Progressive Autonomy for Optimized Mission Design, Training, and Operations

Cindy Marie Kurt* United Space Alliance, LLC, Houston, TX, 77058

Decisions about autonomy in operations are fundamental to the design of future space missions. Leveraging the capabilities that intelligent systems match or surpass over humans enables optimized human and system performance. The resulting human roles and autonomous technologies in turn define the operations concepts for future space programs, and serve to define vehicle and ground systems design requirements. The early introduction and adoption of novel human-systems interactions will promote a culture that is aligned with the program’s operations paradigm from the beginning. We report on the initial results of a trade study to assess the role of autonomy in the current manned space flight program. Autonomy focus areas, considerations, and lessons learned as determined by Space Shuttle and International Space Station operations personnel are presented. An overview of current autonomous technology development projects that address several of these focus areas are presented.

I. Introduction HE space community is interested in the potential impact autonomous and intelligent systems can have on improving operator effectiveness by providing enhanced capabilities for situational awareness onboard and in

ground systems. The goals of such applications are to enhance human performance and system performance by using their respective strengths in the most effective combination to achieve mission success, increase safety, and reduce risk. These goals will not be realized unless the conscious effort to optimize human and system roles for new vehicles and systems becomes an overarching requirement.

T

To this end United Space Alliance, LLC (USA), the mission operations prime contractor to NASA for the Space Shuttle Program (SSP) and the International Space Station Program (ISSP), is engaged in a comprehensive systems-level trade study to identify lessons learned and assess the need and impact of introducing more autonomy into the current Space Shuttle and International Space Station (ISS) Programs. The study results may also apply to considerations for the Crew Exploration Vehicle and other new space systems under development at NASA. The trade study report compiles a history from pre-SSP through present day ISS operations, and is several hundred pages in length. This document provides select highlights from that report, with a focus on introducing autonomy into onboard and ground systems for more effective in-flight situational awareness and operations.

II. Mission Operations Overview

A. Plan-Train-Fly Paradigm Mission Operations is comprised of numerous, overlapping technical activities that can be categorized into three

broad areas: Plan, Train, Fly. The mission operations team includes flight controllers, trainers, and mission design personnel that support SSP and ISS operations 24 hours a day, every day of the year. An overview of the Plan-Train-Fly system1 is provided in Figure 1. The trade study highlights provided in this document focus primarily on mission execution considerations.

* Project Lead, Advanced Technology Department, 600 Gemini, USH-405L

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SpaceOps 2006 Conference AIAA 2006-5533

Copyright © 2006 by United Space Alliance, LLC. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

Mission Operations: Plan-Train-Fly System

Mission OpsIntegration

Program RequirementsMission ObjectivesAssessments & TradesVehicle Support

Mission DesignLaunch & Entry DesignOrbit & Rendezvous DesignConsumables Management

Mission Planning Long-term and Short-term PlanningSystems, EVA, Robotics, & Payloads IntegrationIntegrated Planning System Development

Mission ProductsCrew & Ground Procedures DevelopmentFlight Rules Development

Mission TrainingCrewFlight Controllers International PartnersManagementIntegrated Simulations

Training FacilitiesShuttle and Station SimulatorsVehicle MockupsNeutral Buoyancy Lab (EVA)Classroom & Computer-Based Lessons

Mission FacilitiesMission Control CenterIntegrated Mission Planning SystemExternal Interfaces (MSFC, KSC, International Partners)

Mission ExecutionFlight Readiness ReviewsReal-Time Flight OperationsFlight Directors and ControllersInternational Operations IntegrationReal-Time Planning

Plan Facilities

Plan

Train

Fly

Although some automated tools and capabilities exist in most areas, the majority of practices are human-intensive. The introduction of new autonomous systems ideas and operations concepts is ongoing, but many challenges including system certification and program-wide implementation remain. A central methodology has been needed to assess the overall impact – benefits and obstacles – of such large-scale changes to the current paradigm.

Figure 1. Mission Operations Plan-Train-Fly Activities1

B. Trade Study Methodology United Space Alliance, as part of a larger collaboration2 with the NASA Ames Research Center, documented

baseline practices in the Space Shuttle and International Space Station Programs to identify candidate functions and processes for autonomous operations, and to estimate the impact of bringing automation into these processes. USA’s initial objectives were to

• Perform a baseline analysis of the distribution of ground crew time for station and shuttle between the major systems. For each system, document how time is spent (plan/train/fly tasks, routine monitoring, failure isolation and recovery, crew support, etc.).3

• Estimate the expected costs to develop, and more importantly to flight validate, flight and ground autonomous systems.3

The latter objective included defining focus areas for potential autonomy consideration. To assist with focus area definition and target implementation area (onboard or ground system) a decision tree was constructed. Select decision points are provided in Table 1.

High-Level Decision Points for Autonomous Systems Is the reaction time required less than the communication latency? Is the retention and training of Subject Matter Experts still required for other tasks? Are the autonomy lifecyle costs significantly greater than simply adding personnel? Do engineering or structural barriers exist that preclude autonomy? Is human control inherently unsafe in performing the task? Is the crew available to execute the task? For existing Programs – Is the cost of retrofitting autonomy into the Program design excessively risky or costly? For new Programs – Is the Program likely to undergo significant redesign or descope before becoming operational?

Table 1. Select Decision Points from the Trade Study Decision Tree

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For complete details on the decision tree, methodology, and tools used please refer to the original trade study document.3

III. Autonomy Focus Areas Each major mission operations discipline within the Spaceflight Systems, Flight Design and Dynamics, and

Space Flight Operations organizations at USA provided inputs to the trade study to identify autonomy focus areas for the current ISS and Space Shuttle Programs. In some cases specific tasks were recommended. General flight operations recommendations were also compiled. In addition to technical functions, an initial assessment of where to implement the autonomy (onboard or ground systems) is provided. Table 2 provides an overview of the results by listing several entries from each organization.

Ground Crew

Ground Autonomy

Flight Crew Flight Autonomy

Safety/Mission

Schedule/Executability

Extensibility Affordability

Automate First Response actions to all Emergency Class failures (e.g. powerdown equipment, FSS Suppression, PCS reconfig, etc).

X X 4 3 4 1

Automate continual atmospheric monitoring of trace contaminants (e.g. Halons, combustion products such as HCN, HCL, HF, CO)

X X 4 2 4 2

Automate systems powerdown (e.g. ISS loadshed) X X 2 3 3 1Include and automate the execution of root cause analysis programs into the flight software. X X 3 3 3 2

Automate error identificaiton in communication and instrumentation equipment (e.g., incorporate ingelligent BITE circuitry to provide sensing to the onboard software).

X 3 3 4 4

Automate communications and instrumentation (ground, crew, automated software) X X X 3 3 4 2

Automate the execution of inertial platform alignments X X 4 3 4 2

Automate the tracking of rendzvous targets X X 4 3 4 2

Automate landing capability; (e.g., landing gear deploy).X X 2 3 1 1

Automate landing capability; (e.g., nosewheel steering; braking).

X X 2 3 1 1

Automate deorbit targeting X X 3 3 1 1

Automate the computation and execution of rendezvous in-plane and out-of-plane maneuvers during approach

X X 4 3 4 2

Automate commanded orbital manuevers (TLI and TEI burns)

X X 3 3 4 4

Include and automate goal-based flight planning software X X X 2 2 4 2

Include and automate goal based adaptive coaching and training system (e.g. crew just-in-time training or retraining)

X X 2 2 3 2

Automate nominal and failure driven procedures including command string generation, validation, and execution

X X X 3 3 4 2

Fault Detection, Isolation and Recovery: Over-rideable by crew and ground; modular and upgradeable;

X 4 3 4 3

Flight Design and Dynamics

Space Flight Operations and Training

Descent

On-Orbit Operations (Rendz/Prox Ops/Sep)

Mechanical Systems

Electrical Systems

Communications

GN&C

General Recommendation

Automation in Flight OperationsFlight Operations System / Function Where to Do the Function on CEV Automation Rationale

Environmental SystemsSpaceflight Systems

Table 2. Selected autonomy focus areas by mission operations discipline, from USA’s ongoing Autonomous Operations Trade Study report3

Rationale categories are provided for each entry, providing a subjective assessment of the viability of the

recommendation. The rationale categories are: • Safety/Mission Success: Degree to which automation ensures system safety and mission criteria • Schedule/Executability: Degree to which automation enables timely mission planning and execution • Extensibility: Degree to which automation applies to future missions • Affordability: Degree to which automation development and implementation costs fit within program budgets

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Grading levels were assigned to each recommendation/rationale according to (subjective) past experience. The levels progress as follows: 1=Poor; 2=Fair; 3=Good; 4=Excellent. A rigorous treatment of safety, risk, cost, mission success and other factors relevant to the impact of autonomy on operations is planned for the second half of the trade study.

IV. Autonomy Considerations In the day to day operation of NASA’s manned spaceflight program, subject matter experts (SMEs) utilize their

training and experience to identify, analyze, and act on numerous mission novelties, anomalies, and interruptions, in addition to the nominal operations tasks planned for their shift. Potential benefits from autonomous systems are large. However, care must be taken when considering an autonomous technology development effort. Automating a function because the technology exists to do so does not guarantee the most efficient use of program resources. For example, providing autonomous capabilities may not be cost-effective when subject matter experts must be trained, certified, and retained for mission contingencies or flexibility. Some of the more common misconceptions about operations that can derail autonomous system projects (technical success as well as operator acceptance) are described here.

Misconception: During “nominal operations” system health and status parameters are static or always fall within limits

A common argument for autonomy is to claim nothing significant happens during nominal operations. Thus computer software should be used to monitor the data and report on deviations from nominal, usually by using data thresholds, simple parameter relationships, or more complex system models. What defines nominal operations? Nominal implies routine, planned, understood, and expected. One example of a nominal activity is an ISS maneuver, such as an attitude change or “reboost”. During the maneuver event, data in many systems appears anomalous or “off-nominal”. For example, jitter in control moment gyroscope (CMG) gimbal data is often seen. After the event, the data returns to normal. But normal may mean the CMG is operating nominally, but the data baseline has shifted due to the new vehicle configuration. Such a shift may demand retraining anomaly detection algorithms that were optimized for the previous CMG baseline. Figure 2 provides an example.

Figure 2. Example of nominal CMG response during an attitude maneuver

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Another example of where “nominal” jitter is sometimes seen in CMG parameters is when the robotic arm of the ISS is moving, as depicted in Figure 3. The flight controllers are aware of the context of the mission and can quickly understand changes in data resulting from system interdependencies. However, an anomaly detection system model that does not include these nominal mission events or cannot discern such an event is occurring will provide too many false positives.

Figure 3. Example of nominal CMG response in top three plots as the ISS robotic arm is moving, shown in the bottom plot

Misconception: SME resource requirements are eliminated when autonomous functionality is implemented

It is desirable to have self-governing systems, leaving SMEs to concentrate on more challenging tasks and to step in only when the autonomous system indicates it cannot resolve a problem. Inherently this requires SMEs to be retained and available for mission contingencies. Furthermore, it is difficult to anticipate the number of anomalies or contingencies that will be encountered with a new vehicle/system or a new mission objective.

Figures 4a and 4b show graphs of the number of Items for Investigation (IFI) by year for the ISS from 1998-

2005, and the number of In-Flight Anomalies (IFA) by mission for the Space Shuttle from STS-98 to STS-113, respectively. While almost all of the IFAs and IFIs are well understood and closed, the graph shows a sustained identification of anomalies each month/mission even as the Programs mature. Each item warrants investigation by SMEs to determine root cause and any resulting actions. The numbers illustrate there is not a guaranteed elimination of flight control personnel simply because new systems are introduced into a discipline to autonomously monitor telemetry and detect change.

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ISS IFI Count History

Freq

uenc

y

20052004200320022001200019991998

250

200

150

100

50

0

Histogram of ISS IFI Count History

Figure 4a. Count by year of ISS Items For Investigation. A root cause analysis and disposition is performed for each IFI before closure.

SSP IFA Count History

Freq

uenc

y

11211010810610410210098

40

30

20

10

0

Histogram of SSP IFA Count History

Figure 4b. Count by mission of In-Flight Anomalies from the Space Shuttle Program. The x-axis shows the mission number in sequential order (e.g. STS-98, STS-113). A root cause analysis and disposition is performed for each IFA before closure.

Misconception: At vehicle assembly-complete the core component behavior and resource loads are fixed

The goal is to have core systems (e.g. propulsion, thermal) and resources (e.g. fuel, water) adequately modeled and useful for intelligent reasoning and planning systems once the system design is fully operational. But other considerations exist that will not be fixed. The crew is a commonly overlooked consideration. The unique characteristics of individual crew members determine human-system interactions. Oxygen consumption rates and exercised-induced vehicle vibration are two examples of parameters that are difficult to model before the crew is onboard for some time. An example of the latter is given in Figure 5.

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Figure 5. Crew exercise-induced vibrations along the ISS contribute to differences in attitude rates as measured by sensors in the United States segment (US) and the Russian segment (RS) several hundred feet apart. Crew members were scheduled to perform exercises between 10:30 – 13:00 for the day plotted.

Another group of considerations are changes from equipment degradation, failure, fix, or workaround during the

mission. As each scenario is encountered the autonomous system must adapt to the new environment. Autonomous system developers must realize their models may need to incorporate new content – not simply by-pass failed components in the model - even after the vehicle and vehicle systems are mature.

Misconception: All necessary telemetry are available for analysis

Today the ISS has tens of thousands of parameters routinely monitored by flight control teams on the ground. Many telemetry values are updated once a second on the ground systems (change-only updates), and a typical flight control discipline monitors hundreds of parameters at a time. Intelligent systems that monitor a multitude of parameters and follow complex system interactions cannot reach their full utility if those parameters are not guaranteed to be available for analysis. Future systems can prevent this situation with focused operations and design planning. But for current vehicles such as the ISS, all the telemetry parameters cannot be simultaneously sent to the ground for analysis due to bandwidth limitations. Significant quantities of data cannot be stored onboard indefinitely for later retrieval either, as sufficient storage space is not available. As the ISS is built, the flight controllers are developing lists of which parameters will and won’t be sent to the ground at certain times, far in advance of the execution timeline. Additionally, scheduled periods of communication outages can mean data for the outage range is never sent to the ground. For representative examples, see the gaps in the telemetry in Figures 2, 3, 5, and 6. Autonomous systems must to take into account the available onboard processing capabilities, bandwidth constraints, and onboard data storage in their design. Misconception: Autonomous FDIR actions are “better” because they are guaranteed faster and correct

Automating system response promises to speed up response time and alleviate human error. But such recovery actions can only be trusted if the system state and all possible state evolutions during execution are understood and certified to end with the desired result. The current effort to introduce autonomous command generation-validation-execution for the ISS illustrates this fact. In 2004 over 127,000 commands were uplinked by the ISS flight control teams. The success rate is over 99.9%. But the task is time consuming and an autonomous system is highly desirable. There is one particular class of command errors, commonly identified in Station Program Notes (SPNs), that challenge the ability to provide such a system.

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SPNs are written to document deficiencies, peculiarities, or counter-intuitive operation of the ISS flight software. A SPN may indicate a documented command procedure has a workaround for a specific scenario. Identified workarounds can be incorporated into the logic of an autonomous commanding system. But the underlying problem is the flight software is not always guaranteed to react exactly as expected in unique scenarios, and it is impossible to predict every scenario in which exceptions will occur. The inability to identify such scenarios before they occur negates any assurance that an automated command sequence will continue to be correct during execution as the vehicle state continuously changes. Therefore speed may not ensure correctness for ISS commanding, and may in some cases prevent it.

Misconception: SMEs staring at numbers reviewing data patterns never adds value

Computer systems today are capable of recognizing data patterns as good or better than human operators. Examples of the latter include patterns that develop over long periods of time (weeks, years), or multi-system patterns since operators focus their telemetry review primarily to their own discipline parameters. However, robust pattern-matching algorithms are not yet context aware. Realizing that parameter A and parameter B have recognizable and related signatures is valuable, but it currently takes an SME to understand the implications of those signatures in the larger context of the vehicle and crew. For example, SMEs have noticed the overall amplitude in the signal of the outer gimbal rate parameters of the CMGs on the ISS is noticeably less at certain periods of the day. Figure 6 shows an example. The flight controllers have realized this pattern occurs when the crew has gone to sleep. There is no telemetry parameter, be it a light switch sensor or any other mechanism available to the flight controllers, to directly determine if the crew has retired for the day. But SMEs have discovered certain CMG parameters provide this situational awareness in addition to relaying the physical condition and state of the hardware.

Figure 6. The crew sleep period for day 129-130 is distinguishable in this plot of a CMG outer gimbal rate parameter.

Misconception: Available technology should drive autonomy choices

While it is true that existing or emerging technology solutions are a driver for autonomous systems, a key area often overlooked is human-system integration. Which tasks are uniquely human in nature and which can be accomplished by an intelligent system with no reduction in safety, mission success, and other key factors? For example, flight controllers plan and replan the daily activities of the astronauts. Numerous inputs must be factored into any crew schedule including resource consumption, power availability, communications availability, duration of activity, pre- and post- activity requirements, etc. But the process is time consuming and to a large extent a constraint-based resource scheduling activity. Autonomous scheduling systems can effectively plan and replan onboard activities while adhering to the same constraints a human planner would enforce. Such a system would free up significant time for the flight controllers to work on other tasks.

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V. Autonomy Alignment with Operations Concepts and Culture The concept of mission operations is developed around human and vehicle/system abilities. Careful

consideration must be given to this design allocation upfront. Large-scale changes to established operations concepts or mature vehicle/system designs are often met with resistance because they are perceived as too risky or costly to implement due to their extensive impact on mission design. Whether the program is existing or new, all mission operations resources are affected including people, processes, procedures, hardware, software, certifications and training.

Each resource helps define the culture of the program. Culture shapes the foundation of the program including organizational structure, norms, beliefs, and expectations. Poor upfront human-systems integration can prevent the desired operations concepts and culture from being realized, whereas the correct balance of human-system integrated operations can maximize investments across the program. Key operations culture issues are summarized in Table 3.

Cultural Challenges for Crews and Ground Personnel

Concern of loss of expertise in systems knowledge Concern of over-reliance on tools Large changes to established team communication and behavior norms Perception of loss of status /sense of accomplishment when execution tasks are transitioned to autonomous systems Perception of loss of control when situational awareness tasks are transitioned to autonomous systems Uncertainty with the new trust hierarchy: Which is more reliable – the human or the system?

Table 3. Cultural Considerations that Impact Autonomous Systems Acceptance

Challenges such as these if not addressed properly can undermine the best of technology advances. To address

these challenges, lessons learned were provided by USA operations personnel who are engaged in autonomous system development and certification.

VI. Lessons Learned

Select Lessons Learned from current USA autonomy development efforts within the SSP and ISSP are given here. Each Lessons Learned resulted from the exchange of ideas and recommendations within mission operations from key aspects of the autonomy effort including SMEs, trainers, system developers, project leads, and management.

• Operations concepts should start before vehicle design. Operations concepts and vehicle designs should evolve together.

• Engage the operations community early in autonomous system application development to understand operations challenges, preferences, expectations, and acceptance criteria (both for ground flight controllers and the onboard crew).

• Optimize human roles and system roles to focus autonomy investments and maximize autonomy benefits.

• Trust in systems is earned, just like it is with people. If operators are uncomfortable with or unconvinced of the perceived merit of an autonomous solution they will exclude the technology from their operating concept and culture.

• Autonomy solutions are not one-size-fits-all. Each discipline has unique challenges and requirements. • The impacts of autonomy are not exclusive to mission execution. Mission design and training are

significantly impacted as well. Consequently it is not sufficient to solely test the autonomous technology in the execution environment. All aspects of the Plan-Train-Fly system, particularly the operations concepts, must be tested as well.

• Culture alignment with new operations concepts and technologies is essential to achieve peak human operations performance.

VII. Validation with Emerging Technologies

Mission Operations personnel from United Space Alliance are continuously involved in the development and testing of autonomous capabilities and their impact to operational concepts and culture onboard and within ground

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systems for the International Space Station Program and the Space Shuttle Program. Several ongoing autonomy projects within the Advanced Technology Department at United Space Alliance are summarized in this section.

USA is testing new autonomous capabilities that assist flight controllers with data monitoring, anomaly detection, and decision support. The initial tool set is built from several integrated research and development projects that assimilate real time telemetry, analyze the telemetry using spacecraft system models and machine-learning algorithms, provide the results in an effective visualization to the mission control personnel, and link the results to related flight rules, commands, procedures and other related artifacts using an ISS ontology. The underlying communications framework integrates multiple telemetry streams and archival data, manages inputs/outputs between the computational and decision algorithms, and outputs the results to various visualization tools and/or a data archive. Major components in work are outlined below.

1. Data analysis and summarization (clustering, forecasting algorithms)

Machine learning methods, including clustering and forecasting algorithms, are used to identify nominal, novel, and anomalous telemetry and telemetry patterns. Archival ISS data is used to identify telemetry signatures and train the algorithms. User-initiated or predefined actions can force the algorithms to retrain to compensate for nominal change. A measure of novelty is also provided to help assess the magnitude of the deviation from expectations.

2. Adaptive displays and intelligent visualization

Projects in this area focus on taking algorithmic output and transforming it into useful knowledge by applying intelligent decision points to determine optimal visualizations. The visualizations are provided to the user automatically or on-demand, as defined by individual user or flight control discipline preference. The visualizations include enhanced versions of existing, certified flight control displays and new data summarization techniques.

3. Knowledge fusion emphasizing ontological search

Projects in this area focus on knowledge representation, and include the design and construction of tools and methodologies for efficient information organization, identification, and extraction. To date we are building a searchable ISS ontology to bring complex domain relationships, operations artifacts, and subtle system/procedure interdependencies to the flight controller’s console in two ways. The first is an automated response to address an anomalous or pre-defined scenario. Relevant artifacts are presented to the user as determined by intelligent visualization rules and ontological relevance. The second is a query-driven graphical user interface. The interface further provides ontology authoring and extension.

4. Model-based reasoning/planning and root cause analysis

A USA-Texas Tech University collaboration over the past several years has provided a model-based reasoning, planning, and diagnostics tool using answer-set programming and enhancements to the A-Prolog language. The system will aid flight controller’s response to complex, multi-failure situations. The system reasons about all of the possible ways to achieve defined goals while taking into account current system failures or degradations. The output contains all viable plans for achieving the goals using available command actions. The system can also be used to forecast or envision failure scenarios and appropriate responses. Future work includes mapping plans to executable procedures and commands for robust situation response and recovery.

5. Multi-agent architectures for distributed operations

Multi-agent architectures help address the problems of distributed expertise, control, and priorities in a distributed operations environment. Agents act autonomously, negotiating and collaborating with other agents to achieve specified goals. We are investigating two multi-agent architectures for use with the afore-mentioned capabilities to efficiently handle dynamic and geographically distributed resources across heterogeneous systems. Our focus to date has been on self-healing pathways for real time telemetry acquisition and dispersion in support of ubiquitous mission operations flight following, anomaly detection, and recovery.

Additional projects include a NASA-sponsored collaboration with Dr. Michael Turmon at the Jet Propulsion Laboratory to provide time series search, novel vehicle health and status visualizations, and additional anomaly detection capabilities4, and a new USA-sponsored university collaboration to help automate command generation, validation, and execution. In each case, identifying changes to current operations concepts and potential cultural impacts are a key part of the development effort. As emerging autonomous capabilities are progressively introduced and tested the entire Plan-Train-Fly paradigm will be exercised and evaluated to help optimize and integrate autonomy appropriately within mission design, training, and operations.

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VIII. Conclusion

Autonomy decisions are fundamental to overall mission design. Leveraging the capabilities that intelligent systems match or surpass over humans enables optimized human and system performance, drives the operations paradigm, and helps mold the Program’s culture. As increasing focus is placed on NASA’s new Constellation Program, more resources will be dedicated to autonomous technology development, verification, and operation to meet the requirements of the new program. Such improvements have the potential to impact the human-system relationship in all future manned space endeavors. To this end, the cost-benefit-risk analysis planned to further our trade study work will facilitate an informed and focused application of program resources towards autonomous systems targets and goals in the current Space Shuttle and ISS Programs, as well as future Constellation Programs.

Acknowledgments

The work reported herein was performed under NASA contracts NAS9-20000 and ARC05AC43C. The author would like to thank Patrick Walter for providing many of the telemetry plots in this paper. The author would like to thank ISS Flight Controllers Karen Bush, Tatiana Dobrinskaya, Michael Lammers/NASA, and former SSP Flight Controller James Solomon for their invaluable technical insight and mission operations examples. Telemetry plots in this paper were created using the Java Mission Evaluation Workstation (JMEWS) tool developed by the Engineering & Science Contract Group (ESCG) at the NASA Johnson Space Center.

References

1 Harpold, Jon C., “Mission Operations Directorate Overview,” Jan 2002, available from http://mod.jsc.nasa.gov, accessed 6 June 2005. 2 Crawford, James. PhD, “Project Plan: Trade Study on Autonomous Operations for the Crew Exploration Vehicle” National Aeronautics and Space Administration, Ames Research Center, 6 March 2005. 3 Solomon, James & Wells, Cynthia., “Trade Study on Autonomous Operations for the Crew Exploration Vehicle: Flight Operations Considerations”, United Space Alliance, 30 November 2005. 4 Turmon, Michael. PhD, “Project Plan: Decision Support System for Health Management (DSS)”, Jet Propulsion Laboratory, California Institute of Technology, 12 November 2004.

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