In support of this effort, a suite of software was developed to allow ... Measurement of wind speed and direction comes directly from the autopilot used in the UA.
Distributed Atmospheric Sensing using Small UAS and Doppler Radar Jack Elston ∗ , Brian Argrow† University of Colorado, Boulder, CO, 80309, USA
Adam Houston‡ , Jamie Lahowetz§ University of Nebraska, Lincoln, NE, 68588, USA
A distributed sensing system has been developed to probe an atmospheric airmass boundary with simultaneous dual-Doppler sensing and in-situ sampling using an Unmanned Vehicle System (UAS). In support of this effort, a suite of software was developed to allow for real time visualization of radar and UA information. Through this interface, controllers were able to effectively control a UA to an area of interest based upon meteorological information. An existing ad-hoc network was augmented to allow for the effective dissemination of telemetry, sensor data, and control throughout the multi-user network. Furthermore, a UA was developed that could carry the various sensors and conduct the required mission. These efforts were verified by flight operations conducted at the Pawnee National Grasslands under CoA 2008-WSA-51.
I.
Introduction
ccording to the National Weather Service, in 2004 (most recent data posted) severe weather caused 369 A fatalities, over 2400 injuries, and $26.8 billion in the United States. These losses could be dramatically reduced with effective advanced prediction and warning systems. Tornadoes are especially violent products 1
of severe storms and thus the study of tornado formation and evolution is a public safety necessity. The inability to determine the volumetric thermodynamic state of the atmosphere between the ground and the base of the mesocyclone remains a major barrier towards a deeper understanding of tornado genesis. The limitations of remote sensing are evident; one cannot remotely sense the thermodynamic field, these data can only be obtained with in situ sensing. Research into tornadogenesis will not progress significantly until there are measurements of the thermodynamic and microphysical properties aloft in the vitally important rear-flank region of supercell storms. A consensus of research in the last 25 years makes it clear that a small downdraft of a few kilometers width, known as the ”rear-flank downdraft” plays a causative role in tornado formation.1, 2 But recent studies have produced a quandary: surface observations from instrumented vehicles beneath this downdraft indicate that it typically arrives at the ground relatively warm and potentially buoyant compared to typical thunderstorm downdrafts, while studies of the flow in and around this downdraft suggest that it is negatively buoyant aloft. It is surmised that this negative buoyancy, if present in sufficient quantities upstream of the location of potential tornado formation, causes the rotation that is eventually reoriented and concentrated into a tornado. Unfortunately, barriers toward the study of the rear-flank downdraft have not been easily overcome. While weather radar can return detailed precipitation and wind-field data, it cannot return directly-measured thermodynamic data. Balloons cannot ascend through strong downdrafts and these flows are much too dangerous for penetration using manned aircraft, as evidenced by the inadvertent penetration of a rear-flank downdraft.2 ∗ Graduate
Research Assistant, Department of Aerospace Engineering Sciences. Student Member Professor, Director Research and Engineering Center for Unmanned Vehicles. Senior Member. ‡ Assistant Professor, Department of Geosciences. § Research Assistant, Department of Geosciences † Associate
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Miniaturization of large-scale computing devices and mechanical servos along with wideband communications, and other high-performance technologies, have revolutionized the capabilities of unmanned vehicle systems. UA remove the need to place humans in hazardous environments to acquire in-situ data. Small UA may be constructed with the ability to either possess enough power to combat strong winds, or avoid them through appropriate sensing and path planning.3 It is due to these reasons that small unmanned vehicle systems have become ideal for in-situ study of atmospheric phenomena.4
Figure 1. Experimental Setup for the Pawnee National Grasslands Experiments
Before in situ observations can be collected by UA in the vicinity of tornadoes, the utility of targeting more benign mesoscale phenomena with UAS must be demonstrated. This paper describes the components of a system designed to demonstrate the ability to navigate a small UA with an atmospheric sensor package into a transient mesoscale atmospheric phenomenon. It demonstrates how data from Colorado State University’s NSF sponsored CHILL and Pawnee radars is fused with UA telemetry to allow for navigation of the UA to a pre-existing atmospheric airmass boundary. The paper concludes with dual-Doppler and in-situ data collected during experiments conducted at the Pawnee National Grasslands.
II. A.
Meteorological Data
Dual-Doppler Sensing
Identification of airmass boundary position and motion will primarily depend upon Doppler radar data. The NSF sponsored CHILL and Pawnee Doppler radars located east of Fort Collins on the eastern plains of Colorado (Figure 2) are the ideal radar platforms for these experiments. They are located in a region of the state with a modest population density where the need to operate over major urban areas can easily be avoided. Furthermore, they are positioned so that dual-Doppler measurements can be made during UA operations. As illustrated in Figure 2, the 30◦ dual-Doppler lobes yield observations at minimum altitudes of 300 m AGL over the southwestern areas of the PNG to 900 m AGL over the northeastern areas. The manifestation of airmass boundaries in radar data usually extends through these heights. Juxtaposing dualDoppler observations with in-situ UAS observations also allows for demonstrating the use of UA-measured wind velocity to validate (ex post facto) the accuracy of remotely sensed velocity measurements derived through dual-Doppler analysis. The CSU-CHILL staff have set up a real-time feed of both the CHILL and Pawnee radar data converted to Level-II format. File sizes have been optimized to limit the bandwidth required for dissemination: only two elevation angles are provided, two-gate averaging and median filtering are imposed, and the radar range is limited to 100km. Additional real-time meteorological data are also made available for situational awareness. These data include 1-km visible satellite images and Automated Surface Observing Staiton (ASOS) observations and are provided through the Unidata Internet Data Distribution via UNL. Connectivity to UNL is maintained 2 of 6 American Institute of Aeronautics and Astronautics
Figure 2. Locations of the CHILL and Pawnee radars. The smaller (larger) unfilled lens represents the area for which radar measurements can be made at minimum altitudes between 35 and 100 m (100 and 200 m). All points within the hatched region reside too close to the baseline to allow for reliable dual-Doppler measurements.
using cellular-based wireless internet. Cellular coverage enables transmission rates up to 144 kbps; speeds that are more than sufficient to transfer the data necessary for experiments. B.
UA Sensors
The UA provides in-situ measurement of pressure, temperature, humidity and wind speed and direction. The pressure, temperature and humidity (PTH) measurements are provided through miniaturized temperature and humidity sensors provided by the In-Situ Sensing Facility at NCARs Earth Observing Laboratory. The sensor was originally developed for use in the MIST (miniature in-situ sounding technology) dropsonde.5 These sondes are typically used in combination with at transmitter to provide measurements when dropped from manned aircraft.6 There accuracy has been well documented and their sensing core from Visala has been tested in side by side weather balloon and UA.7 Two of the sondes have been installed in the wingtips of the UA to provide redundancy. Measurement of wind speed and direction comes directly from the autopilot used in the UA. By comparing the GPS ground track with the solution derived from filtering IMU and pressure data, the component of motion due to the wind can be determined. This is calculated continuously, but the level of accuracy of the measurement depends on the amount of variation in UA heading. By performing maneuvers such as ”S” turns, the UA can determine a more accurate wind estimate. C.
Visualization
Software for sensor fusion and meteorological situational awareness was developed using Gibson Ridge Level2 software. GRRUVI, (Gibson Ridge Level-2 Research Radar and UAS Visualization Interface) synthesizes data from research radars such as the CHILL and Pawnee radars with visible satellite images. Real-time positions of the UA are overlayed on top of the radar and satellite data so that navigation decisions for the UA can be made by providing GPS waypoints. These GPS waypoints are automatically communicated through a gateway to an ad-hoc network where they are received by the UA.
III. A.
UAS
Platform
The platform chosen for this experiment is the NexSTAR ARF (almost ready-to-fly), which has been modified by the University of Colorado to operate autonomously.8 Measures have been taken to ensure the reliability
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of the platform and feasibility of deployment in locations across the Pawnee National Grasslands site. These measures include: strengthening to allow for rail launch, conversion to electric propulsion, addition and tuning of an autopilot system, addition of single board computer for flight management and communication through an ad-hoc network, and the addition of pressure, temperature, and humidity sondes. The NexSTAR UA platform along with all of its support systems have been documented and a certificate of authorization (CoA) has been obtained from the FAA to allow for operations in the Pawnee Grasslands. Emergency procedures have been well defined and the operating crew all possesses a current class 2 medical certification. Furthermore a notice to airmen (NOTAM) is issued prior to each operation and various centers are contacted regarding coordination of manned and UA activity. B.
Network
The Ad-hoc UA Ground Network (AUGNet) was developed at the University of Colorado, Boulder to investigate the performance of airborne mobile ad-hoc networks.9 The current AUGNet system allows for the connection of many mobile nodes into an ad-hoc network utilizing adhoc on-demand vector routing (AODV) and the IEEE 802.11 wireless protocol. Small nodes may be randomly placed throughout a range, and data may then be relayed to any chosen node on the network. Network topology may change, and in the case of deployment of a node on a UA, the network topology may change at a significant rate. The network operates on IP based addressing and the transport layer can support both TCP and UDP transport schemes. It has been tested and benchmarked in its current configuration with static and both terrestrial and aerial based mobile nodes. All of the nodes are constructed from COTS technology, keeping system cost down and enabling easy system upgrades and integration with other devices. Built on top of the AUGNet, application layer networking provides the mechanism for service discovery, data stream subscription, and command issuance over the network. This software enables using more than one ground station; communications with any node without prior knowledge of IP addresses; interaction with the UA from outside of the adhoc network; and communication between both MAVs and SAVs over the multiple, heterogeneous sub-networks. The combination of the application layer networking and AUGNet is referred to as the networked unmanned aerial system (NetUAS).10 A graphical user interface (GUI) for visualization and control of the experiment is part of the application layer networking. This is a software component which provides status of network links connecting all of the participating nodes (ground vehicles and UA) together along with node telemetry and scientific data (wind speed and direction, humidity, pressure, and temperature). This is the primary interface for the UA operator to monitor the status of the experiment and issue commands to the nodes.
IV.
Flight Data
The CoCoNUE experiments completed March 1st, 2009 provided for a functional test of the entire system. The team chose to deploy in the South East corner of the CoA region between along CR 69 about 1.5 mi north of HWY 14. This location was chosen because of its inclusion in the dual-Doppler coverage region, good visibility, and proximity to a well graded road. There were two successful two flights of NexSTAR-3 using the sonde wing. The second flight also had a successful automatic landing. During both of the flights the Pawnee and CHILL radars were operating and collecting data. A.
NexSTAR-3 Flight 1 • Flight Time: 24.74 mins • Autonomous Flight Time: 22.07 mins
For the first flight, the system was to be tested by allowing the controller at the meteorology base to command a relatively simple waypoint pattern. Two tracker teams were deployed in cars (Tracker-2 and Tracker-4), both tasked with maintaining line-of-sight (LOS) to the UA. After orbiting several minutes just north of the ground station at 900-ft AGL, NexSTAR-3 was commanded to fly north, along CR 69, about 1.5 mi to CR 69, then west about 2 mi along CR 96. Following successful completion of the pattern, the NexSTAR was commanded to return to base.
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B.
NexSTAR-3 Flight 2 • Flight Time: 25.64 mins • Autonomous Flight Time: 24.70 mins
Following the successful first flight, it was determined that it would be easier for the spotters to maintain LOS to the UA if the UA was simply commanded to orbit a tracker vehicle. The sampling could then be directed by the meteorology base issuing driving commands. Given a tracking command, the UA subscribes to the telemetry stream of the desired node, and updates an orbit waypoint pattern to remain above the node. Using this controller, NexSTAR-3 was leashed to a node on the Tracker-4 vehicle with Tracker-2 following to help keep eyes on the UA. Following launch, NexSTAR-3 climbed to about 900-ft AGL, and was commanded to track the vehicle. Tracker-4 then drove north on CR 69. The objective was to drive to CR 104, about 5-mi north, then west on CR 104 another 2 mi, then return. Tracker-4 drove north at about 25 mph. Tracker -4 stopped at least twice on the drive north when Tracker-2 reported difficulty maintaining visual. When Tracker-4 stopped, the Tracker-4 crew was immediately able to maintain eye contact with the UA orbiting overhead. At about 4 mi north of the GS, the 900-MHz Piccolo link dropped below a satisfactory level and the UA made an immediate turnaround and headed home with both Tracker 2 and Tracker 4 following. The UA then made a clean autonomous landing on CR 69, next to the ground station. Meteorology results from this experiment were similar to those discussed in flight 1.
40.695
40.69
40.685
40.68
40.675
40.67
40.665
−104.445
−104.44
−104.435
−104.43
−104.425
−104.42
−104.415
−104.41
−104.405
Figure 3. GPS track of the UA for flight one. Black dots indicate samples taken while the UA was greater than 5m from its desired sampling altitude of 1800m MSL. Colored dots indicate the average relative humidity measured by the four onboard sensors.
V.
Results
Unfortunately due to the very small amount of scatterers in the atmosphere and extremely calm weather, the radar was unable to make any significant measurements for which to correlate with the UA. Despite this, the in-situ meteorological data from the UA was examined. Figure 3 shows the track of the UA for flight one along with the average relative humidity measured by the four onboard sensors. Although the changes in humidity were small, portions of the track that overlapped demonstrated spatial correlation. Figure 4 shows the measured wind vectors for the western portion of flight one. There was good correlation between estimated winds for most of the flight, all indicating a background wind from the South.
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GPS Track with Wind Vector for Time of Day: [1165.6, 1213.3] min
40.685
40.6845
40.684
Latitude [deg]
40.6835
40.683
40.6825
40.682
40.6815
40.681 −104.446 −104.4455 −104.445 −104.4445 −104.444 −104.4435 −104.443 −104.4425 −104.442 −104.4415 −104.441 Longitude [deg]
Figure 4. Measured wind vectors for a portion of flight one (green) along with UA track (blue).
VI.
Conclusion
A system was constructed that allows for the collection of in-situ atmospheric data using a UA that can be directed using dual-Doppler radar to interesting meteorological phenomena. This system was successfully demonstrated in a deployment to the Pawnee National Grasslands in early March. Lack of scatterers in the beam of the radar did not allow for significant measurements to be made, however, combining radar measurement with in-situ measurements from the UA remain a priority. Experiments are planned for April and March that will hopefully provide this opportunity.
VII.
Acknowledgements
Dr. Patrick Kennedy in support of the Colorado State University CHILL/Pawnee radars. Mr. Terry Hock and Mr. Dean Lauritsen of NCAR’s Earth Observing Laboratory for their adaptation of the Radio Sonde.
References 1 of Climate Weather, N. W. S. O. and Services, W., “NWS weather fatality, injury, and damage statistics,” http://www.nws.noaa.gov/om/hazstats.shtml, 2008. 2 Argrow, B., Lawrence, D., and Rasmussen, E., “UAV Systems for Sensor Dispersal, Telemetry, and Visualization in Hazardous Environments,” 43rd Aerospace Sciences Meeting and Exhibit, AIAA, January 10-13 2005, AIAA Paper 2005-1237, Describes ERC Proposal. 3 Elston, J. and Frew, E. W., “Unmanned Aircraft Guidance for Penetration of Pre-Tornadic Storms,” AIAA Guidance, Navigation, and Control Conference, Honolulu, HI, Aug. 2008. 4 Dixon, C., Eheim, C., Argrow, B., and Palo, S., “Tornado Chaser: A Remotely Piloted UAV for In Situ Meteorological Measurements,” Proc. AIAA 1st Unmanned Aerospace Vehicles, Systems, Technologies, and Operations Conference and Workshop, Portsmouth, VA, May 2002. 5 “In-situ Sensing Facility (ISF) GPS Dropsonde & MIST Sonde,” http://www.eol.ucar.edu/about/ourorganization/isf/Handout-Dropsonde.pdf, 2009. 6 Hock, T. and Franklin, J. L., “The NCAR GPS dropwindsonde,” Vol. 80, 1999, pp. 407–420. 7 Soddell, J., McGuffie, K., and Holland, G., “Intercomparison of atmospheric soundings from the Aerosonde and radiosonde,” Journal of Applied Meteorology, Vol. 43, No. 9, Sept. 2004, pp. 1260 – 9. 8 “The NexSTAR UAS,” http://recuv.colorado.edu/nexstar, 2009. 9 Brown, T. X., Argrow, B., Dixon, C., Doshi, S., Thekkekunnel, R.-G., and Henkel, D., “Ad Hoc UAV Ground Network (AUGNet),” Proc. AIAA 3rd Unmanned Unlimited Technical Conference, Workshop and Exhibit, Chicago, Illinois, Sept. 2004. 10 Elston, J. and Frew, E. W., “Hierarchical distributed control for search and tracking by heterogeneous aerial robot networks,” Pasadena, CA, United states, 2008, pp. 170 – 175.
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