A Systems Approach to Autonomous Space Exploration
Quentin L. Willard, Austin M. Bartlett, Louis S. Harrington, and Jason C. McKay
Faculty Advisor: Maj. Ernest Y. Wong
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Abstract—As Systems Engineering and Engineering
Management cadets at the United States Military Academy, we
are working with NASA for our senior-year capstone project.
In concert with “NASA’s 2006 Strategic Goals,” our team is
exploring how to more safely land autonomous spacecraft and
more reliably guide autonomous surface rovers. Employing
Value-Focused Thinking (Keeney, 1992), we have created a
Microsoft Excel spreadsheet model that will assist NASA in
determining what capabilities ought to be considered for the
variety of sensors being developed. Because of conflicting
objectives, complex alternatives, and major uncertainties
associated with space operations, we are leveraging Multiple
Objective Decision Analysis (Kirkwood, 1997) to both identify
vital capabilities as well as illuminate critical shortcomings in
the design of future sensor suites on autonomous space systems.
Specifically, our model is a decision support system that
examines space exploration as a series of sequential phases,
each with a distinct set of requirements and demands. Because
of trade-offs between the various phases, our model will
provide NASA decision-makers with a greater sense of where
they can focus their efforts so that the chances of mission
success will increase. While neophytes in the realm of space
exploration, we believe the Interactive Management Science
(Savage, 2003) architecture featured in our model will allow
experts at NASA to input their own parameters—parameters
that we know will inevitably change because of different
mission requirements and continual advances in technology. In
addition to assisting NASA with returning to the moon, our
model will also help with other space exploration missions,
including the Extended Lunar Outpost Architecture, Mars
Outpost and Science Support Systems, and Exploration
Systems Architecture. Not only will our decision support
system help to better link NASA’s strategic goals to its
operations, it will also help better focus its research and
development, improve allocation of existing resources, and
increase the probability of future space exploration successes.
I.
INTRODUCTION
As part of our senior year capstone project at the United
States Military Academy, we are working with NASA on
enhancing ways to more safely land autonomous space
landers on other planets and more reliably guide
autonomous surface rovers through our largely un-navigated
solar system. Even though we did not come equipped with
any noteworthy qualifications or specific background on
space or sensor technologies, web believe our undergraduate
courses and background in systems engineering have
adequately prepared us for dealing with this experience. As
a result, we are putting into practice much of the theory that
we have been learning. In this paper, we describe what it
takes to successfully engineer a solution to a real-world
problem and the diverse methodologies we used to get there.
We will also highlight portions of our work and describe our
methodology for tackling this challenging project.
II.
BACKGROUND AND STAKEHOLDER ANALYSIS
Early last semester we were introduced to our capstone
project via a teleconference with Jon Paterson and Tom
Bryan, two of NASA’s engineers working on advanced
sensor technologies for remote and unmanned systems at the
Marshall Space Flight Center. About a month later we
finally got to meet both in person as well as various other
stakeholders involved with the project to refine how our
cadet team could best contribute to the mission. At first we
were very worried about how we were going to provide any
value to NASA. Fortunately, through our discussions with
our stakeholders, we discovered that we would be able to
provide a different approach to NASA’s current
methodology for determining the most appropriate sensor
suit to be placed on-board automated space landers and
surface rovers. By leveraging the Systems Design Process
that we have learned in our systems engineering
undergraduate courses at West Point, we believed we could
introduce NASA to our interdisciplinary approach for
encouraging the realization of a truly successful system.1
At first, NASA presented us with two very distinct
problems they were struggling to resolve. The first problem
dealt with how NASA could best implement a crew launch
vehicle that would eventually replace the current space
shuttle program by the year 2010. In light of the
catastrophic consequences associated with recent space
shuttle missions, this problem deals with engineering a
system that would more effectively launch astronauts into
space and allow them to re-enter earth in a more efficient
and safer manner. The second project that NASA proposed
to us dealt with developing a system that could detect and
avoid hazards while landing on the moon or any other
planet. NASA called this project its Object Detection and
Avoidance Capability (ODAC) program. Our team decided
to pursue the ODAC project because it seemed to better fit
our skill sets and appeared to more closely align with some
of the engineering class projects we had recently completed
for our studies at the United States Military Academy.
Mr. Patterson and Mr. Bryan also provided us with a list
of ten concerns for the ODAC project (see figure 1). These
Weight
Micro-meteoroids
Size
Radiation
Power & Power Sources Vibrations
Cost
Visibility
Thermal Capability
Durability
Figure 1: NASA’s Key Concerns for Object Detection
concerns are unique to every mission depending on the
mission’s goal, but at the same time significant trade-offs
exist when attempting to account for all ten concerns
simultaneously. These stakeholder concerns helped us think
through what key issues NASA wanted to solve and gave us
insights into what it wanted as a potential final solution. We
eventually realized that NASA appeared to be focused more
on existing alternatives based on current sensor
technologies, which we believe our systems engineering
background and education could help improve upon.
As Systems Engineers at the United States Military
Academy we use a process called the Systems Decision
Process (SDP) for analyzing complex problems (see figure
2). The systems decision process can be applied in any
stage in the system life cycle, and it has the following
characteristics:
• Starts with the current system and ends with the
desired end state;
• Focuses on the needs and objectives of
stakeholders and decision makers;
• Has four steps (problem definition, solution design,
decision making, and solution implementation) but
is highly iterative;
• Explicitly considers the environment (historical,
social, cultural, technological, legal, and economic)
that systems will operate in and the political,
organizational, moral/ethical, and emotional issues
that arise in the environment.2
functional analysis is to enable us to focus our resources and
energies on defining the right problem that needs to be
solved. We spent most of our first semester coming up with
a value model that our entire team could agree upon and
present to NASA as our initial value proposition for the
ODAC project.
Based on our initial analysis, we defined our problem
statement as follows: Develop a model to help NASA
determine which sensor capabilities are most critical for
autonomous landing and roving on lunar and planetary
surfaces. The greatest benefit we believe our team could
provide NASA is introducing it to a Value-Focused
Thinking (VFT) approach versus its current AlternativeFocused Thinking (AFT) design. VFT tends to be a
different way of focusing an organization’s goals and
objectives into an action plan. Values are what people really
want, and VFT offers organizations a markedly different
approach to simply choosing among various alternatives and
going with the one that fits the best. Oftentimes, when
organizations rely on AFT, they fail to take the time to
reflect on what is really important to them. Ralph Keeney, a
pioneer in the field of VFT, introduces the concept of
Constraint-Free Thinking:
“thinking about values is
constraint-free thinking . . . it is thinking about what you
wish to achieve or what you wish to have.”4 By introducing
VFT to NASA, we believe we can offer NASA a new
paradigm for examining how it researches, develops, and
designs sensor technologies for autonomous space missions.
Rather than merely selecting the best set of sensor
technologies currently available, we believe a VFT approach
will provide NASA with a more unconstrained view of
examining what sensor capabilities and requirements it
values as being the most critical for future missions. In
doing so, we believe can leverage our systems engineering
education and help NASA come up with a systematic way of
better articulating what it values most and considers to be
mission critical for space exploration.
Our team was fortunate to have Jon Paterson and Tom
Bryan support our value proposition. With our key
stakeholders eager to see what our team could do to
introduce such a different approach to the ODAC project,
we proceeded to move into the solution design phase of the
SDP.
IV. SOLUTION DESIGN PHASE AND DECISION MAKING PHASE
Figure 2: Model of the Systems Decision Process (SDP)
3
In the following sections, we will articulate the major steps
we considered for applying the SDP for our capstone
experience with NASA.
III. PROBLEM DEFINITION PHASE
The Problem Definition step of the SDP prepares us to
develop effective candidate solutions to a problem. The
purpose of conducting a stakeholder analysis and a
With NASA’s key needs, wants, and desires identified for
the ODAC project, the outcome of the solution design phase
is to develop a pool of candidate solutions in which the
“best” one can be expected to be found. This pool is refined
as it grows, checked constantly against the problem
definition, and measured against stakeholder criteria until
the best solution emerges. The resulting solution is a
process for solving the problem at hand. In our case, we
decided to build a decision support system in order to help
NASA determine what sensor capabilities were required to
ultimately come up with the best sensor combinations and
sensor suits needed for autonomous space exploration. We
wanted our model to be appropriate not only for NASA’s
near-term horizon and current missions, but to be flexible
enough so that NASA could apply it to future missions that
may not even be considered at the moment.
In order to create an effective solution for NASA, we
realized that our model would need to address a number of
NASA’s key concerns: 1) pattern recognition, 2) range
detection, 3) orientation, 4) altitude, 5) integration of
sensors, 6) facilitate autonomous & manned missions, 7)
conserve fuel, and 8) hover time. We believe that these
functions are important for all autonomous space
exploration missions and will help in determining the best
set of sensors needed to be employed on-board future
spacecraft. To help us better visualize our system of
concern, our team developed the following functional
hierarchy:
Figure 3: Functional Hierarchy of NASA’s Autonomous
Space Exploration Mission
This functional hierarchy also shows how the ODAC project
is a critical component of NASA’s overall space exploration
mission.
NASA asked us to consider these possible solutions:
Infrared, Tactical Arial Navigation, Light Detection and
Ranging, Three Dimensional Imaging, Night Vision
Devices, Outpost Guidance System, Stereo Video, and
Video Camera. By doing so, NASA indicated to us that it
was approaching the problem via an AFT methodology.
Our approach to the project, on the other hand, would be
based on a VFT methodology, which we believe will benefit
NASA in the following ways:
• Allowing it to think outside the box of current
sensor technology;
• Limiting cost and expenses on unnecessary or noncritical capabilities;
• Identifying gaps with existing sensor technology
and improving upon key existing requirements;
• Generating ideas for new solutions that NASA has
not yet considered.
Through our VFT approach, we believe we can help NASA
better articulate and pin down what sensor capabilities it
values most and then incorporate them into an optimal
action plan.
Furthermore, we believe the decision support system we
are creating will not only benefit just NASA, but can be
easily modified and incorporated into other organizations as
well. As a team we really didn’t realize this point until late
in the project, for the beauty of our model we are creating is
that fact that it can be customized to help decision-making
on a wide-range of problems. Our model will make
complex problems more manageable by providing decisionmakers with an arguably more systemic way of identifying
where they ought to focus their available resources.
V. OUR MODEL
The purpose of our model is to introduce and implement
Value-Focused Thinking (VFT) into NASA by providing it
with a user-friendly resource to focus their research and
development efforts for sensor capabilities that are mission
essential and are not widely available for current space
platforms. By accomplishing this, our model attempts to
expand the decision maker’s options beyond the sensor
technologies that are currently available, and it focuses the
decision maker on evaluating the relative importance of
various sensor capabilities across the full spectrum of a
given space exploration mission. We believe our model is
extremely robust because it not only considers current
sensor capabilities, but more importantly, permits the
decision maker to factor in whatever capabilities are deemed
to be important. Additionally, the model is flexible enough
to permit the decision maker to modify and employ it for
other critical capabilities assessment projects—not just
sensor capabilities for autonomous vehicles. It is from this
perspective that we believe we are providing NASA with a
much greater solution than simply listing a set of current
sensors for NASA to use.
Because our current problem statement focuses on
autonomous missions for landing spacecraft on planetary
bodies, we decided to focus our model on that one mission.
However, we broke the mission down into three phases:
mid-orbit, landing, and roving. This three phase approach
allows the decision maker to account for different global
weighting schemes for the overall mission. For example,
landing may be more important than mid-orbit and roving
for a certain mission. Accordingly, our model allows the
decision maker to adjust the relative importance of each
phase of the mission, and in doing so, our model is able to
capture the overall importance the decision maker has
placed on various sensor capabilities considered for the
mission.
We built the model using Microsoft Excel, which
provides a sense of familiarity to most potential users.
There are four worksheets within the model. Each phase has
its own input worksheet, and there is one final output
worksheet. We used brainstorming techniques to develop
and refine the seven capabilities that we felt would be
important to space exploration as outlined by NASA:
altitude, depth perception, movement, orientation, range,
speed, and vision. Vision is further broken down into the
seven spectrums of the electromagnetic spectrum. These
capabilities can be modified, deleted, or added onto if the
decision maker so desires. We incorporated macro scroll
bars that link an adjustable slider bar to each capability in
order to allow the decision maker to easily adjust the
importance placed on each capability. The decision maker
uses the slider bars to input how important each capability is
to that particular phase of the mission. The model than
automatically normalizes the inputs to create local weights
for each capability. The decision maker than moves on and
repeats the same process for the other two phases of the
mission.
Figure 4: Screen Shot of One Phase of our VFT DSS Model
On the final output worksheet, the decision maker again
uses slider bars to adjust the weights representing the
importance of each phase of the mission. Once all the
parameters have been input as the decision maker sees fit,
the decision maker is able to see the output of the model as a
list of capabilities that have a corresponding global weight
for the mission. The model derives the output from the local
weights for the capabilities in each phase and the weight of
each phase itself. The decision maker can than look at the
output and see which capabilities are most important. The
value of the model is that the decision maker can now make
a choice on which areas to focus research and development
based off the capabilities that are most important.
Figure 5: Screen Shot of Sensor Capabilities Ranking
Generated from Value Assessments in our DSS Model
VI. SOLUTION IMPLEMENTATION PHASE
The last phase of the systems decision process is the
implementation phase—the phase in which our stakeholders
actually use the model we created for them. Just because a
decision has been made to implement the best solution does
not imply the solution will be successfully implemented.
The implementation of a system solution is based on sound
work in (1) defining the problem, (2) generating appropriate
alternatives, and (3) selecting a solution that satisfies the
client and user needs. These events should precede solution
implementation in the previous phases of the SDP. Any
flaw in these steps will impact whether the right solution is
implemented. The success of the SDP throughout the life
cycle hinges on the success of each phase of the SDP.
There are three steps involved in solution implementation.
They are (1) planning for action, (2) execution, and (3)
assessment and control. These steps create a continuous
cycle and feedback loop that generates new and updated
information on system performance.
As continual
assessments are performed, it is determined whether changes
should be made to the system solution or whether a new and
unique system is preferred by the stakeholder. If a new and
unique system is preferred by the stakeholder, the new
problem definition will initiate the Problem Definition phase
of the SDP. By monitoring all the steps and setting up
controls within the Solution Implementation phase we
ensure our client’s needs are met and that the problem is
solved. It is an ongoing process that continues until project
termination which is determined by the decision maker.5
VII. CONCLUSION
In 2004, President George Bush articulated a number of
key milestones for NASA to accomplish: “Our goal is to
return to the moon by 2020 . . . [By] establishing an
extended human presence on the moon [we] could vastly
reduce the costs of further space exploration, making
While the
possible ever more ambitious missions”6
President’s speech has helped to galvanize renewed interest
in our nation’s space exploration program, it has also forced
NASA to adhere to an accelerated timeline that attempts to
balance both current concerns (retirement of the space
shuttle program, completion of the international space
station, etc.) with future opportunities (return to the moon,
mission to mars, etc.). Consequently, NASA’s interest in
autonomous landers and rovers has received greater
attention and research in the area is helping to lay the
foundation for future projects.
In concert with the NASA’s 2006 strategic goals, our
team has created a Microsoft Excel spreadsheet model that
will assist NASA in determining what sensor capabilities it
considers to be most critical for employment on-board
autonomous spacecraft.7 Our model is a decision support
system that examines space exploration as a series of
sequential phases, each with a distinct set of requirements
and constraints. We believe the Interactive DecisionMaking Architecture featured in our model will allow
experts at NASA to input their own parameters that we
know will inevitably change because of differing mission
requirements and advances in technology.8 Our decision
support system will help to better link NASA’s strategic
goals to its operations and thereby increase the probability
of success for future space missions. Furthermore, we
believe our model is robust enough that it can be easily
transferable to a wide range of military and industrial
applications, such as acquisitions, mission analysis, and
capabilities assessments.
ACKNOWLEDGEMENTS
We are grateful for the support and assistance Mr. Jon
Paterson and Mr. Tom Bryan have provided to our team
throughout our capstone experience. They have provided us
with an inside look into our nation’s space exploration
program, given us the opportunity to put into practice much
of the theory we have been taught, and allowed us to
demonstrate our talents and skills on an important, complex,
and real-world problem. We are better students, problem
solvers, teammates, and future leaders as a result of their
continual encouragement and support on the project.
REFERENCES
[1] United States Military Academy Department of Systems Engineering.
(2007). Systems Decision Making in Systems Engineering and
Management, Fall 2006 Edition. USA: Wiley Custom Services.
[2] Ibid.
[3] Ibid.
[4] Ralph L. Keeney. (1992). Value-Focused Thinking. Massachusetts:
Harvard University Press.
[5] USMA DSE. (2007).
[6] President George W. Bush. (2004). Presidents Bush’s address to NASA
on
January
14
2004.
accessed
3/21/07
from:
http://www.whitehouse.gov/news/releases/2004/01/20040114-3.html.
[7] National Aeronautics and Space Administration. (2006). NASA’s 2006
Strategic Goals.
[8] Sam L. Savage. (2003). Decision Making with Insight. Belmont, CA:
Brooks/Cole.
BIOGRAPHIES
Austin M. Bartlett is a senior at the United States Military
Academy who is majoring in Systems Engineering. He was
born in Ft. Benning, Georgia. As the son of an Army
officer, Austin lived in many different locations throughout
his life, including Panama. His family eventually settled
down in Fayetteville, North Carolina. Upon graduating
from West Point, he will be commissioned into the Army as
a 2nd Lieutenant in the Aviation branch.
Louis S. Harrington is a senior at the United States
Military Academy. He is an Engineering Management
major with a Nuclear Engineering track. Louis lives in
Houston, Texas, and was born in Cody, Wyoming. Upon
graduation he will be commissioned into the Army as a 2nd
Lieutenant in the Engineer branch.
Jason C. McKay is a senior at the United States Military
Academy who is an Engineering Management major. He is
originally from Bridgewater, New Jersey, and will be
commissioned into the Army as a 2nd Lieutenant in the
Infantry branch.
Quentin L. Willard is a senior at the United States
Military Academy. He is an Engineering Management
major with and Electrical Engineering track. He lives in
Van Buren, Arkansas and will be commissioned into the
Army as a 2nd Lieutenant in the Field Artillery branch.
Maj. Ernest Y. Wong is an assistant professor with the
Department of Systems Engineering at the U.S. Military
Academy. He holds a B.S. in Economics from USMA, and
both a M.S. in Management Science & Engineering and a
M.A. in Education from Stanford University. His recent
NASA fellowship with the Exploration Systems Summer
Research Opportunities program at the Marshall Space
Flight Center has made it possible for him to provide a
similarly rewarding experience for cadets whom he advises
at West Point.