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INTELLLIGENT SYSTEMS ROADMAP
Topic Area: Intelligent Systems for Increasing Ground System Automation
Paul Zetocha, AFRL/RV, Kirtland AFB, NM
Chris Tschan, The Aerospace Corporation, Colorado Springs, CO
Introduction
This contribution to the Roadmap for Intelligent Systems will focus on the increased
automation of ground systems. Space operations are used when needed to illustrate
potential implementation. However, the capabilities, roles, and challenges for increased
ground system automation are similar for other aerospace domains.
Ground systems for space operations perform functions such as space vehicle
commanding, mission planning, state of health monitoring, and anomaly resolution, as
well as the collection, processing, and distribution of space systems payload data.
Traditionally the ground segment of most space programs has received less emphasis
than the development of the on-orbit asset(s) which has hindered the advancement and
implementation of ground segment technologies. This is one of the reasons why ground
system functionality for maintaining safe spacecraft operations, maneuvering, and
responding to anomalies have not changed substantially in recent years.
Intelligent Systems Capabilities and Roles
Description of Intelligent Systems Capabilities
There are few intelligent systems being used today by ground systems for space. Part of
the reason for this is the risk adverse nature of space programs. The result is that the
number of people generally required to support manual space operations is larger than it
needs to be and the cost for space operations remains higher than it should be. There is
demand to drive space system operations costs down, reduce the response time to the
detection and resolution of anomalies, and to reduce the potential for human error.
Recently there has been increasing acceptance of intelligent systems technologies. This is
opening up opportunities for intelligent systems to contribute and a likely place to start is
to focus on intelligent automation. A path forward is to develop and demonstrate tools
that facilitate reliable, trustworthy intelligent automation of technical tasks currently
performed by competent, ground system operators.
A short list of desired intelligent systems capability goals include the following:
- Reduce the probability for human commanding error
- Avoidance or minimization of anomalies
- Optimization of spacecraft operations to extend mission life
- Increase mission productivity and data return
- Remain human-on-the-loop, but minimize the need for human operators
- Reduction in unused ground system computational capabilities, replaced by
automated prioritization and management of lower priority (background) tasks
- Reduction in elapsed time to detect and make decisions
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Increase situational awareness of potential threats to satellite/mission health
Intelligent Systems Roles and Example Applications
Intelligent systems could perform the following roles during a phased increase of ground
system automation for space operations:
- Optimization of resource scheduling and mission planning
- Archive, analyze, and quantify technical skill of human Subject Matter Experts
(SMEs) currently performing technical tasks on ground systems
- Monitor Operator actions and advise SMEs when an action proposed to be taken
on a ground system has previously resulted in undesirable results
- Hot back up to take over limited ground system control from human operators
- Automation of low-level technical activities at a ground system
- Health and status monitoring of spacecraft telemetry
Technical Challenges and Technology Barriers
Technical Challenges
Due to the historically risk adverse nature of space programs the state of practice with
regards to the use of intelligent system technologies for ground system automation is at a
low technology readiness level (TRL). Many ground and flight based intelligent systems
prototypes have been developed within various laboratories and in many cases have been
demonstrated in limited shadow mode operations. However, far fewer intelligent systems
tools have made their way into spacecraft operations. Intelligent systems are not
automatically considered as the technology best suited to providing increased ground
system automation for domains such as space operations. To overcome this, the
intelligent systems community needs to demonstrate the technical ability to perform these
functions and to quantitatively show improvements in response time, reduce costs, and
increased system performance. Our goal is to raise the TRL level for easy-to-adapt,
hierarchical intelligent automation to TRL 6.
Another technical challenge may come from the traditional automation community.
Traditional automation development usually involves an outside organization studying
current operations, analyzing work flow, decomposing human tasks, followed by the
recommendation to conduct first principles software development that creates custom
automation for that specific operation. While that process works, we propose an
intelligent systems alternative here that compliments this approach. This intelligent
automation approach may prove to be faster, less costly and may end up being more
easily trusted. This approach involves expanding on the concept of an intuitive
application that uses human SMEs mentoring as the basis for the intelligent automation.
There are a number of technical challenges associated with successfully achieving the
vision articulated above. Several of them are articulated below.
a. Converge on a universally accessible intelligent automation framework
b. Establish extreme ease-of-use capability
c. Establish the ability to archive human actions, decisions, and outcomes in order to
provide traceability
d. Ensure modular archival, so that human technical expertise is never lost
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e. Establish the ability to score the success of individual humans, human teams, and
the intelligent automation on specific tasks or ensembles of tasks, as well as how
those scores evolve over time
f. Establish the capability for the intelligent automation to learn/adapt in order to
attempt to improve its success rate
g. Establish the ability for human reviewers to easily review specific actions and
provide constructive feedback both to humans and the intelligent automation
h. Ensure the intelligent automation the ability to access, use, run and manage the
majority of existing lower-level intelligent systems
i. Determine innovative operator training methods so operators can step in and take
over operations if needed
j. Establish the ability for the intelligent automation to perform as a test bed for both
intelligent and non-intelligent techniques, so that the platform can be used to
evaluate suitability for various techniques application toward performing a task
k. Determination for how to characterize and adapt to uncertainty in reasoning
systems that perform space operations
Along the way, we desire the ability to evaluate more sophisticated aspects of intelligent
automation as feedback for iterative development and in order to make accurate
recommendations for technology adaptation. We expect to conduct experiments to
document skill in performing deterministic versus non-deterministic tasks, long-term
versus short-term tasks, as well as success rates for adaptations on systems that are
dynamically stable as well as systems that have instability issues. As the practical ability
of the intelligent automation matures in the long term we anticipate not having to specify
the algorithms used to intelligently automate a task. Instead we anticipate the intelligent
automation having the ability to test several solutions and determine/converge on the best
suited algorithm to perform the task.
Technical Barriers
Many of the technologies needed to achieve the desired intelligent automation vision
exist today. So achieving this vision is less an exercise in fundamental research, and
more of an applied development activity without substantial technology barriers.
Policy and Regulatory Barriers
Our vision is for increased human-on-the-loop automation, not autonomy, so we don’t
expect regulatory barriers. There will be information assurance and cyber security
barriers for intelligent automation to overcome since this capability is a suite of
algorithms performing functions potentially across multiple domains that previously were
performed by humans. It would be desirable for parties responsible for information
assurance and cyber security policy to be thinking now of methods for successful
certification of intelligent automation software.
Impact to Aerospace Domains and Intelligent Systems Vision
This contribution to the Roadmap for Intelligent Systems may provide a higher-level
perspective to the vision for intelligent systems for aerospace applications that is not
addressed elsewhere in the roadmap. For example, if successful this intelligent
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automation capability may have relevancy to numerous aerospace domains along the
spectrum from conducting long-term research and development to performing day-to-day
operations.
Research Needs to Overcome Technology Barriers
Research Gaps
To make intelligent automation of ground systems (such as those used for space
operations) a reality, there is applied research required to develop a generic suite of easyto-use automation tools that would match the functionality described in the vision above.
Research is also needed in for how to best make use of uncertainty inherent within nondeterministic systems.
Operational Gaps
The lack of tools for easy automation of ground system activities leads to the
continuation of highly manual and expensive status quo operations. Further, there is no
ability to capture and comprehensively quantify the skill of the humans performing these
operations. As a result, we don’t really know how good they are or when the next human
error could result in loss of control of a billion dollar space system. Tools and methods
are needed in order to help quantify the benefits and increase the trust of automated
systems over traditional methods.
Research Needs and Technical Approaches
Without getting into technical design aspects of intelligent automation software
development, the following describe functionality of the desired software suite:
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Develop or implement a generic software framework that can execute all or nearly all
existing intelligent automation and intelligent systems algorithms
Develop an intuitive capability for organizations to easily monitor and archive human
SME system operators performing specific technical tasks including outcomes
Develop the capability for management to easily review, evaluate, and establish a
quantified skill level based on individual and aggregated sequences of archived
decisions made by either SMEs or the intelligent automation in conjunction with the
current/historical information available at the time the decision was made.
Develop the capability for goal-driven intelligent automation to learn from the
archives of human sequences of actions and skill levels to create modified timing and
sequences of actions that may increase the intelligent automation’s skill level over
that of individual human SMEs.
Allow intelligent automation with the capabilities above to continue monitoring
SMEs as a safety net, notifying them if an action they are taking could result in an
adverse outcome.
Implement the ability for the intelligent automation to be certified to conduct specific
tasks with a human-on-the-loop either as a hot backup or the primary.
Our technical approach to testing this intelligent automation is to begin with individual
serial tasks performed on a space operations ground system and evaluate performance on
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those tasks. Then we anticipate evaluating intelligent automation on parallel tasks.
Assuming positive results, we would follow this with the evaluation of the new capability
on multiple hybrid (serial and parallel) tasks. Finally, we desire to evaluate the ability of
intelligent automation to manage hierarchical tasks, especially for instances where the
intelligent automation gets to manage lower-level serial, parallel and hybrid tasks. A key
to success will be the ability to accurately quantify the improvements over traditional
methods.
Prioritization
Our first priority and the main impediment is not technical, but rather insufficient funding
levels. We have proposals, concepts and designs, but we do not currently have funding to
fully pursue them.
Our second priority is securing a small technical development team with the proper skills.
To be successful, we don’t just need developers, we need the right developers. Access to
ground operation facilities and ground operators is also critical.
Third, having seen the outcome of DARPA funding the past decade that resulted in
Apple’s Siri and the Grand Challenge that ultimately resulted in Google cars, we’d like to
suggest a similar event for intelligent systems. Consider encouraging DARPA to hold an
Intelligent Systems Challenge.
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