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American Institute of Aeronautics and Astronautics
TOWARDS A SUBSTANTIALLY AUTONOMOUS AEROBOT FOR TITAN
EXPLORATION
Alberto Elfes, Jeffery L. Hall, James F. Montgomery, Charles F. Bergh, Brenda A. Dudik
Jet Propulsion Laboratory
California Institute of Technology
4800 Oak Grove Drive
Pasadena, CA 91109
{elfes, jeffery.l.hall, monty, cfb, brenda.a.dudik}@jpl.nasa.gov
ABSTRACT
Robotic lighter-than-air vehicles, or aerobots, have a
strategic potential as surveying and instrument
deployment platforms for the exploration of planets and
moons with an atmosphere, such as Venus, Mars and
Titan. Aerobots are characterized by modest power
requirements, extended mission duration and long
traverse capabilities, as well as the ability to transport
and deploy scientific instruments and in-situ laboratory
facilities over vast distances. With the arrival of the
Huygens probe at Saturn’s moon Titan in early 2005,
there is considerable interest in a subsequent follow-on
mission that would explore Titan’s surface through a
substantially autonomous aerobot. Autonomous
operation is required due to the nominal 2.6 hours
round trip communication delay between Earth and
Titan, as well as multi-day communication blackouts
caused by Titan’s rotation and orbit around Saturn. In
this paper, we discuss first steps towards the
development of an autonomy architecture and a core
set of perception, reasoning and control technologies
for a future Titan aerobot. We provide an overview of
the design of the autonomy architecture, which
integrates perception-based inferences about the
environment of operation of the vehicle, vehicle health
monitoring and reflexive safing actions, accurate flight
control, and long-range mission planning and
monitoring. We also describe the JPL aerobot testbed
and the avionics architecture being developed for
testing and validation of aerobot autonomy capabilities.
INTRODUCTION
NASA’s 2003 Solar System Exploration Roadmap
states that aerial platforms could play a key role in the
exploration of Mars, Venus and Titan [NASA 2003]. It
also defines advanced autonomy technologies as a high
priority development area for the operation of aerial
exploration vehicles. In this paper, we discuss the
advantages of planetary exploration using aerobots, the
challenges involved in aerobot exploration of Titan, and
the required autonomy capabilities. We provide an
overview of the autonomy architecture we are
developing, and describe the aerobot testbed and the
avionics architecture being developed at JPL for testing
and validation of aerobot autonomy capabilities. We
finalize with a discussion of the current state of this
research.
PLANETARY EXPLORATION USING
AEROBOTS
Exploration of the planets and moons of the Solar
System has so far been done through remote sensing
from Earth, fly-by probes, orbiters, landers and rovers.
Remote sensing systems, probes and orbiters can only
provide non-contact, low to medium resolution imagery
over a limited number of spectral bands; landers
provide high-resolution imagery and in-situ data
collection and analysis capabilities, but only for a single
site; while rovers allow imagery collection and in-situ
science across their path. The crucial drawback of
ground-based systems is their limited coverage: in past
or planned exploration missions, the rover range has
varied from approximately 130 m (for the 1997
Sojourner mission) to 1 km (projected for the 2003
Mars Exploration Rovers), to tens of kilometers (for the
Lunokhod rovers).
While the data collected through these various
approaches has been invaluable, there is a strategic gap
in current exploration technologies for systems that can
combine extensive coverage with high-resolution data
collection and in-situ science capabilities. For planets
and moons with an atmosphere, this gap can be filled
by aerial vehicles. In the Solar System, in addition to
Earth, the planets Venus and Mars, the gas giants
(Jupiter, Saturn, Uranus and Neptune) and the Saturn
moon Titan have significant atmospheres. Aerial
vehicles that have been considered for planetary
exploration include airplanes and gliders, helicopters,
balloons [Kerzhanovich 2002] and airships. Flight time
for gliders depends heavily on wind and updraft
patterns, which in turn constrain their surface coverage,
AIAA's 3rd Annual Aviation Technology, Integration, and Operations (ATIO) Tech
17 - 19 November 2003, Denver, Colorado
AIAA 2003-6714
Copyright © 2003 by the American Institute of Aeronautics and Astronautics, Inc.
The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes.
All other rights are reserved by the copyright owner.
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American Institute of Aeronautics and Astronautics
while airplanes and helicopters expend significant
energy resources simply staying airborne [Elfes 2001,
Elfes 2003]. These considerations point towards the use
of lighter-than-atmosphere (LTA) systems for planetary
exploration due to their potential for extended mission
duration, long traverses, and extensive surface coverage
capabilities. To date, only two LTA vehicles have ever
flown outside of Earth, as part of the Soviet VEGA
mission to Venus in 1985 [VEGA 1985]. Two identical
balloons, each 3.4 m in diameter with a 6.7 kg gondola,
flew at an altitude of 53 – 55 km, where the
atmospheric temperature and pressure are Earth-like (10
– 30
o
C, 0.5 atm). The balloons had no propulsion
system, so they simply traveled with the winds. The
envelopes were constructed from Teflon material to
withstand the sulfuric acid in the atmosphere. Both
VEGA balloons were delivered inside an entry vehicle
and executed aerial deployment and inflation after
arrival, while descending under a parachute. Pressure,
temperature and wind speed data were returned for two
days until the onboard batteries ran out of power. It is
not known how much longer the balloons flew after
that.
Robotic airships have unique capabilities that make
them ideal candidates for airborne planetary
exploration. Airships have modest power requirements,
and combine the long-term airborne capability of
balloons with the maneuverability of airplanes or
helicopters. Their controllability allows precise flight
path execution for surveying purposes, long-range as
well as close-up ground observations, station-keeping
for long-term monitoring of high-value science sites,
transportation and deployment of scientific instruments
and in-situ laboratory facilities across vast distances to
key science sites, and opportunistic flight path
replanning in response to the detection of relevant
sensor signatures. Furthermore, robotic airships provide
the ability to conduct extensive surveys over both solid
terrain and liquid-covered areas, and to reconnoiter sites
that are inaccessible to ground vehicles.
Implementation of these capabilities requires achieving
a high degree of vehicle autonomy across a broad
spectrum of operational scenarios.
Interest in unmanned airships, primarily for 1)
advertising [Foster 2003], 2) military surveillance and
intelligence gathering [Boschma 1993], and 3) high-
altitude communication platforms [Rehmet 2000], has
been growing in the last decade. Small remotely-piloted
airships are becoming commercially available,
primarily for advertising [Foster 2003]. Autonomous
robotic airships, however, have only very recently
started to be developed. The leading projects in this
area are AURORA [Elfes 2001], which focuses on
autonomy technologies for terrestrial unmanned
airships for environmental research and monitoring;
SAA LITE [Boschma 1993], which has developed
highly capable teleoperated surveillance platforms;
LOTTE [Kröplin 2002], which addresses new designs
and materials for long-term mission solar-powered
unmanned airships; and the JPL aerobot research
described in this paper. Generally speaking, it can be
said that current systems use GPS-based motion control
for accurate flight trajectory following, while other
capabilities, particularly those related to visual
navigation or long-term mission planning and
execution, are still in their infancy. An integrated set of
enabling technologies for autonomous aerobot
navigation and aerial exploration is currently not
available, and is the core focus of the research being
developed by the authors.
Figure 1: Near-infrared images of the surface of Titan,
taken with the Hubble Space Telescope. Bright areas
indicate the possible presence of a continental mass,
while dark areas indicate possible oceans of liquid
ethane and methane. Source: NASA/University of
Arizona.
THE CHALLENGE OF TITAN
NASA’s 2003 Solar System Exploration Roadmap also
identifies a follow-on Titan mission with an in-situ
vehicle as a high priority after the Cassini-Huygens
mission. Titan is the largest moon of Saturn, with a
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American Institute of Aeronautics and Astronautics
radius of 2,575 km. It has a thick atmosphere with a
surface density of 5.55 kg/m
3
(4.6 times the density of
the Earth's atmosphere at sea level), with a nominal
composition of 95% nitrogen, 3% methane and 2%
argon. The surface pressure is approximately 1.5 bar,
and the gravity at the surface is 1.35 m/s
2
(approximately 1/7 of the gravity of Earth). The surface
temperature is approximately –180
o
C.
Although the lower atmosphere is expected to be clear
and highly transparent, the upper atmosphere has a
thick haze that shrouds the surface of Titan from visual
observation. Consequently, very little is known of
Titan’s geography and geology. Recent Hubble Space
Telescope (HST) observations (Fig. 1) in the near-
infrared spectrum (0.85 to 1.05 microns) indicate the
possible existence of a continent composed of solid
rock and frozen water ice, and of liquid bodies
potentially composed of liquid ethane and methane
[Hall 2002a, Hall 2002b, Lorenz 2000]. Additional
long-term observations have also provided indications
of weather on Titan, including clouds and storms.
Detailed knowledge of Titan is expected to increase
dramatically with the arrival of the Cassini spacecraft at
Saturn in July 2004 and the subsequent delivery in 2005
of the Huygens probe into the Titan atmosphere.
However, Huygens will only investigate Titan at one
location for a few hours while Cassini will be limited to
a few dozen relatively brief flybys of Titan; therefore,
many scientific questions will remain unanswered,
particularly in the areas of weather and seasonal
variability, subsurface morphology, and the
composition and distribution of surface organic material
[Chyba 1999], leading to the requirement for a follow-
on mission.
AEROBOT EXPLORATION OF TITAN
Based on the discussion above, we argue that
exploration of Titan can best be accomplished through
an aerobot, a self-propelled buoyant robotic airship that
can access most of the world over multi-month
timescales with minimal consumption of limited
onboard electrical power [Hall 2002a, Hall 2002b] (Fig.
2).
The main challenges for exploration of Titan by an
aerobot are:
• Large communication latencies, with a round trip
light time of approximately 2.6 hours. This
precludes both vehicle teleoperation and close,
human-in-the-loop, supervisory control.
• Extended communication blackout periods with a
duration of up to 9 Earth days, caused by the
rotation of Titan and its orbital occlusion by
Saturn.
• Extended duration of the exploration mission,
currently projected to be on the order of multiple
months to a year.
• Operation in substantially unknown environments,
wi t h l argel y unknown wi nd pat t erns,
meteorological conditions, and surface topography.
Figure 2: An artist’s impression of an aerobot lowering
a scientific payload to the surface of Titan. Source: JPL.
These challenges impose the following capability
requirements on a Titan aerobot:
• Vehicle safing: the aerobot will have to
continuously ensure its safety and integrity over the
full duration of the mission and during extended
communication blackout, with loose or no human
supervision, and under substantially unknown
operational conditions.
• Autonomous flight: the aerobot will have to execute
compl ex aeri al maneuvers (i ncl udi ng
deployment/lift-off, landing, hovering/station-
keeping, surface approach, and long traverses) with
accuracy and safety, while receiving human
guidance only at the level of goals and pre-
computed flight patterns.
• Mapping and self-localization: the aerobot will
have to estimate its motion, localize itself within a
regional or global reference frame, and perform
spatial mapping of its environment without the
support of a global positioning system and
probably of a magnetic field on Titan. This will be
accomplished through the fusion of inertial
measurements, vision-based motion estimation,
and additional information provided potentially by
an infrared Sun tracker, a radio-based Earth tracker
and/or a possible future Saturn tracker.
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• Advanced perception: the aerobot has to respond
dynamically to sensor input, allowing it to detect
and avoid atmospheric and topographic hazards,
and also to identify, home in, and keep station over
pre-defined science targets or terrain features
through visual servoing.
It should be noted that this is an ambitious set of
capabilities, and that feasible and valuable Titan
missions can be done with less autonomy. Elsewhere,
we have discussed a range of mission scenarios and
identified the associated autonomy requirements [Hall
2002b].
In the sequence of this paper, we provide an overview
of the design of the autonomy architecture being
developed to address the requirements of Titan
exploration through an aerobot. It should be noted that
this is a multi-year research effort, and that in the
remainder of the discussion we will concentrate on
those components of the architecture already developed
or currently under development. This includes the
description of the aerobot testbed being developed at
JPL and its avionics architecture.
The work discussed here complements a parallel
research effort at JPL focused on development of the
thermo-mechanical platform required for aerobot
operation in the cryogenic environment at Titan [Hall
2003].
AEROBOT AUTONOMY ARCHITECTURE
To address the aerobot autonomy capabilities outlined
above, we are developing an aerobot autonomy
architecture that integrates accurate and robust vehicle
and flight trajectory control, perception-based state
estimation, hazard detection and avoidance, vehicle
health monitoring and reflexive safing actions, vision-
based localization and mapping, and long-range
mission planning and monitoring. The major
components of this architecture are shown in Fig. 3.
Lower level functions in the autonomy architecture
include sensor and actuator control, vehicle state
estimation, flight mode control, supervisory flight
control, and flight profile execution. Intermediate level
functions include vehicle health monitoring, failure
detection and recovery, flight trajectory and profile
planning, and vision based navigation. The latter
provides GPS-independent localization, local and
regional mapping, and hazard detection and avoidance
(HDA) capabilities. Higher level functions include
mission planning, resource management, and mission
execution and monitoring.
Failure Detection
& Recovery
Vehicle Health
Assessment
Mission Execution
& Monitoring
Earth Comm Mission Planning
Navigation
Sensors
Science
Sensors
Flight Profile
Planning
Flight Profile
Execution
Wind Field
Estimation
Vehicle State
Estimation
Resource
Management
Aerobot
Actuator
Control
Sensor
Control
Flight Mode
Supervisory Control
Ascend Descend Traverse
Hover Takeoff Land
Internal
Sensors
3D Mapping
HDA
Note: only a representative set of pathways is shown
Figure 3: Aerobot autonomy architecture showing the
major subsystems to be developed.
Our initial research thrust has been concentrated on the
actuator and vehicle flight control modules. For that, we
are developing:
ß A robust flight control system based on vehicle
aerodynamic modeling, system simulation for
robust control law development and testing, and
vehicle system identification.
ß Accurate vehicle multi-sensor state estimation
methods, incorporating both inertial and vision-
based motion and position estimation.
Once stable and robust flight control has been achieved,
our research will focus on vision-based local and global
mapping, target tracking and visual servoing
techniques, internal fault detection and recovery,
external hazard detection and avoidance, and flight and
mission planning and monitoring.
Aerobot Flight Control
The aerobot flight control system being developed is
based on: (1) system modeling, which includes
aerodynamic, airship sensor and actuator, and
environmental modeling); (2) system identification for
aerodynamic parameter estimation; (3) model and
control system validation in a physically-based
simulation environment; and (4) flight testing on the
aerobot testbed.
A physically accurate airship aerodynamic model is
significantly different from fixed-wing or rotary-wing
aircraft aerodynamic models. Airship dynamic models
have more in common with submarine hydrodynamics,
as the virtual mass and inertia properties of the
displaced atmospheric volume are substantial when
compared with those associated with the vehicle itself.
Additionally, an aerobot is characterized by having
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American Institute of Aeronautics and Astronautics
different flight modes (take-off/landing, station-
keeping/hovering, ascent/descent, high-speed cruise,
low-speed flight) that require alternative actuator
control strategies and flight control algorithms.
Important flight control challenges include non-
minimum phase behavior and oscillatory modes at low
speeds, time-varying behavior due to altitude variations,
and variable efficiency of the actuators depending on
aerobot speed [Gomes 1990, Elfes 2001].
Wind disturbances will be dealt with using a robust
controller design. Pose (position and orientation in 6
DOF) and motion estimation will initially be done by
fusion of IMU and GPS data, allowing assessment of
the vehicle flight control and trajectory following
accuracy. To move towards GPS-independent
operation, an image-based vehicle motion and position
estimation system will be developed, and a multi-sensor
state estimation filter will be used to fuse inertial and
visual navigation estimates.
Aerodynamic Model Development
The aerobot aerodynamic model developed has the
following features: all bodies are considered rigid,
ascent/descent and trajectory studies have been
addressed, attitude dynamics are included, the planet is
modeled as a flat plane, and thrusting devices are
modeled by (potentially articulated) point force/torque
vectors.
The fundamental aerodynamic equation is given by:
M v’ = F
d
(v) + A (v) + G + P
where M is the inertia matrix, v is the velocity vector
with respect to the center of volume, F
d
are the
centrifugal and Coriolis forces, A are the aerodynamic
forces due to the hull and control surfaces, G are the
gravitational and buoyancy forces, and P are the
propulsion forces and moments.
The plant model incorporates initial estimates of
aerodynamic coefficients and added (virtual) mass
coefficients from theory and tests reported in the
literature [Gomes 1990]). These will be refined through
system identification procedures. The complete model
has been implemented in Simulink (Fig. 4), and initial
actuator control studies have been performed. In the
simulation models, lateral and vertical components of
wind gust profiles can be applied. Additionally, sensor
models and estimators (IRU, accelerometers, gyro),
actuator models (first order lag model), and tracking
control laws (proportional, derivative, and feedforward)
are included. Further details, as well as a discussion of
the specific aspects of the kinematic and dynamic
models developed, are provided in [Quadrelli 2003].
Kinematic &
Dynamic
Models
Control and
Actuator
Models
Guidance
System
Sensors Models
and State
Estimators
Environmental
Models
Figure 4: Aerobot system model implemented in
Simulink. The subsystems modeled are: (1) Guidance,
(2) Control and Actuator Models, (3) Environmental
Models (including wind disturbances and atmospheric
characteristics), (4) Kinematic and Dynamic Models,
(5) Sensor Models and State Estimators.
Aerobot Simulation
Development and testing of the aerobot models and
flight controllers will increasingly be supported by a
high fidelity, physically accurate simulation
environment. This simulation environment provides a
low-cost approach for rapid testing and prototyping of
control, guidance and navigation algorithms before
flight evaluation on the aerobot. Additionally, it allows
projection of algorithm and system performance from
Earth to the Titan environment. With the models
validated under Earth flight conditions, the simulation
system provides the capability to predict the
performance of a Titan aerobot in the Titan atmosphere
(Fig. 5).
Vehicle
Simulation
Path/Attitude
Control Models
Area/Global Guidance
& Navigation Models
High-Fidelity Aerobot Simulation
Earth Airship
Test Platform
Performance
Prediction
Validation Data
Titan
Models
Earth
Models
TITAN
PERFORMANCE
Vehicle
Simulation
Path/Attitude
Control Models
Area/Global Guidance
& Navigation Models
High-Fidelity Aerobot Simulation
Earth Airship
Test Platform
Performance
Prediction
Validation Data
Titan
Models
Earth
Models
TITAN
PERFORMANCE
Figure 5. The simulation framework provides for
aerodynamic model and flight controller validation
before subsequent flight testing and evaluation on the
JPl aerobot testbed; it also allows prediction of system
performance at Titan.
The aerobot simulation environment will be based on
the high-fidelity spacecraft simulation framework of the
Darts/Dshell tool [Biesiadecki 1997]. This includes the
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Darts (Dynamics Algorithms for Real-Time
Simulation) real-time, flexible-body, multibody
dynamics package and the Dshell (Darts shell) tool for
integrating reusable hardware and environmental
models with Darts to develop high-fidelity spacecraft
engineering simulations. This system is the basis for the
ROAMS (Rover Analysis Modeling & Simulation)
planetary rover simulator and the DSENDS (Dynamics
Simulator for Entry Descent and Surface landing) entry,
descent and landing simulator [Balaram 2002]. An
extensive set of models developed for these
applications is available for immediate use by the Titan
aerobot simulation effort.
Vision-Based Navigation
The aerobot will rely heavily on vision for state
estimation, hazard detection and avoidance, navigation,
and mapping. To progress towards GPS-independent
operation over extended travel distances, an image-
based vehicle motion and position estimation system
will be developed using a multi-sensor state estimation
approach. For this, a multi-sensor Kalman filter will be
used to fuse inertial navigation measurements
(rotational velocities and linear accelerations) from the
IMU (inertial measurement unit) with surface relative
motion estimates derived from image-based motion
estimation (IBME). IBME makes use of feature image
locations from feature tracking and the aerobot state
from the Kalman filter to make these estimates
[Roumeliotis 2002].
THE AEROBOT TESTBED
The prototype aerobot testbed being developed at JPL is
based on an Airspeed Airship AS-800B (Fig. 6). The
airship specifications are: length of 11 m, diameter of
2.5 m, total volume of 34 m
3
, two 2.3 kW (3 hp) 23 cm
3
(1.4 cu inch) fuel engines, double catenary gondola
suspension, control surfaces in an “X” configuration,
maximum speed of 13 m/s (25 kts), maximum ceiling
of 500 m, average mission endurance of 60 minutes,
static lift payload of 10 kg asl, and dynamic lift payload
of up to 16 kg asl. The avionics and communication
systems are installed in the gondola.
The aerobot avionics is built around the PC-104+
computer architecture (Fig. 7). The PC-104+ stack is
composed of a CPU board running the onboard avionics
software, a serial board interface to the navigation
sensors and pan/tilt unit, a timer/counter board for
reading pulse width modulated (PWM) signals from a
human safety pilot and generating PWM signals based
upon control surface commands from the avionics
software, and an IEEE 1394 board for sending
commands to, and reading image data from, the
navigation and science cameras.
Figure 6: The JPL aerobot testbed has a length of 11 m,
a diameter of 2.5 m, and a static lift payload of 10 kg.
PC-104+
CPU Stack
Control
Surfaces
I
n
t
e
g
r
a
t
i
o
n
B
o
a
r
d
OVER
RIDE
Pilot Control
72MHz
Serial Rx/Tx
900MHz
Li
+
Batteries
Science
Camera
Video Tx
426MHz
Video Tx
434MHz
Payload Cameras
Cut-Away
Rx
Navigation
Camera
IMU
Compass
Laser
Altimeter
GPS
Barometric
Altimeter
Ultrasonic
Anemometer
Nav Avionics
RS232
6-PWM
12V, 6.0W
RS232
5V, 1.0W
RS232
12V, 10W
RS232
12V, 0.3W
2-RS232
5V, 3.2W
RS232
12V, 3.5W
11VDC
9Ah
Pan/Tilt
Unit
RS232
12V, 7.5W
6-PWM
RS232
Figure 7: Avionics architecture.
The navigation sensors consist of an IMU (angular
rates, linear accelerations), a compass/inclinometer
(yaw, roll and pitch angles), laser altimeter (surface
relative altitude), barometric altimeter (absolute altitude
against reference point), GPS (absolute 3D position)
and ultrasonic anemometer (3D wind speed). The vision
sensors consist of two down-looking navigation
cameras (to gather imagery used for motion and
position estimates that feed into the navigation
software) and a science camera (mounted on the pan/tilt
unit that acquires imagery used for science-based
processing). The science camera is also used for
aerobot navigation, but at a higher functional level, i.e.,
the navigation camera processing provides inputs into
the Kalman filter used for vehicle state estimation while
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the science camera processing provides inputs into
vehicle functions such as go-to or hover over a site of
interest.
Wireless serial modems provide a two-way link
between the aerobot and a ground station. High level
commands from a human user are sent to the aerobot
from the ground station and system telemetry is sent
from the aerobot to the ground station for display and
archival purposes. Two wireless video transmitters on
the aerobot provide a one-way link from the aerobot to
video receivers at the ground station to send imagery
from the navigation and science cameras to the ground
for display on video monitors and data archival. The
human safety pilot can send vehicle control commands
via a one-way 72 MHz wireless link using a hand-held
transmitter to onboard receiver. There is a control
signal switching box onboard the aerobot that allows
the routing of either human or robot commands to be
sent to the vehicle control actuators on an actuator by
actuator basis. In addition, the safety pilot can always
reassert “pilot override” control over the aerobot. As a
final safety layer, a gas release valve on the aerobot
envelope can be activated by a standalone one-way
wireless link from the ground to the aerobot, causing
loss of lift and a forced but graceful landing.
Figure 8: Screenshot of the ground station showing
aerobot state estimate telemetry from the onboard
avionics system.
The ground station (Fig. 8) is composed of a laptop, the
wireless data and video links, video monitors and
VCRs. The final component of the ground station is a
differential GPS (DGPS) base station that provides
differential corrections to the GPS receiver onboard the
aerobot, allowing vehicle 3D position estimates with an
accuracy on the order of centimeters. This is
particularly important in the early stages of flight
control system development and in later stages for
validation and accuracy assessment of the vision-based
navigation system.
Figure 9: Streaming video imagery from the down-
looking navigation camera.
FLIGHT TESTING
We have recently initiated flight testing of the aerobot
testbed. An operational subset of the avionics system
has been demonstrated in tethered outdoor tests (Figs. 8
and 9). Fig. 10 shows the maiden flight of the JPL
aerobot, conducted on September 10, 2003. Test flights
of the aerobot are being conducted at the El Mirage dry
lake site in the Mojave desert. Currently, the system is
teleoperated, but as the complete avionics system is
transitioned to the vehicle and the control systems are
validated in simulation, vehicle flight control will be
progressively switched to the onboard autonomy
system.
CONCLUSIONS
In this paper, we have argued that robotic LTA
vehicles, or aerobots, have a strategic potential for the
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American Institute of Aeronautics and Astronautics
exploration of planets and moons with an atmosphere,
such as Venus, Mars and Titan. We argued that
aerobots can provide geographically extensive science
data at high resolution and over varied terrains, to a
degree that cannot be matched by surface-bound rovers
or other aerial vehicles. We discussed the challenges for
operation of an aerobot under Titan conditions, and the
corresponding autonomy requirements. We outlined an
architecture for a substantially autonomous aerobot, and
discussed the aerobot vehicle and flight control systems
being developed. We also described the airship testbed
and the avionics architecture being developed at JPL for
validation of aerobot autonomy capabilities. Our next
steps include transitioning the aerobot control from
teleoperation to autonomous flight trajectory execution,
and incorporation of vision-based navigation
capabilities.
ACKNOWLEDGMENTS
The authors would like to acknowledge the help and
support of: Marco Quadrelli, who developed the
aerobot kinematic and dynamic models; J. Bob
Balaram, who is leading the development of the aerobot
simulation environment; Eric A. Kulczycki, who was
instrumental in aerobot propulsion and flight testing;
and Lee Magnone and Michael S. Garrett, for support
in avionics development. The research described in this
paper was performed at the Jet Propulsion Laboratory,
California Institute of Technology, under a contract
with the National Aeronautics and Space
Administration (NASA), and administered through the
Intelligent Systems (IS) Program. The views and
conclusions contained in this document are those of the
authors and should not be interpreted as representing
the official policies, either expressed or implied, of the
sponsoring organizations.
Figure 10: Test flight of the JPL aerobot under
teleoperated control, conducted at the El Mirage dry
lake in the Mojave desert. The photos show liftoff,
ascent, and flight of the aerobot.
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REFERENCES
[Balaram 2002] J. Balaram et al. "DSENDS - A High-
Fidelity Dynamics and Spacecraft Simulator for
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