Investigators: Eric Frew (PI), Brian Argrow (CU), Adam Houston (UNL), Chris Weiss (TTU), Volkan Isler (UMN), and Dezhen Song (TAMU);
Sponsor: NSF National Robotics Initiative
This project addresses the development of autonomous self- deploying aerial robotic systems (SDARS) that will enable new in-situ atmospheric science applications through targeted observation. SDARS is comprised of multiple robotic sensors and distributed computing nodes including: 1.) multiple fixed-wing unmanned aircraft, deployable Lagrangian drifters, mobile Doppler radar, mobile command and control stations; 2.) distributed computation nodes in the field and in the lab; 3.) a net-centric middleware connecting the dispersed elements; and 4.) an autonomous decision-making architecture that closes the loop between sensing in the field and new online numerical weather prediction tools. The proposed effort draws on the expertise of the project team in the areas of robotics, unmanned systems, networked control, wireless communication, active sensing, and atmospheric science to realize the vision of bringing cloud robotics to the clouds. Combining autonomous airborne sensors with environmental models dispersed over multiple communication and computation channels enables the collection of information essential for examining the fundamental behavior of atmospheric phenomena.
Autonomous self-deploying aerial robotic systems will close significant capability gaps in conventional platform’s abilities to collect the data necessary to answer a wide range of scientific questions. The motivating application for this work is improvement in the accuracy and lead time of tornado warnings. Other science applications that would benefit from the proposed work include thunderstorm outflows and gust fronts; landfalling hurricane boundary layer dynamics; planetary boundary layer physics studies (including many processes relevant to climate dynamics); atmospheric responses to fires; pollutant dispersion monitoring and forecasting; and terrain-driven circulation systems. Curriculum development, mentoring, and outreach activities will emphasize the complexity of modern engineered systems and their potential societal benefits in order to motivate the next gener- ation of scientists and engineers.
Researchers & Collaborators
Brian Argrow, Professor, Aerospace Engineering Sciences (AES), University of Colorado Boulder
Mission-derived unmanned aircraft systems design, high-speed & hypersonic aerodynamics, dense gas dynamics, rarefied gas dynamics
Eric Frew, Associate Professor, Aerospace Engineering Sciences (AES), University of Colorado Boulder
Networked heterogeneous unmanned aircraft systems, optimal distributed sensing by mobile robots, controlled mobility in ad-hoc sensor networks, miniature self-deploying systems, guidance and control of unmanned aircraft in complex atmospheric phenomena
Adam Houston, Assistant Professor, Earth and Atmospheric Sciences, University of Nebraska Lincoln
Atmospheric convection, mesoscale processes, severe weather, instrumentation, climate diagnostics
Chris Weiss, Associate Professor, Atmospheric Science, Texas Tech University
Severe storm dynamics and tornadogenesis, the initiation and sustenance of deep moist convection (particularly as related to High Plains drylines), radar meteorology
RECUV is a university, government, and industry partnership dedicated to development and application of Unmanned Aircraft Systems (UAS). Facilities include the Mobile Research Collaboratory (MRC), an indoor flying-robot lab, a systems-integration lab, ground-based LIDAR capability, and a fixed-wing and rotary-wing unmanned aircraft fleet integrated with a variety of sensors. RECUV also possesses the most expansive university network of FAA Certificates of Authorization (COAs), allowing UAS operation and research over more than 100,000 square miles of space in CO, OK, KS, TX, NE, WY, and AK.
Energy-Aware Aerial Systems for Persistent Sampling and Surveillance
(Frew, Argrow, Houston, Weiss)
- Goal of this work is to create energy-aware, airborne, dynamic data-driven application systems for persistent sensing in complex atmospheric conditions.
- Exploits wind energy through proactive modeling and planning for autonomous soaring.
- Combines new onboard and remote real-time, wind sensing capabilities; online models for planning that use machine learning techniques for onboard data and dynamic atmospheric models that assimilate Doppler radar data; and a hierarchical guidance and control framework with algorithms that can adapt to environmental, sensing, and computational resources.
- Deployment to Lubbock, TX validated algorithms behavior and full system performance.
Verification of the Origins of Rotation in Tornados Experiment 2 (VORTEX-2)
(NOAA, NSF, and 100 scientists and researchers from around the globe)
- NSF/NOAA-funded tornado research project to explore how, when, and why tornados form
- Incorporated 100 scientists from government and universities around the world to collect weather measurements around and under supercell thunderstorms
- Teams employed 10 mobile radars and over 70 measurement instruments, all equipped with cutting-edge communication and computer technologies
- Partners roamed more than 10,000 miles across the southern and central plains in 2009-2010 and collected data on 11 supercells, including 1 tornadic supercell
- Results from VORTEX-2 data collection will continue to enhance cloud models and storm forecasting well into the future