theoretic metrics, can be achieved.