Robotic Lake Lander Test Bed for Autonomous
Surface and Subsurface Exploration of Titan Lakes
Wolfgang Fink(1,2), Markus Tuller(3), Alexander Jacobs(1), Ramaprasad Kulkarni(1,3),
Mark A. Tarbell(1), Roberto Furfaro(2), Victor R. Baker(4,5)
(1)
Visual and Autonomous Exploration Systems Research Laboratory, Departments of Electrical & Computer
Engineering and Biomedical Engineering, University of Arizona, Tucson, AZ 85721, USA
Email: wfink@ece.arizona.edu
(2)
Systems and Industrial Engineering Department, University of Arizona, Tucson, AZ, USA
(3)
Soil, Water and Environmental Science Department, University of Arizona, Tucson, AZ, USA
(4)
Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, USA
(5)
Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ, USA
In Memoriam Michael J. Drake (LPL, University of Arizona) and Ronald Greeley (SESE, Arizona State University)
patented, revolutionary paradigm shift in robotic space
exploration missions of the future. These types of missions
are called for in extreme space environments, including
planetary atmospheres, surfaces (both solid and liquid), and
subsurfaces (e.g., oceans and lakes), as well as in potentially
hazardous or inaccessible operational areas on Earth. Our
particular focus is on Saturn’s moon Titan with its
hydrocarbon lakes with respect to future missions involving
lake landers (e.g., Titan Mare Explorer (TiME) mission),
potentially in conjunction with balloons/airships and orbitersupport overhead (Fig. 1).
Abstract—We introduce a robotic lake lander test bed that can
be operated either stand-alone or as part of a Tier-Scalable
Reconnaissance mission architecture to study and field test an
integrated hardware and software framework for fully
autonomous surface and subsurface exploration and
navigation of liquid bodies. The lake lander is equipped with
both surface and subsurface sensor technologies. Our
particular focus is on Saturn’s moon Titan with its
hydrocarbon lakes with respect to future missions involving
lake landers (e.g., Titan Mare Explorer (TiME) mission),
potentially in conjunction with balloons/airships and orbitersupport overhead. This test bed serves as an analog to a Titan
unpiloted surface vessel equipped with its own onboard realtime navigation and hazard avoidance system, surface and
subsurface exploration sensor suite, and autonomous science
investigation software system. As such the test bed helps map
out a technical path toward true autonomy for the robotic
exploration of the Solar System.
To investigate the feasibility of such (tier-scalable)
reconnaissance missions and the associated autonomy of
operation, we describe in this paper a system of systems
approach towards the development of an Earth-based (i.e.,
outdoors), computer-controlled, robotic lake lander test bed
for in-situ liquid surface/subsurface exploration (see also
[7]) in the following order:
TABLE OF CONTENTS
1. INTRODUCTION ...................................................... 1 2. RELEVANCE TO NASA’S SPACE TECHNOLOGY GRAND
CHALLENGES ............................................................. 2 3. NASA MISSION RELEVANCE ................................ 2 4. ROBOTIC LAKE LANDER TEST BED ..................... 3 5. ONBOARD SENSORS AND DETECTORS FOR TITAN LAKE
EXPLORATION ........................................................... 4 6. OVERHEAD DETECTION, VISUAL ODOMETRY, AND
HAZARD AVOIDANCE FOR SURFACE TIER NAVIGATION
................................................................................... 5 7. AUTONOMOUS NAVIGATION FOR ROBOTIC LAKE
LANDER TEST BED .................................................... 6 8. DISCUSSION & CONCLUSIONS............................... 8 ACKNOWLEDGEMENT ............................................... 8 REFERENCES ............................................................. 9 BIOGRAPHY ............................................................. 11 Section 2: describes how the robotic lake lander test bed is
relevant to NASA’s Space Technology Grand Challenges;
Section 3: describes how the robotic lake lander test bed fits
into future NASA planetary missions;
Section 4: description of the robotic lake lander test bed
itself;
Section 5: description of onboard sensors and detectors that
are potentially relevant to Titan lake exploration;
Section 6: overhead detection, visual odometry, and hazard
avoidance for surface tier navigation on Titan’s lakes;
1. INTRODUCTION
Section 7: autonomous navigation scenarios for the robotic
lake lander;
Tier-scalable Reconnaissance [1-6] is a NASA-awardwinning (Fink et al., NASA Board Award 2009) and
Section 8: application examples for robotic lake lander test
bed and outlook.
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The test bed described here is part of a larger robotic test
bed for tier-scalable reconnaissance at the University of
Arizona [6-10]. While it is clear that our robotic test beds
are not space-deployable as yet, they are being used to
engineer, operate, and validate various systems that might
be applied to such spacecraft that are ultimately deployed. It
is their respective autonomy of operation (by employing
enhanced sensory/cognitive capabilities), and their
implementation into a tier-scalable reconnaissance
architecture with associated coordination and collaboration,
that are being studied.
In particular the development of autonomous exploration
and navigation capabilities for Titan lake landers using this
test bed may significantly advance NASA’s mission
capabilities to autonomously access the hydrocarbon lakes
on Titan (with or without balloon/airship or orbiter relay)
for a future flagship mission. Employing a Stochastic
Optimization Framework and a Fuzzy Expert Framework
(see Section 7 below) to integrate autonomous sensor data
interpretation algorithms with a guidance navigation and
control system to create a truly autonomous system for the
exploration of hydrocarbon lakes on Titan has the potential
to yield significant improvements toward the design of truly
autonomous (i.e., no human in the loop) systems for space
exploration.
3. NASA MISSION RELEVANCE
Titan is one of the most exciting places in the Solar System
and its exploration is labeled as “high priority” as
recommended by the 2006 NASA Solar System Exploration
Roadmap and the ESA Cosmic vision. Whereas the CassiniHuygens probe was extremely functional in uncovering the
complexity of Titan, revealing an Earth-like world with
potential for habitability, it was limited by the use of onboard instrumentation. In support for the next joint Outer
Planet Flagship Mission, in 2008, ESA and NASA
completed a pre-phase A study of an architecture for the
Flagship mission called Titan Saturn System Mission
(TSSM) [12]. The study was also included as a concept
study for the Planetary 2013-2022 Decadal Survey, which
required an independent cost estimate for the next decade
recommendation [13]. The TSSM architecture comprised a
NASA-supplied orbiter and ESA-supplied in-situ
components, i.e., a hot-air (Montgolfière) balloon and a lake
lander – an instantiation of a Tier-Scalable Reconnaissance
mission architecture [1-6]. Considering the cost of the fullscale Flagship mission and the priority given to the Europa
Jupiter System Mission (EJSM) as the next Flagship
mission, one of the major issues was to identify if the in-situ
explorers may be flown as stand-alone missions before the
next TSSM. Since the hot-air balloon requires a high-data
rate to remotely image Titan’s surface, the focus was shifted
to the lake lander. With the primary goal being lake
chemistry, direct connection with Earth may be more
feasible at a reduced data rate. “Real-time” commanding of
a lake lander from Earth would not be an option because of
3 hours signal relay time, hence calling for a, for the most
part, completely autonomous lake lander, which is one of
the rationales for our robotic lake lander test bed.
Figure 1. Embodiment of Tier-Scalable Reconnaissance
for autonomous robotic exploration of planetary bodies,
applied to Saturn's moon Titan with its hydrocarbon
lakes. Depicted are an orbiter, controlling airships or
blimps, which in turn control ground rovers and lake
landers or boats (from [7]).
2. RELEVANCE TO NASA’S SPACE TECHNOLOGY
GRAND CHALLENGES
The robotic lake lander test bed presented here directly
addresses one of NASA’s Space Technology Grand
Challenges [11] – “All Access Mobility”: “Create mobility
systems that allow humans and robots to travel and explore
on, over or under any destination surface.” The problem
stated is the following: “Exploration of comets, asteroids,
moons and planetary bodies is limited by mobility on those
bodies. Current robotic and human systems cannot safely
traverse a number of prevalent surface terrains. Current
systems travel slowly, requiring detailed oversight and
planning activities. Consequently, these systems are often
limited to exploring areas close to their original landing
site.”
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Indeed the lake lander was chosen by the current NRC
planetary decadal survey to be studied as a stand-alone
mission and as an element of a Flagship-class mission [13].
The study analyzed three possible New Frontiers-class
missions and a more ambitious collaborative Flagship-like
mission. The New Frontiers-class mission would comprise
2
three options, i.e., (a) a lake lander using a direct
communication link with Earth, (b) a submersible-only
probe with a relay orbiter, and (c) a lake lander with an
orbiter as relay. Conversely, the Flagship-class lander would
be comprised of a lake lander as well as a submersible
probe. From a power perspective, all proposed architectures
would be powered by Advanced Sterling Radioisotope
Generators (ASRG), mounted either on the lander, the
orbiter, or both. The study concluded that the three New
Frontiers-class missions are not feasible within the indicated
cost cap (~$710M not including launch vehicle) unless the
Decadal Survey Satellites Panel would change the required
study ground rules [13].
to maximize the buoyancy and thereby the payload. Each
pontoon mounts to the frame of the boat in a modular
fashion, such that the chassis of the boat may be attached to
another method of locomotion.
More recently, as part of the NASA Discovery and Scout
Mission Concept Study, Proxemy Research (lead by Dr.
Ellen Stofan) proposed the Titan Mare Explorer (TiME)
mission as a first probe to explore extraterrestrial seas [14].
TiME is a low-cost, discovery-class mission that would (1)
measure the organic constituents on Titan and (2) perform
the first nautical exploration and in-situ analysis of an
extraterrestrial sea, including its shoreline [15]. In early
May 2011, NASA announced that TiME was selected for a
Phase-A study [16] as part of the Discovery Program, with
the potential to be launched in a 2015-2016 timeframe. The
mission ranked Category-1, i.e., recommended for selection
with high priority. The selection of the TiME mission over a
stiff competition of 30 other mission concepts is a strong
indication of the importance of Titan lake exploration within
the planetary community, and thus serves as an additional
rationale for the development of our robotic lake lander test
bed.
Figure 2. Schematic drawing of Tucson Explorer II
(top). Actual Tucson Explorer II (TEX II) with trailing
side-scanning sonar and electric propulsion system
mounted (bottom).
4. ROBOTIC LAKE LANDER TEST BED
As a prototype for a (Titan) lake lander or sea rover we
designed and built from the ground up a computercontrolled boat with high-performance, battery-powered
onboard Unix workstation (i.e., Apple Mac mini). The
Tucson Explorer II (TEX II, Fig. 2) is the latest addition to
the fleet of robotic surface explorers [7] at the Visual and
Autonomous Exploration Systems Research Laboratory at
the University of Arizona. According to the tier-scalable
reconnaissance paradigm, TEX II will be of the first tier,
i.e., it will directly engage in exploration of target areas insitu.
TEX II features two 60-amp 10,000 rpm electric motors,
each of which drives an air propeller in both forward and
backward direction. The propellers are mounted maximally
apart from each other, affording maximum torque to the
chassis when turning (Fig. 2).
Thanks to the powerful onboard computing capabilities and
long battery life, this robotic lake lander test bed permits
complex and numerically intensive onboard calculations,
and thus does not suffer from constraints introduced by the
premature usage of embedded computing.
TEX II (Fig. 2) is a catamaran capable of an onboard
computer and sensor payload in excess of 68 kg in water. It
weighs approximately 45 kg, and is 1.8 m long by 1.5 m
wide by 0.5 m tall. The chassis of TEX II consists of a
sensor platform or deck mounted to a frame. Sensors may be
mounted above or below the deck. The frame consists of
three arches and two cross bars, which are mounted on top
of two pontoons that provide the buoyancy for TEX II. The
pontoons are made of fortified Styrofoam (i.e., low density)
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For earthly applications, the wireless TCP/IP capabilities of
the robotic lake lander test bed allow for near real-time
interactive (or automatic) control from anywhere in the
world via iOS technology (e.g., iPhone, iPod, or iPad) [9,
10]. This enables the implementation, field-testing, and
validation of algorithms/software and strategies for
navigation, exploration, feature extraction, anomaly
3
detection, and science goal prioritization for autonomous
planetary exploration with particular focus on liquid bodies
(i.e., oceans or lakes). Furthermore, the robotic lake lander
test bed permits field-testing of (novel) instrument and
sensor equipment.
•
To achieve a fully operational Tier-scalable Reconnaissance
test bed, aerial platforms, such as balloons or blimps, will
soon be integrated.
Propellers submersed in liquid methane and/or ethane
as opposed to water would have to be larger in surface
area (and thus heavier) to generate the same amount of
propulsion because of the lower densities [18].
5. ONBOARD SENSORS AND DETECTORS FOR
TITAN LAKE EXPLORATION
Motivation and Justification for Lake Lander Design with
Respect to Titan Lake Environments
With a payload of 68 kg the Tucson Explorer II (TEX II) is
a capable platform to study and test sensors and sensor
systems that may help discover and measure
physicochemical variables of Titan’s hydrocarbon lakes and
near-surface atmospheric conditions. Deployment of a highfrequency side-scan sonar for bathymetric lakebed surveys
[7] will provide detailed information about hydrocarbon
volumes that currently can be only estimated based on
similarities with geometrical and topological features of
Earth-based lakes [22].
As already stated in [7] early lake deployments of the
Tucson Explorer I (TEX I), a miniaturized precursor of TEX
II, showed that the catamaran-design results in a very stable
behavior of the boat, i.e., reduced banking for stable sensor
readings such as with onboard cameras and chassis-mounted
side-scanning sonar system (i.e., stable sonar swath) in the
case of TEX II.
The rudder-less design of the lake lander, already introduced
in [7], enables unique pivoting maneuvers, comprises fewer
moving parts, and renders its control identical to tracked
ground rovers [7] as far as onboard electronics and
navigational control are concerned, hence also the name
“sea rover”.
Depending on the mole fraction of methane and ethane (and,
by an order of magnitude smaller degree, nitrogen)
encountered in a hydrocarbon lake on Titan [17], the lake
liquid density is expected to range anywhere from ~453.4
kg m-3 for liquid methane to ~654.1 kg m-3 for liquid ethane
at a temperature of 92.5 K [18] and at a surface pressure of
1.467x105 N m-2 [19, 20], compared to the density of water
(~1,000 kg m-3). According to Archimedes’ principle this
will directly and proportionally affect the payload capacity
of a Titan lake lander. Thus, the use of low-density
materials and/or a high volume-to-mass ratio for the lake
lander design are indicated to maximize the buoyancy and
thereby the payload.
In particular for Titan lake environments there are several
compelling reasons for considering an air-based (i.e., above
surface) propulsion system as opposed to a liquid-based
(i.e., submersed) one:
•
Reduced risk of freezing of propulsion and control
elements, such as propellers and rudders, at the
cryogenic temperatures of liquid methane and ethane;
•
Avoidance of cavitation and other disturbances of
subsurface measurements (e.g., with sonar or EMI);
•
Increased propulsion efficiency because of higher
surface air density of ~5 kg m-3 [19, 21] compared to
~1.3 kg m-3 on Earth;
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Figure 3. Electromagnetic Induction Instrument (EMI)
for proof-of-concept purposes (top). Positioning of EMI
on robotic lake lander test bed TEX II (bottom).
Lakebed surveys may be accompanied by detailed electrical
resistivity mapping by means of electromagnetic induction
(EMI) [23]. A lightweight EMI instrument (Fig. 3, top) with
a penetration depth of about 4 m can be mounted below the
deck of TEX II (Fig. 3, bottom) and potentially provide
4
important information about the composition of
hydrocarbons (i.e., mainly methane and ethane [17]) in
Titan lakes. With proper calibration, EMI has the potential
to provide additional information about the mineralogical
composition of near-shore lakebed sediments.
Additional sensors and detectors that are currently being
integrated aboard the robotic lake lander test bed comprise
(described in detail in [7]):
Standard weather variables such as intensity and duration of
hydrocarbon precipitation, near-surface temperature as well
as wind speeds and directions can be measured with a drop
impact sensor, adapted thermistor or thermocouple, and an
ultrasonic sensor, respectively. Figure 4 shows an
instrument that combines all three modalities.
•
Onboard cameras for navigation, investigation, and
scene/deployment documentation;
•
Onboard Scanning Laser Rangefinder for obstacle
avoidance and navigation;
and for Earth-based applications in addition:
•
Onboard GPS.
Future onboard sensors will also include hyperspectral
imagers (e.g., [29]) and detectors for in-situ discovery and
monitoring of microbes on liquid surfaces [30-32, 7].
It is important to emphasize that the technologies discussed
above need to be further developed and tailored (if not
completely reengineered) to withstand the harsh
environmental conditions prevailing on Titan. Recent
advances in microelectronics and nanotechnology offer
exciting opportunities to accomplish this task (e.g., [33] and
references therein).
6. OVERHEAD DETECTION, VISUAL ODOMETRY,
AND HAZARD AVOIDANCE FOR SURFACE TIER
Figure 4. Instrument with integrated precipitation,
wind, and temperature sensors for proof-of-concept
purposes.
NAVIGATION
The near surface atmosphere, estimated to be composed of
mainly nitrogen and small amounts of methane, hydrogen,
argon, a variety of hydrocarbons, nitriles, and oxidized
species [24-26], can be more accurately determined with
Fourier Transform-Infrared (FT-IR) spectroscopy [27]. An
adapted FT-IR spectroscope can be applied for rapid
differentiation and identification of microorganisms in
liquids [28]. Portable, off-the-shelf systems (Fig. 5),
previously deployed in harsh Antarctic conditions, weigh
only 12 kg and can easily be integrated with TEX II.
Visual Odometry
Visual odometry is required to command robotic surface
explorers from a dynamic aerial platform such as a blimp,
airship, or balloon (Fig. 6). In [7] we described an
algorithmic approach for removing uncertainties in position
due to perspective distortion (because of wind movement of
the aerial platform) and a potentially constantly changing
reference frame, because the airship-mounted camera is not
stationary, and therefore the geometry of the scene changes
with changes in orientation of the camera to the operational
area on the surface below. According to [7] a series of
transformations is considered as a pipeline, which converts
image plane locations to ground plane locations. Once the
variables governing these transformations are determined
via a multivariate optimization procedure, inverse
transformations can be performed on the image plane points
to arrive at their respective locations in the ground plane.
Overhead Detection
Before visual odometry can be performed, the image plane
points belonging to the surface explorers (here: lake lander)
have to be determined (Fig. 6). To accomplish this, an
acquired overhead image is first transformed in various
Figure 5. Portable Fourier-Transform Infrared (FT-IR)
Spectrometer for proof-of-concept purposes.
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ways to facilitate the search for the surface explorer. The
image is converted to gray scale, followed by contrast
enhancement. Following contrast enhancement, a ‘nearest
neighbor’ algorithm is deployed to smoothen the image.
Subsequently we employ a template search for the surface
explorer in the so processed overhead image. Once a
template match is found, the coordinates of the template are
transferred to the visual odometry algorithm for tracking
and subsequent navigational purposes.
Figure 6. Overhead detection from aboard a blimp or
balloon and visual odometry for navigation of a Titan
lake lander (not drawn to scale). [Titan lake radar image
courtesy NASA/JPL/USGS]
Hazard Avoidance
Figure 7. AGFA processing example of target/obstacle
detection on ocean surface: original image frame (top)
and AGFA-processed frame (bottom).
The Automated Global Feature Analyzer (AGFA; [34] for
details) is an extensible analysis and classification
framework. Images of the operational area under
investigation are obtained at different length scales and
resolutions, depending on the vantage point (e.g.,
spaceborne, airborne, or ground). AGFA performs image
segmentation through mean-shift or histogram-based
segmentation for rapid target and obstacle identification in
the imaged operational area (Fig. 7). In contrast to
approaches that use stereo-vision (i.e., range data) for the
navigation of and obstacle avoidance for surface vessels
(e.g., [35]), AGFA operates currently on single camera
images (i.e., mono-vision). Through image processing
algorithms [34] AGFA extracts features (e.g., target shape,
size, color, albedo, texture, vesicularity, angularity,
eccentricity, compactness, extent), and generates feature
vectors for all identified targets. AGFA subsequently
prioritizes targets for in-situ follow-up investigation [36]
and flags anomalies based on the feature space (i.e., sensorspecific data types) alone as opposed to biased, humanhypothesis-driven analyses [34].
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7. AUTONOMOUS NAVIGATION FOR ROBOTIC
LAKE LANDER TEST BED
Autonomous Navigation & Control of a Titan Lake
Lander via Stochastic Optimization Framework (SOF)
Optimization in high-dimensional configuration spaces is in
general computationally expensive, and typically on the
order of NP-complete (or at least exponential) for problems
such as the Traveling Salesman. However, for most
problems, the optimal solution is often not required, rather a
good approximation (i.e., local optimum) found in a short
amount of computation time is preferred, especially on a
computationally restricted computing platform such as
commonly found on planetary spacecraft, (lake) landers, and
rovers.
6
Figure 9. Example island, as an analog to a lake or ocean
floor, from the IEEE CEC 2006 International “Huygens
Probe” Optimization Competition. [Image courtesy
Cara MacNish, University of Western Australia]
Figure 8. Functional schematic of a Stochastic
Optimization Framework (SOF; from [37]): The SOF
efficiently samples the entire model/process/systemintrinsic parameter space by repeatedly running the
respective model/process/system forward and by
comparing the outcomes against a desired outcome,
which results in a fitness measure. The goal of the SOF is
to optimize this fitness by using, e.g., Simulated
Annealing algorithms [38] as the optimization engine.
A striking feature was consistently observed during the
hundreds of test runs and the actual competition runs, which
is the main basis for employing an SOF-Simulated
Annealing-based autonomous navigation & control system
for a lake lander: After only 10-20 iterations (deployments)
the algorithm consistently found the basin in which the
lowest point was located. This means that complex lake
exploration tasks, such as finding the deepest/shallowest
part of a lake, can now be conducted and tested fully
autonomously with the robotic lake lander test bed.
We have developed and successfully tested an approach
using a Stochastic Optimization Framework (SOF; [37];
Fig. 8) for operating complex mobile systems with several
degrees of freedom, such as planetary rover robotic limbs
with N joints, in environments that can contain obstacles
[39]. As part of the SOF we have employed an efficient
Simulated Annealing algorithm that is particularly suited to
run onboard spacecraft, planetary (lake) landers, and rovers,
i.e., robotic platforms with limited computational
capabilities.
Autonomous Guidance & Control of a Titan Lake Lander
via Fuzzy Expert Systems
Autonomy will play a critical role in future science-driven
and less constrained exploration of extremely challenging
planetary
environments
including
low-temperature
hydrocarbon lakes on Titan. Over the past few years, our
team proposed to use fuzzy logic as a fundamental
framework to devise intelligent systems capable of
reasoning over sensor/science data while mimicking the
cognitive process employed by planetary scientists [40-44].
In case of a lake lander, such a framework can be employed
to design fuzzy expert systems as well as more advanced
cognitive schemes (e.g., Fuzzy Cognitive Maps [43]) to
reason over data collected while patrolling the lake.
Advanced reasoning systems must be integrated with the
lander Guidance Navigation and Control (GNC) to enable
complete closed-loop capabilities where information is
autonomously acquired and interpreted and acted upon, after
a decision is taken, to navigate the lake using the on-board
GNC system.
The IEEE Congress on Evolutionary Computation
International “Huygens Probe” Optimization Competition,
created by Cara MacNish, University of Western Australia
School of Computer Science and Software Engineering, was
first held at the 2006 IEEE World Congress of
Computational Intelligence in Vancouver, Canada. The
competition addressed a “worst case” scenario optimization
problem of finding the lowest point possible in a fractal
landscape within 1,000 iterations (Fig. 9). The landscape
was a function of two variables x and y (analogous to lake
surface coordinates). In the “Huygens Probe” optimization
scenario the landscape was never revealed. Only the depth z
(analogous to lake bathymetry) for a submitted (x, y)location per iteration was reported back to the optimization
algorithm. For the competition, access was given to a set of
20 unseen fractal surfaces. We applied a modified Simulated
Annealing algorithm that allowed for both global and local
search at an increasingly more refined scale. This algorithm
won the “Huygens Probe” Optimization Competition in
2006 with 11 out of 20 direct wins (1st place) and an average
ranking of 2.4.
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Figure 10 shows the nominal architecture conceived and
developed by our team. The lander is assumed to travel near
the shoreline, and it is gathering data for both navigational
and geological interpretation. The onboard laser rangefinder
provides information about the distance from the shoreline,
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8. DISCUSSION & CONCLUSIONS
whereas sonar provides information about the lake
bathymetry. The first step executed by the system is to
process the visual information using the AGFA platform
[34] for feature extraction and target prioritization [36].
After proper preprocessing and categorization, the fuzzy
expert system synthesizes the information and assesses the
potential for scientific interest of the observed region [41,
42]. The guidance planner subsequently determines the
proper distance from the shoreline, which is followed by a
fuzzy controller that controls the lander actuators (i.e., rpm
for each of the two motors) to achieve that desired distance,
thereby modifying the lander trajectory across the lake
(Figs. 6 and 7).
This introduced robotic lake lander test bed serves as an
analog to a Titan unpiloted surface vessel equipped with its
own onboard real-time navigation and hazard avoidance
system, surface and subsurface exploration sensor suite, and
autonomous science investigation software system.
Maneuvers and other planetary reconnaissance operations,
such as autonomous patrolling of a lake shoreline and
profiling and mapping of a lake floor, can be studied in
detail.
While the current instantiation of the test bed is not spacequalified as yet, it will serve the objective of studying the
autonomous deployment, operation, and coordination
between the multiple agents that are integral components of
a tier-scalable reconnaissance system. The ultimate goal is
to explore the optimal way of implementing autonomous
planetary reconnaissance using the enhanced sensory and
cognitive capabilities that form the architectural backbone
of the robotic test bed. Whatever the robotic system
ultimately deployed on remote planetary surfaces, isolated
from direct Earth command and control, it will have to
contend with exactly the same issues of autonomous
operation that we are studying by employing these robotic
test platforms. The test bed development is a critical step to
map out a technical path toward true autonomy for the
robotic exploration of the Solar System.
The theoretical implementation of a fuzzy expert for Titan
exploration has been previously analyzed [42]. Moreover,
our
team
designed
and
simulated
a
fuzzy
controller/guidance planner for altitude control and station
keeping of a hot-air balloon for Titan exploration [45].
Future work will focus on modifying the currently available
schemes to the lake lander problem by (1) designing a fuzzy
expert system capable of interpreting the lake geological
environment, (2) implementing a dynamical model for the
motion of the lander on the lake, and (3) designing and
simulating a fuzzy-based controller capable of maintaining
the desired distance from the lake shoreline and/or track a
specified trajectory autonomously defined by the expert
system.
In addition to applications pertaining to planetary
exploration, the robotic lake lander test bed can be
employed in terrestrial lake exploration (e.g., Mono Lake
near Mammoth, California), marine/oceanic exploration,
scenarios of security surveillance (e.g., harbor security,
perimeter surveillance), reconnaissance of (contaminated)
disaster areas (e.g., radioactive leakage after tsunami),
military reconnaissance (e.g., riverine reconnaissance),
delivery of lethal force, and cleanup operations such as
littoral munitions dump and mine cleanup.
ACKNOWLEDGEMENT
This research was supported by the Edward & Maria
Keonjian Endowment at the Electrical and Computer
Engineering Department at the University of Arizona.
Figure 10. Proposed architecture for the fuzzy-based
autonomous reasoning system integrated with the Titan
lake lander GNC subsystem, conceived for (a)
autonomous feature extraction and interpretation of
geological data collected while patrolling the
hydrocarbon lake, and (b) autonomous trajectory
planning and tracking to satisfy science objectives. The
system is conceived to provide autonomous
understanding of the science data and decision-making
capabilities to select the optimal travelling distance from
the shoreline.
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Robotic Platform for Testing and Validating Image
Processing and Autonomous Navigation Algorithms in
Support of Artificial Vision Prostheses; Comput Methods
Programs
Biomed,
96(3):226-33;
DOI:
10.1016/j.cmpb.2009.06.009
[1] Fink W, Dohm JM, Tarbell MA, Hare TM, Baker VR
(2005)
Next-Generation
Robotic
Planetary
Reconnaissance Missions: A Paradigm Shift; Planetary
and Space Science, 53, 1419-1426.
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_challenges_12_02_10.pdf
[2] Fink W, Dohm JM, Tarbell MA, Hare TM, Baker VR,
Schulze-Makuch D, Furfaro R, Fairen AG, Ferre TPA,
Miyamoto H, Komatsu G, Mahaney WC (2007) TierScalable Reconnaissance Missions For The Autonomous
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[4] Fink W, George T, Tarbell MA (2007) Tier-Scalable
Reconnaissance: The Challenge of Sensor Optimization,
Sensor Deployment, Sensor Fusion, and Sensor
Interoperability; Proc. SPIE, Vol. 6556, 655611 (2007);
DOI:10.1117/12.721486.
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M. Trainer M. Waite H. Zarnecki J. (2010) Exploring the
Seas of Titan: The Titan Mare Explorer (TiME) Mission,
41th Lunar and Planetary Science Conference [#1236],
Houston, March 2010.
[5] Fink W, Tarbell MA, Jobling FM (2008) Tier-Scalable
Reconnaissance - A Paradigm Shift in Autonomous
Remote Planetary Exploration of Mars and Beyond;
Chapter 1 in “Planet Mars Research Focus”, Ed. L. A.
Costas. Nova Science Publishers, Hauppauge, NY. ISBN:
1-60021-826-1.
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329, 508-509.
[17] Lunine, J.I. (1993) Does Titan have an ocean? A review
of current understanding of Titan’s surface; Rev.
Geophys. 31, 133–149.
[7] Fink W, Tarbell MA, Furfaro R, Powers L, Kargel JS,
Baker VR, Lunine J (2011) Robotic Test Bed for
Autonomous Surface Exploration of Titan, Mars, and
Other Planetary Bodies; IEEE Aerospace Conference
Proceedings, paper #1770, Big Sky, Montana.
[18] Younglove, B.A., Ely, J.F. (1987) Thermophysical
properties of fluids. II. Methane, ethane, propane,
isobutane, and normal butane; J. Phys. Chem. Data 16, 4.
[8] Fink W and Tarbell MA (2007) Tier-scalable
Reconnaissance Mission Test Bed: Implementation of
Ground-Tier [abstract 2410]. In 38th Lunar and Planetary
Science Conference Abstracts [CD-ROM], Lunar and
Planetary Institute, Houston.
[19] Fulchignoni, M., and 41 colleagues (2005) In situ
measurements of the physical characteristics of Titan’s
environment; Nature 438, 785–791.
[20] Lindal GF, Wood GE, Hotz HB, Sweetnam DN (1983)
The Atmosphere of Titan: An Analysis of the Voyager 1
Radio Occultation Measurements; Icarus 53, 348-363.
[9] Fink W, Tarbell MA (2009) Multi-Rover Test Bed for
Tele-Conducted
and
Autonomous
Surveillance,
Reconnaissance, and Exploration; Proc. SPIE, Vol.
7331, 73310B (2009); DOI: 10.1117/12.819991
[21] Mitri G, Showman AP, Lunine JI, Lorenz RD (2007)
Hydrocarbon lakes on Titan; Icarus 186, 385-394.
[22] Clark, R. N., et al. (2010) Detection and mapping of
hydrocarbon deposits on Titan, J. Geophys. Res., 115,
E10005, doi:10.1029/2009JE003369.
978-1-4577-0557-1/12/$26.00 ©2012 IEEE
9
[23] Tuller, M., and M.R. Islam (2005) Field methods for
monitoring solute transport. In J. Álvarez-Benedí and R.
Muñoz-Carpena (Eds.), Soil-Water-Solute Process
Characterization: An Integrated Approach, CRC Press,
Boca Raton, FL, pp.309-355.
[35] Huntsberger T, Aghazarian H, Howard A, and Trotz D
(2011) Stereo Vision Based Navigation for Autonomous
Surface Vessels; Journal of Field Robotics, Special Issue
on Maritime Autonomy, Part 2, Vol. 28, No. 1, pp. 3-18;
DOI: 10.1002/rob.20380
[24] Niemann, H. B., et al. (2005) The abundance of
constituents of Titan’s atmosphere from the GCMS
instrument on the Huygens probe, Nature, 438, 779–784,
doi:10.1038/nature04122.
[36] Fink W (2006) Generic Prioritization Framework for
Target
Selection
and
Instrument
Usage
for
Reconnaissance Mission Autonomy, Proceedings of IEEE
World Congress on Computational Intelligence (WCCI)
2006, Vancouver, Canada, 11116-11119.
[25] Waite, H., D. Young, T. Cravens, A. Coates, F. Crary,
B. Magee, and J. Westlake (2007) The Process of Tholin
Formation in Titan’s Upper Atmosphere, Science, 316,
870–875; doi:10.1126/science.1139727.
[37] Fink W (2008) Stochastic Optimization Framework
(SOF) for Computer-Optimized Design, Engineering, and
Performance of Multi-Dimensional Systems and
Processes; Proc. SPIE, Vol. 6960, 69600N (2008);
DOI:10.1117/12.784440 (invited paper).
[26] DeKok, R., et al. (2007) Oxygen compounds in Titan’s
stratosphere as observed by Cassini CIRS, Icarus, 186,
354–363; doi:10.1016/j.icarus.2006.09.016.
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Optimization by Simulated Annealing, Science, 220, 671680.
[27] Griffiths, P.R., and J.A de Haseth (2007) Fourier
Transform Infrared Spectroscopy, 2nd Edition, John
Wiley & Sons, Inc., Hoboken, NJ.
[39] Fink W, Huntsberger TL, Aghazarian H (2010) Dynamic
Optimization of N-Joint Robotic Limb Deployments;
Journal of Field Robotics, Volume 27, Issue 3, pp. 268280; DOI: 10.1002/rob.20323
[28] Beekes, M., P. Lasch, and D. Naumann (2007)
Analytical applications of Fourier transform-infrared (FTIR) spectroscopy in microbiology and prion research, Vet.
Microbiol.,
123:305-319,
doi:10.1016/j.vetmic.2007.04.010.
[40] Furfaro R, Dohm JM, Fink W (2006) Fuzzy Logic
Expert
System
for
Tier-scalable
Planetary
Reconnaissance, 9th International Conference on Space
Operations, AIAA, Rome, Italy, June 19-23.
[29] Johnson WR, Wilson DW, Fink W, Humayun M,
Bearman G (2007) Snapshot hyperspectral imaging in
ophthalmology; Journal for Biomedical Optics 2007 JanFeb;12(1):014036.
[41] Furfaro, R., Dohm, J.M., Fink, W., Kargel, J., SchulzeMakuch, D., Fairén, A.G., Palmero-Rodriguez, A., Baker,
V.R., Ferré, P.T., Hare, T.M., Tarbell, M.A., Miyamoto,
H., and Komatsu, G. (2008) The search for life beyond
Earth through fuzzy expert systems, Planetary and Space
Science, Volume 56, Issues 3-4, 448-472.
[30] Estes, C., A. Duncan, B. Wade, C. Lloyd, W. Ellis, Jr.,
and L. Powers. 2003. Biosensors & Bioelectr. 18:511519.
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Intelligent Systems for the Autonomous Exploration of
Titan and Enceladus, Space Exploration Technologies.
Proc. SPIE, Volume 6960, 69600A (12 pages).
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Patent 6,750,006.
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Bishop, M. P. (2010) Identification of Cryovolcanism on
Titan Using Fuzzy Cognitive Maps, Planetary and Space
Science, Volume 5, Issue 5, Pages 761–779.
[33] Fink W, George T, Tarbell MA (2007) Tier-Scalable
Reconnaissance: The Challenge of Sensor Optimization,
Sensor Deployment, Sensor Fusion, and Sensor
Interoperability; Proc. SPIE, Vol. 6556, 655611 (2007);
DOI:10.1117/12.721486 (invited paper).
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[34] Fink W, Datta A, Dohm JM, Tarbell MA, Jobling FM,
Furfaro R, Kargel JS, Schulze-Makuch D, Baker VR
(2008) Automated Global Feature Analyzer (AGFA) – A
Driver for Tier-Scalable Reconnaissance; IEEE
Aerospace Conference Proceedings, paper #1273; DOI:
10.1109/AERO.2008.4526422
978-1-4577-0557-1/12/$26.00 ©2012 IEEE
10
BIOGRAPHY
Alexander Jacobs is a Ph.D. student at the Visual and
Autonomous
Exploration
Systems
Research
Laboratory
(under
Dr.
Wolfgang Fink) in the Department of
Electrical and Computer Engineering at
the University of Arizona. He obtained a
B.S. in physics from Indiana University.
His research is focused on autonomous
robotic planetary exploration platforms.
His research interests are in autonomous
robotics, swarming behavior, and neural systems.
Wolfgang Fink is currently an Associate Professor and the
inaugural Edward & Maria Keonjian
Endowed Chair of Microelectronics with
joint appointments in the Departments of
Electrical and Computer Engineering,
Biomedical Engineering, Systems and
Industrial
Engineering,
and
Ophthalmology and Vision Science at the
University of Arizona in Tucson. He is a
Visiting Associate in Physics at the California Institute of
Technology, and holds concurrent appointments as Visiting
Research Associate Professor of Ophthalmology and
Neurological Surgery at the University of Southern
California. Dr. Fink is the founder and director of the
Visual and Autonomous Exploration Systems Research
Laboratory at Caltech (http://autonomy.caltech.edu) and at
the University of Arizona. He was a Senior Researcher at
NASA’s Jet Propulsion Laboratory from 2000 till 2009. He
obtained a B.S. and M.S. degree in Physics and Physical
Chemistry from the University of Göttingen, Germany, and
a Ph.D. in Theoretical Physics from the University of
Tübingen, Germany in 1997. Dr. Fink’s interest in humanmachine interfaces, autonomous/reasoning systems, and
evolutionary optimization has focused his research
programs on artificial vision, autonomous robotic space
exploration, biomedical sensor/system development,
cognitive/reasoning systems, and computer-optimized
design. Dr. Fink is a Fellow of the American Institute for
Medical and Biological Engineering (AIMBE). His work is
documented in numerous publications and patents. Dr. Fink
holds a Commercial Pilots License for Rotorcraft.
Ramaprasad Kulkarni is a Masters student at the Visual
and Autonomous Exploration Systems
Research
Laboratory
(under
Dr.
Wolfgang Fink) in the Department of
Electrical and Computer Engineering,
and
in
the
Soil,
Water,
and
Environmental
Science
Department
(under Dr. Markus Tuller) at the University of Arizona. He
obtained a B.S. in Engineering from Visvesvaraya
Technological University, Belgaum, India in 2007. His
research is focused on automated segmentation of X-Ray CT
images of porous media and template matching for
autonomous robotics. His research interests include image
analysis, computer vision, autonomous robotics, parallel
programming, and high performance computing.
Mark A. Tarbell is a Visiting Scientist at Caltech and
Senior Software Specialist with more than
25 years of satellite and ground-based
command and control system architecture
design and development. Tarbell designed
and implemented the ground data
processor control infrastructure for JPL's
recent SRTM mission, and was involved
with JPL's Jason JTCCS project, which
supports real-time telecommanding of Earth-orbiting
satellites from wireless handheld PDAs. In collaboration
with the Visual and Autonomous Exploration Systems
Research Laboratory at Caltech, he recently co-designed
and implemented a remote telecommanding control system
for an outdoor test bed for autonomous surface exploration
at the Visual and Autonomous Exploration Systems
Research Laboratory at Caltech and University of Arizona.
Markus Tuller is currently an Associate Professor of
Environmental Physics in the Department
of Soil, Water, and Environmental
Science at the University of Arizona with
an
adjunct
appointment
in
the
Department of Hydrology and Water
Resources. He received M.S. and Ph.D.
degrees in Civil Engineering and Water
Management from the University of Natural Resources and
Applied Life Sciences in Vienna, Austria. His research is
focused on environmental sensing technology, modeling and
measurement of mass and energy transport and distribution
in porous media, and development of advanced algorithms
for segmentation of X-Ray Computed Tomography (CT)
data of artificial and natural porous materials. He was
involved with the design of advanced plant growth modules
for reduced gravity environments in support of NASA’s
advanced life support systems for manned long-duration
space missions.
978-1-4577-0557-1/12/$26.00 ©2012 IEEE
Roberto Furfaro is currently an Assistant Professor in the
Systems and Industrial Engineering
Department, University of Arizona. He
has a large spectrum of research
interests, which includes neutron and
photon computational transport, neural
and fuzzy systems, space systems and
micro-satellite design. Over the past few
years, he has been collaborating with
Ecosystem Science and Technology branch at NASA Ames
on the “NASA Coffee Project” in which he led the
development of an intelligent algorithm for coffee ripeness
11
prediction using UAV airborne images. He has had a longterm involvement with Mars exploration since 1998 when he
joined the NASA SERC at University of Arizona to become
the project manager for the development of two robotic
devices designed to utilize Martian local resources.
Recently, he has been working developing of novel
engineering solutions for planetary exploration including
fuzzy-based expert systems for autonomous life-searching in
extraterrestrial bodies.
Victor A. Baker is a Regents' Professor of the University of
Arizona in the Departments of Hydrology
and Water Resources, Planetary Sciences
and Geosciences. He has more than 35
years experience in planetary science
research, particularly in geological
studies of Mars and Venus. He also has
had long experience with interpretive
studies of terrestrial remote sensing,
especially in regard to his specialties in fluvial
geomorphology and flood hydrology. Dr. Baker is a Fellow
of the American Geophysical Union, Honorary Fellow of
the European Geosciences Union, Fellow of the American
Association for the Advancement of Science, and Foreign
Member of the Polish Academy of Sciences. He was the
1998 President of The Geological Society of America, and
he holds the 2001 Distinguished Scientist and 2010
Distinguished Career Awards from the Quaternary Geology
and Geomorphology Division of that society. He is author
or editor of 17 scholarly books or monographs, more than
350 scientific papers and chapters, and over 400 published
abstracts and short reports.
978-1-4577-0557-1/12/$26.00 ©2012 IEEE
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