Embedded Reasoning for Atmospheric Science Using Unmanned Aircraft Systems
Eric W. Frew and Brian Argrow
Research and Engineering Center for Unmanned Vehicles
University of Colorado
Boulder, CO 80309
1. Introduction
eral ways. A mothership that can carry and deploy the DS
aircraft over large areas extends the footprint of operation
of the UAS. This gives the system a large workspace while
allowing for fine scale sensing in local regions. Furthermore, the payload capabilities of the DS limit the amount
of sensor information, communication, and processing that
can be achieved onboard. In contrast, the size of the mothership allows them to carry components that can communicate
over larger distances and process more information at higher
rates. Thus, MS can act as coordination agents by exploiting their communication and processing capabilities to minimize the amount of communication, estimation, planning,
and motion carried out by the DS aircraft.
The ability to understand and predict the dynamic behavior
of our planet’s environment over multiple spatial and time
scales remains an outstanding challenge for science and engineering (National Research Council 2007). For the past
35+ years, sensors on spaceborne platforms have increasingly surveilled most of the Earths surface and atmosphere.
However, a summary of US severe weather data over the
past 12 years shows no sustained reduction of fatalities or
property losses (NOAA National Weather Service 2007). To
reduce losses, in situ sensing and forecast systems must be
improved. In particular, this paper addresses the use of a
particular form of unmanned aircraft system to provide embedded reasoning for atmospheric science.
The development of mobile systems for complex sensing
applications has motivated the cooperative control of heterogeneous robots in general and unmanned aircraft systems
in particular. Combining large numbers of robotic sensors
leads to improved flexibility compared to single-robot systems. While heterogeneity expands the set of possible actions, solutions for general teaming are difficult, especially
when robot capabilities vary drastically. This paper focuses
on a specific form of heterogeneous unmanned aircraft system (UAS) comprised of two classes of aircraft with significantly different, though complementary, attributes: 1.)
miniature daughterships (DS) that provide improved flexibility and spatio-temporal diversity of sensed data and 2.)
larger motherships (MS) that carry and deploy the daughterships while facilitating coordination through increased mobility, computation, and communication.
A fundamental drawback of miniature unmanned aircraft
(MUA) is their short range, low endurance, and small payload capacity compared to larger systems. On the other
hand, MUA can be deployed in large numbers, allowing
groups to achieve significant spatio-temporal sensing of an
environment. Because these systems are small, they are often safer close to terrain and obstacles, have smaller weight
and speed so the kinetic energy released in a collision
(should it unfortunately occur) is low, and are inexpensive
and expendable.
The motherships extend the capability of the UAS in sev-
Atmospheric Science Applications
The MSDS architecture proposed here will enable fundamentally new in situ science whereby autonomous systems
actively assimilate data and explore in places too hostile for
humans or expensive single-vehicle systems. Specific scientific applications being addressed by the work described
here include the study of tornadogenesis in severe convective storms, the evolution and movement of Polar sea ice,
and the development of airmass boundaries. The MSDS architecture can be applied to additional applications including ocean sampling, planetary exploration, pollution studies,
and wildfire monitoring.
Tornadoes Effective advanced warning systems could
drastically reduce the loss of life and damage caused by
severe convective storms. Unfortunately, research into tornadogenesis will not progress until there are in situ measurements of the thermodynamic and microphysical properties aloft in the vitally important rear-flank region of supercell storms (Argrow, Lawrence, and Rasmussen 2005;
VORTEX2 2008). The process of tornado formation occurs in regions 1-10 km wide, within a few kilometers of
the ground, and in less than 20 minutes after the first manifestations of tornado potential. Disposable miniature aircraft
with minimal kinetic energy can be inserted into the storm
by riding the wind from the proper location. A fast MS aircraft can dash into the proper region to deploy the DS and
remain nearby to collect data in order to mitigate the possible loss of the DS aircraft.
c 2010, Association for the Advancement of Artificial
Copyright Intelligence (www.aaai.org). All rights reserved.
40
Sea Ice Changes in sea ice mass have a variety of important implications for regional and global weather, access to
polar regions, and the stability of the Arctic and Antarctic environments. Reduction of Arctic pack ice (Lynch,
Maslanik, and Wu 2001; Study of Environmental Change
(SEARCH) 2005) is one of the most dramatic indicators of
global change. Sea ice can exhibit extreme changes over
thousands of square kilometers and over periods as short as
a few weeks. Measurement of ice mass variables requires
new platform concepts for sampling over broad inaccessible
areas and at adequate resolution, thus driving the need for
unmanned aircraft (UA) using deployed surface sensors (the
daughterships whose motion is that of the uncontrollable sea
ice) whose data is harvested periodically by a mothership
UA.
Airmass Boundaries Airmass boundaries are ubiquitous
phenomena in the atmosphere and can play a significant role
in the development of supercells and tornadoes (Argrow
and Houston 2007). Previous methods for collecting
in-situ measurements of airmass boundaries do not provide
significant spatio-temporal sampling (Argrow and Houston
2007). Airmass boundaries are characterized by a long
along-boundary scale (100s to 1000s of km) so they can
be easily tracked via existing observation networks, but
must be traversed by long range mothership aircraft. They
are characterized by a short across-boundary scale on the
order of 1-10km that can be sampled by miniature UA with
limited endurance.
Figure 1: Mothership unmanned aircraft deploying a miniature air
vehicle.
can occur over longer distances or at higher rates than DS to
MS communication.
This work considers two main aspects of robot sensors,
the coverage area of the sensor and the “quality” of the measurements they make. Sensor coverage is modeled as the
geometric area Ssensor (pk ) within which a sensor can receive a meaningful measurement. Sensor quality is most
often measured by the covariance matrix Psensor of an additive white noise vector. In general, the sensor coverage of
the mothership vehicles is greater than that of the daughterships, i.e. SDS (xk ) ⊂ SM S (xk ) while the accuracy of the
sensors on the DS is better, i.e. PDS < PM S
A mothership/daughtership team is further defined by
the large differences in the capabilities of the two sets
of vehicles. Table 1 lists the relationships between the
attributes of the MD. An important feature of this definition
is the fact that it does not define the specific robot types at
each level and therefore the MD concept provides a general
framework. Example MD configurations for different
applications include MS and DS ground robots for planetary
exploration, MS ground and DS aerial robots for convoy
protection, MS aerial and DS ground robots for search
and rescue, MS aerial and DS underwater robots for ocean
sampling, etc. A key challenge is exploiting the synergy
between attributes such that the sum of the MS and DS
robots is greater than the parts, leading to new system
capabilities.
The remainder of this paper is outlined as follows. Section 2 defines a mothership/daughtership system in terms
of complementary characteristics of the two aircraft types.
Section 3 describes the current status of efforts to create an
unmanned aircraft system for severe storm penetration. Section 4 describes the design and implementation of a prototype MS/DS system including preliminary flight data. Finally, Section 5 lays out the future architecture for embedding reasoning about atmospheric science in the field in
order to provide real-time geo-spatial estimation of severe
storms.
Mothership / Daughtership Teams
A mothership/daughtership team is a specific robotic sensor network configuration consisting of two types of robots
organized in a hierarchical communication, command, and
control framework (Fig. 1). At the top layer of the hierarchy
sit motherships that command subsets of daughterships to
which they communicate. At the bottom layer, each daughtership receives commands generated by one and only one
mothership, although that mothership may change over time
and that communication can occur directly or over multiple
hops.
Wireless communication for each robot is assumed to
follow the empirical radio propagation model and provide
communication throughput T (pi , pj ) between two positions that drops off exponentially with distance. In general,
the transmission power PM S of the MS is much greater than
the power PDS of the DS. Thus, MS to DS communication
This presentation discusses the development of mothership/daughtership systems that expand the capabilities of
micro air vehicles. Prototype aircraft, including a modified
Sig Rascal 110 mothership and folding-wing daughtership,
are presented along with a multi-tier net-centric command
and control architecture that create a mothership / daughter-
41
Table 1: Comparison of Attributes for MS and DS robots. The
better values are underlined.
Attribute
Range
Endurance
Comm. Power
Sensor Footprint
Attribute
Sensor Quality
Comp. Processing
Cost
Mass
MS
rmax,M S
Δtmax,M S
P0,M S
SM S (xk )
MS
PM S
f lopsM S
cM S
mM S
DS
>>
>>
>>
⊃
rmax,DS
Δtmax,DS
P0,DS
SDS (xk )
>
>>
>>
>>
DS
PDS
f lopsDS
cDS
mDS
Figure 3: Concept of operations for severe storm penetration.
ship system. Unique research challenges presented by the
MS/DS concept are described and current efforts to address
some of them are discussed.
2. Current Efforts
Field observations of supercells and tornado formation occur
every spring in the Central U.S. Currently these deployments
involve two or more Doppler radar stations. The VORTEX2 experiment currently scheduled for the summers of 2009
and 2010 is by far the largest and most ambitious effort ever
made to understand tornadoes. Over 100 scientists and crew
in up to 35 science vehicles and platforms, including unmanned aircraft, are participating in this unique, fully nomadic, field program.
The Tempest unmanned aircraft system (Fig. 2) was developed at the University of Colorado to provide in situ pressure, temperature, and humidity data within airmass boundaries and severe convective storms. In support of this effort,
a suite of software was developed to allow for real time visualization of Doppler radar and UA information. Through
this interface, controllers are able to control a UA to an area
of interest based upon meteorological information. An ex-
Figure 4: Tempest unmanned aircraft system.
isting ad-hoc network was augmented to allow for the effective dissemination of telemetry, sensor data, and control
throughout the multi-user network (Fig. 3).
A series of simulated deployments were conducted with
the VORTEX-2 team during the Spring 2009 field campaign
in order to evaluate logistical procedures. Figure 4 shows
the ground track of a ground vehicle (simulating the aircraft)
overlaid on Doppler radar data of the storm (Fig. 5). A variety of lessons learned have been incorporated into the current concept of operations, including the introduction of an
“electronic tether” such that the aircraft follows the ground
vehicle in order to maintain visual line of sight and satisfy
Federal Aviation Administration (FAA) regulations.
3. Prototype Mothership / Daughtership
System
While current deployment efforts are focused on a single
vehicle system, ongoing work also addresses the design of
prototype mothership/daughtership vehicles and the heterogeneous architecture needed for communication, command,
and control (C3) of such as system (Elston et al. 2009). The
initial prototype system (Fig. 1) consisted of a commer-
Figure 2: Tempest unmanned aircraft system.
42
Wind field: [u,w,v]
Observation
position pobs
Real-Time Dual Doppler
Wind Field Data
Onboard Planning
and Control
Figure 5: Severe supercell storm encountered in the field.
In situ data: P, H, T
Data Assimilation
cial radio-controlled aircraft outfitted to carry and deploy
four custom-modified commercial miniature aircraft. Current work is combining that prototype design, an existing
net-centric C3 architecture, and a folding-wing miniature
aircraft concept
Figure 7: Block diagram of main components of UAS for
targeted observations in severe storms
Near-optimal planning solutions with guaranteed performance bounds derived from approximate dynamic programming principles.
4. Future Architecture
A new framework is needed to enable embedded reasoning
by autonomous systems within complex atmospheric phenomena. This framework (Fig. 6) combines real-time science driven control of unmanned aircraft systems with online high resolution modeling and data assimilation. It integrates domain-specific reduced order phenomenological
models and decentralized control policies to provide sensing systems with new capabilities, including:
Upward communication of sensed data and local parameter estimates to refine models at different levels of fidelity
and downward communication of refined model parameters derived from higher-level models.
Targeted observation of severe convective storms can be
aided by the presence of mobile Doppler radar systems that
can provide real-time three-dimensional wind fields (Fig.
7). Multiple Doppler radar coordinate data processing to
provide real-time maps of the wind fields to both in situ
aircraft and data assimilation routines connected to high fidelity models. The high fidelity models are used to generate
waypoint locations to reduce model uncertainty and improve
science return. These waypoints are sent to the aircraft as
commands. The aircraft plans motions to the waypoints using the real-time wind fields and phenomenological models
learned from the data. The aircraft collects humidity, pressure, and temperature data that further refines the high fidelity models.
Purely local (i.e. decentralized) control laws combined
with coordination policies that require minimal communication to optimize team objectives.
Sensor-driven policies that explicitly control information
content in sensed data. Optimal action in the information space of the control objective is mapped back into
the physical motion of the vehicles.
Vehicles that have simple (reduced-order) phenomenological models onboard that only retain features of the environment necessary for their guidance. These models
simplify atmospheric phenomena, guidance-layer vehicle
motion, and the dependency of communication channel
capacity on separation distance.
5. Acknowledgments
The Authors would like to acknowledge the work of the
AUGNet research group at the University of Colorado, particularly Tom Aune, Anthony Carfang, Cory Dixon, Jason
Durrie, Jack Elston, and Maciej Stachura, as well as the
MADS and ASTORM student project teams for contributing to the efforts described here. Further, we thank Erik
Rasmussen (Rasmussen Systems), Adam Houston (University of Nebraska), Jerry Straka (University of Oklahoma),
and their students for providing the meteorological support
to this effort.
Optimization over a limited set of motion primitives for
aircraft flight in strong wind fields. These primitives are
well suited to utilizing updrafts for long duration storm
sensing, providing good information on storm evolution,
and generating paths that maneuver around strong winds
with speeds greater than vehicle airspeed.
Science-Driven Control
Local Prediction Data
Regional
Prediction Data
Mothership / Daughtership Robotic Sensor Network
Phenomenological Models
High Resolution Modeling and Data Assimilation
Local Measurements
Regional
Measurements
Mobile Ad-Hoc Network
Mobile Ad-Hoc Network
Real-Time Science-Driven Planning and Control
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Figure 6: A Framework for In Situ Science by Robotics Sensor Networks
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