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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records

Robust telescope scheduling

This paper presents a technique for building robust telescope schedules that tend not to break. The technique is called Just-In-Case (JIC) scheduling and it implements the common sense idea of being prepared for likely errors, just in case they should occur. The JIC algorithm analyzes a given schedule, determines where it is likely to break, reinvokes a scheduler to generate a contingent schedule for each highly probable break case, and produces a 'multiply contingent' schedule. The technique was developed for an automatic telescope scheduling problem, and the paper presents empirical results showing that Just-In-Case scheduling performs extremely well for this problem.

Swanson, Keith↗

Managing Action Duration Uncertainty with Just-In-Case Scheduling

Some applications involve automatic generation and execution of schedules that contain actions with uncertain durations. Such uncertainty can cause schedules to break during execution. This paper presents a technique, called Just-In-Case scheduling or JIC, for building robust schedules that tend not to break. The technique implements the common sense idea of being prepared for likely errors, just in case they should occur. The JIC algorithm analyzes a given schedule, determines where it is likely to break, reinvokes the scheduler to generate a contingent schedule for each highly probable break case, and produces a "multiply contingent" schedule. The technique was developed for a real telescope scheduling problem, and the paper presents empirical results showing that Just-In-Case scheduling performs extremely well for this problem.

Bresina, John↗

On scheduling tasks with a quick recovery from failure

Multiprocessors used in life-critical real-time systems must recover quickly from failure. Part of this recovery consists of switching to a new task schedule which ensures that hard deadlines for critical tasks continue to be met. A dynamic programming algorithm is presented that ensures that backup, or contingency, schedules can be efficiently embedded within the original, 'primary' schedule to ensure that hard deadlines continue to be met in the face of up to a given maximum number of processor failures. Several illustrative examples are included.

Krishna, C. M.↗

Autonomous Rovers for Mars Exploration

Rovers will play a critical role in the exploration of Mars. Near-term mission plans call for long traverses over unknown terrain, robust navigation and instrument placement, and reliable operations for extended periods of time. Longer-term missions may visit multiple science sites in a single day and perform opportunistic science data collection, as well as complex scouting, construction, and maintenance tasks in preparation for an eventual human presence. The Pathfinder mission demonstrated the potential for robotic Mars exploration but at the same time indicated the need for more rover autonomy. The highly ground-intensive control with infrequent communication and high latency limited the effectiveness of the Sojourner rover. When failures occurred, Sojourner often sat idle for extended periods of time, awaiting further commands from earth. In future missions, the tasks will be more complex and extended; hence there will be even more situations where things do not go exactly as planned. Significant advances in rover autonomy are needed to cope with increasing task complexity and greater execution uncertainty. Towards this end, we have designed an on-board executive architecture that incorporates robust operation, resource utilization, and failure recovery. In addition, we have designed ground tools to produce and refine contingent schedules that take advantage of the on-board architecture's flexible execution characteristics. Together, the on-board executive and the ground tools constitute an integrated rover autonomy architecture. This work draws from our experience with the Deep Space One autonomy experiment, with enhancements to ensure robust operation in the face of the unpredictable, complex environment that the rover will encounter on Mars. The rover autonomy architecture is currently being developed and deployed on the Marsokhod rover platform at NASA Ames Research Center. The capabilities of the rover autonomy architecture to support autonomous operations will be demonstrated concretely in upcoming field tests.

Anderson, Corin↗

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service.Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations.The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-to-station handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an in-orbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service. Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations. The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-tostation handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an inorbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗

Initial Performance Evaluation of Flight Path Management Onboard Automation

Significant developments in automation are necessary to achieve safe and efficient operations in advanced aerial mobility related concepts. Urban Air Mobility (UAM) is rapidly growing, emerging field that poses a challenging use case with a tighter scale of operations compared to the traditional commercial transport paradigm. A large part of the challenge is the uncharted territory; as of this paper, no set of operational standards or guidelines for UAM operations have been established and automated en route operations for UAM level 4 (UML-4) have not been studied. Flight Path Management (FPM) automation provides a set of capabilities that are critical toward enabling airborne vehicles to achieve mission success while maintaining operational safety. An initial performance evaluation of FPM automation was conducted using a UAM-adapted version of the Autonomous Operations Planner (AOP), an onboard trajectory management capability developed over years of research targeting commercial transport operations, as its reference implementation. This paper describes the evaluation, including the approach and methodology for simulating FPM automation in UML-4, key results, future work, and conclusions.

flight path management↗

Initial Performance Evaluation of Flight Path Management Onboard Automation

Significant developments in automation are necessary to achieve safe and efficient operations in advanced aerial mobility related concepts. Urban Air Mobility (UAM) is rapidly growing, emerging field that poses a challenging use case with a tighter scale of operations compared to the traditional commercial transport paradigm. A large part of the challenge is the uncharted territory; as of this paper, no set of operational standards or guidelines for UAM operations have been established and automated en route operations for UAM level 4 (UML-4) have not been studied. Flight Path Management (FPM) automation provides a set of capabilities that are critical toward enabling airborne vehicles to achieve mission success while maintaining operational safety. An initial performance evaluation of FPM automation was conducted using a UAM-adapted version of the Autonomous Operations Planner (AOP), an onboard trajectory management capability developed over years of research targeting commercial transport operations, as its reference implementation. This paper describes the evaluation, including the approach and methodology for simulating FPM automation in UML-4, key results, future work, and conclusions.

flight path management↗

Contingency rescheduling of spacecraft operations

Spacecraft activity scheduling was a focus of attention in artificial intelligence recently. Several scheduling systems were devised which more-or-less successfully address various aspects of the activity scheduling problem, though most of these are not yet mature, with the notable expection of NASA's ESP. Few current scheduling systems, however, make any attempt to deal fully with the problem of modifying a schedule in near-real-time in the event of contingencies which may arise during schedule execution. These contingencies can include resources becoming unavailable unpredictably, a change in spacecraft conditions or environment, or the need to perform an activity not scheduled. In these cases it becomes necessary to repair an existing schedule, disrupting ongoing operations as little as possible. Normal scheduling is just a part of that which must be accomplished during contingency rescheduling. A prototype system named MAESTRO was developed for spacecraft activity scheduling. MAESTRO is briefly described with a focus on recent work in the area of real-time contingency handling. Included is a discussion of some of the complexities of the scheduling problem and how they affect contingency rescheduling, such as temporal constraints between activities, activities which may be interrupted and continued in any of several ways, and different ways to choose a resource complement which will allow continuation of an activity. Various heuristics used in MAESTRO for contingency rescheduling is discussed, as are operational concerns such as interaction of the scheduler with spacecraft subsystems controllers.

Britt, Daniel L.↗

Real-time contingency handling in MAESTRO

A scheduling and resource management system named MAESTRO was interfaced with a Space Station Module Power Management and Distribution (SSM/PMAD) breadboard at MSFC. The combined system serves to illustrate the integration of planning, scheduling, and control in a realistic, complex domain. This paper briefly describes the functional elements of the combined system, including normal and contingency operational scenarios, then focusses on the method used by the scheduler to handle real-time contingencies.

Britt, Daniel L.↗

Mars Sample Return (MSR) Sample Receiving Facility (SRF) Assessment Study (MSAS)

NASA, in partnership with the European Space Agency (ESA), is seeking to return Martian geological and atmospheric samples to Earth for scientific study in the early 2030s. Due to the possibility that the samples could contain extraterrestrial life, Mars Sample Return (MSR) is classified as a Category V: Restricted Earth Return mission by the NASA Planetary Protection Office. As a result of this classification, a MSR Sample Receiving Facility (SRF) must not only provide a pristine environment to ensure samples are protected from terrestrial contamination for scientific investigations, it must also provide high-containment (biosafety level 4 [BSL-4]-equivalence) to isolate the samples from Earth’s biosphere until the samples are deemed safe for release and/or sterilized. The nominal utilization period for a SRF is anticipated to be 2-5 years and is intended to enable curation activities, biohazard assessment, select early science activities, and the rapid release of samples to the scientific community. However, to account for possible delays in schedule or the identification of extant life, this anticipated period of time must be flexible to accommodate schedule extensions and contingency plans. Due to requirements for high-level biological containment and cleanliness, a traditional receiving/curation facility cannot be utilized for MSR. Therefore, beginning in 2022, NASA Johnson Space Center is performing a MSR SRF Assessment Study (MSAS) to investigate the most optimal facility modality for a MSR SRF, as well as start to define programmatic early estimate of costs and schedules before the initial design phase begins. NASA is partnering with industry contractors (architectural and engineering firms with BSL-4 and cleanroom technology experience, as well as other contracted infrastructure and construction specialists) along with selected experts from NASA, ESA, existing U.S. BSL-4 facilities, and other U.S. government agencies, to carry out the assessment study. The MSAS should also aid in the future refinement of the science requirements (e.g., contamination control, equipment accommodations) before site-specific design would commence. As part of the MSAS, NASA is planning to assess an array of possibilities for a MSR SRF. One of the main considerations is the facility modality and whether an existing BSL-4 facility can be utilized (for some or all functions); or, if new constructure would be required, would a traditional fixed facility or a modular facility the best choice. MSAS will also investigate the ability of the modalities to accommodate two different facility capability endmembers: 1) a minimal facility focusing on biohazard assessment and curation tasks with a small footprint, and 2) an enhanced facility with additional capabilities to enable expedited processing and the completion of time-sensitive and (some) sterilization-sensitive science. The assessment is intended to generate information that will inform the selection of facility modalities for high-level conceptual design development. While the assessment study will focus on SRF requirements for accommodating curation, science, and sample safety assessment infrastructure, it will also consider an array of other factors, such as ease of access for international users, decommissioning, repurposing, future sale or lease following MSR’s use of the facility, and uncontained preparatory laboratory spaces. Upon completion of the study, the preferred modality and refined requirements would be utilized for site-specific design but will not be finalized until NASA’s completion of the National Environmental Policy Act (NEPA) process.

A.D. Harrington↗

Verifying command sequences for satellite systems

We present a formal basis for the design of a Checker used in validating safe schedules and in selecting error recovery schedules for satellite control systems. This design includes a high-level specification of Checker behavior and properties (called flight rules) of safe schedules. Specifications are written in Timed Linear Logic (TLL). Validation of schedules is performed in terms of real-time telemetry and deduction system proof rules. Telemetry (state information for satellite subsystems) serves as input to the Checker. Detection of violation of a flight rule by the Checker results in the selection of a contingency plan (error recovery schedule). The Checker is illustrated in terms of the TOPEX/Poseidon Oceanographic Satellite System.

Peters, James F., III↗

Integration of On-board EOS Schedule Revision with Space Communication Emulation System

The need for on-board decision-making for planning science observations on Earth Observing Satellites is based on the fact that the desirability of acquiring an image can change dynamically, because of changes in meteorological conditions (e.g. cloud cover), unforeseen events such as fires, floods, or volcanic eruptions, or unexpected changes in satellite or ground station capability. In such cases, satellite resources, such as power and SSR capacity can potentially be better utilized taking another image that is of higher quality. Currently, typical Earth observing satellites cannot communicate directly with each other, and can only communicate with ground stations about 5% to 10% of the time. Because of the limited communication windows, as well as the cost and effort that would need to be expended in revising a mission schedule, a ground-based scheduler would have little or no opportunity to revise the schedule in response to the contingencies that may arise. For this reason, a distributed science planning system combining a ground-based scheduler with on-board schedule revision capabilities is warranted. This paper will describe algorithms for on-board decision-making for science planning and their integration with the advanced satellite control and communications technology developed at the Space Communication Emulation Facility (SCEF) at NASA Glenn Research Center. Our objective of demonstrating how advanced communications and scheduling technology can be combined to improve the scientific utility of images acquired by Earth observing systems will be discussed via a description of a number of realistic flight scenarios.

Khatib, Lina↗

Airline Operations Aid

C Language Integrated Production System (CLIPS), a NASA-developed expert systems program, is used by American Airlines for three purposes: as a rapid prototyping tool; to develop production prototypes; and to develop production application. An example of the latter is CLIPS' use in "Hub S1AAshing," a knowledge based system that recommends contingency plans when severe schedule reductions must be made. Hub S1AAshing has replaced a manual, labor intensive process. It saves time and allows Operations Control Coordinators to handle more difficult situations. Because the system assimilates much of the information necessary to facilitate educated decision making, it minimizes negative impact in situations where it is impossible to operate all flights.

Source record↗

Completable scheduling: An integrated approach to planning and scheduling

The planning problem has traditionally been treated separately from the scheduling problem. However, as more realistic domains are tackled, it becomes evident that the problem of deciding on an ordered set of tasks to achieve a set of goals cannot be treated independently of the problem of actually allocating resources to the tasks. Doing so would result in losing the robustness and flexibility needed to deal with imperfectly modeled domains. Completable scheduling is an approach which integrates the two problems by allowing an a priori planning module to defer particular planning decisions, and consequently the associated scheduling decisions, until execution time. This allows a completable scheduling system to maximize plan flexibility by allowing runtime information to be taken into consideration when making planning and scheduling decision. Furthermore, through the criteria of achievability placed on deferred decision, a completable scheduling system is able to retain much of the goal-directedness and guarantees of achievement afforded by a priori planning. The completable scheduling approach is further enhanced by the use of contingent explanation-based learning, which enables a completable scheduling system to learn general completable plans from example and improve its performance through experience. Initial experimental results show that completable scheduling outperforms classical scheduling as well as pure reactive scheduling in a simple scheduling domain.

Gervasio, Melinda T.↗

Re-scheduling as a tool for the power management on board a spacecraft

The scheduling of events on board a spacecraft is based on forecast energy levels. The real time values of energy may not coincide with the forecast values; consequently, a dynamic revising to the allocation of power is needed. The re-scheduling is also needed for other reasons on board a spacecraft like the addition of new event which must be scheduled, or a failure of an event due to many different contingencies. This need of rescheduling is very important to the survivability of the spacecraft. In this presentation, a re-scheduling tool will be presented as a part of an overall scheme for the power management on board a spacecraft from the allocation of energy point of view. The overall scheme is based on the optimal use of energy available on board a spacecraft using expert systems combined with linear optimization techniques. The system will be able to schedule maximum number of events utilizing most energy available. The outcome is more events scheduled to share the operation cost of that spacecraft. The system will also be able to re-schedule in case of a contingency with minimal time and minimal disturbance of the original schedule. The end product is a fully integrated planning system capable of producing the right decisions in short time with less human error. The overall system will be presented with the re-scheduling algorithm discussed in detail, then the tests and results will be presented for validations.

Albasheer, Omar↗

The human role in space

Requirements for the future human role in space are considered. Human tasks were divided into normal scheduled activities, unscheduled activities, and contingency activities. A strawman mission model was developed. Projections were made of crew size, mission duration, pressurized spacecraft volume, and power requirements. Trends of human roles were likewise projected.

Hall, S. B.↗

The MICRO-BOSS scheduling system: Current status and future efforts

In this paper, a micro-opportunistic approach to factory scheduling was described that closely monitors the evolution of bottlenecks during the construction of the schedule and continuously redirects search towards the bottleneck that appears to be most critical. This approach differs from earlier opportunistic approaches, as it does not require scheduling large resource subproblems or large job subproblems before revising the current scheduling strategy. This micro-opportunistic approach was implemented in the context of the MICRO-BOSS factory scheduling system. A study comparing MICRO-BOSS against a macro-opportunistic scheduler suggests that the additional flexibility of the micro-opportunistic approach to scheduling generally yields important reductions in both tardiness and inventory. Current research efforts include: adaptation of MICRO-BOSS to deal with sequence-dependent setups and development of micro-opportunistic reactive scheduling techniques that will enable the system to patch the schedule in the presence of contingencies such as machine breakdowns, raw materials arriving late, job cancellations, etc.

Sadeh, Norman M.↗