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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Validation of Self-Scheduling Countermeasures in NASA's HERA Campaign 6

Enhancing crew capabilities for planning and scheduling activities is critical for periods of increased crew autonomy in future long-duration missions where communication delays preclude real-time ground support from Earth. Our study focuses on how to empower astronauts to manage their timelines independently from the experts in the mission control center (MCC). Our objective was to evaluate the impact of scheduling countermeasures on crew scheduling performance, workload, and usability in an analog mission environment. The study involved 16 crew members across four missions in the Limited Autonomy phase of Human Exploration Research Analog (HERA) Campaign 6. Crew members used Playbook to schedule one operational day for the entire crew. Half the participants accessed scheduling aids, and we compared their performance to a control group with no aids. Performance, workload, and usability were assessed using time on task, violation counts, NASA Task Load Index (NASA-TLX), and System Usability Scale (SUS). Participants using scheduling aids completed sessions 20% faster and committed 33% fewer violations. While these differences were not statistically significant due to the study’s operational limitations, trends indicate that scheduling aids may reduce errors and improve efficiency. These results can inform the design of scheduling tools to enhance astronauts’ autonomy in long-duration space missions, contributing to improved crew performance and reduced reliance on ground support.

space robotics↗
NASA NTRSDate not supplied

Crew Scheduling Performance

As NASA considers long-duration exploration missions (LDEMs), it is envisioned that crew will behave more autonomously as compared to low-Earth orbit missions. The necessary shift of Ops Planners’ complete management of scheduling and planning to provide flexibility for crew to manage their own schedule in real-time requires significant research and investigation in evaluation of concepts of operations, of software tools to support tasks, and of crew performance to complete scheduling tasks. Our research objective is to characterize the human performance envelope for the task of planning and scheduling (crew self-scheduling), develop countermeasures to mitigate adverse performance effects due to plan complexity, and inform performance standards and guidelines based on research results. In our efforts to understand and characterize scheduling performance of crew members, we have conducted subject matter expert interviews to obtain feedback on scheduling plan complexity drivers and plan goodness. Following the pilot study conducted last year, we designed a study and have begun remote human subject testing to evaluate non-expert human performance, workload, and situational awareness for the task of planning and scheduling. This study investigates the effects of the number and type of constraints on human performance, and the differences in scheduling metrics between scheduling and rescheduling tasks. Subject testing is currently in progress, and we aim to collect data from approximately thirty subjects. We will summarize the methods used and impacts of the number and type of scheduling constraints. We will also present lessons learned as well as relevant results of the study.

crew scheduling↗
NASA NTRS2018

Evaluation of Crew-Centric Onboard Mission Operations Planning and Execution Tool: Year 2

Currently, mission planning for the International Space Station (ISS) is largely affected by ground operators in mission control. The task of creating a week-long mission plan for ISS crew takes dozens of people multiple days to complete, and is often created far in advance of its execution. As such, re-planning or adapting to changing real-time constraints or emergent issues is similarly taxing. As we design for future mission operations concepts to other planets or areas with limited connectivity to Earth, more of these ground-based tasks will need to be handled autonomously by the crew onboard.There is a need for a highly usable (including low training time) tool that enables efficient self-scheduling and execution within a single package. The ISS Program has identified Playbook as a potential option. It already has high crew acceptance as a plan viewer from previous analogs and can now support a crew self-scheduling assessment on ISS or on another mission. The goals of this work, a collaboration between the Human Research Program and the ISS Program, are to inform the design of systems for more autonomous crew operations and provide a platform for research on crew autonomy for future deep space missions. Our second year of the research effort have included new insights on the crew self-scheduling sessions performed by the crew through use on the HERA (Human Exploration Research Analog) and NEEMO (NASA Extreme Environment Mission Operations) analogs. Use on the NEEMO analog involved two self-scheduling strategies where the crew planned and executed two days of EVAs (Extra-Vehicular Activities). On HERA year two represented the first HERA campaign where we were able to perform research tasks. This involved selected flexible activities that the crew could schedule, mock timelines where the crew completed more complex planning exercises, usability evaluation of the crew self-scheduling features, and more insights into the limit of plan complexity that the crew could effectively self-schedule. In parallel we have added in new features and functionality in the Playbook tool based off of our insights from crew self-scheduling in the NASA analogs. In particular this year we have added in the ability for the crew to add, edit, and remove their own activities in the Playbook tool, expanding the type of planning and re-planning possible in the tool and opening up the ability for more free form plan creation. The ability to group and manipulate groups of activities from the plan task list was also added, allowing crew members to add predefined sets of activities onto their mission timeline. In addition we also added a way for crew members to roll back changes in their plan, in order to allow an undo like capability. These features expand and complement the initial self-scheduling features added in year one with the goal of making crew autonomous planning more efficient. As part of this work we have also finished developing the first version of our Playbook Data Analysis Tool, a research tool built to interpret and analyze the unobtrusively collected data obtained during the NASA analog missions through Playbook. This data which includes user click interaction as well as plan change information, through the Playbook Data Analysis Tool, allows us to playback this information as if a video camera was mounted over the crewmember's tablet. While the primary purpose of this tool is to allow usability analysis of crew self-scheduling sessions used on the NASA analog, since the data collected is structured, the tool can automatically derive metrics that would be traditionally tedious to achieve without manual analysis of video playback. We will demonstrate and discuss the ability for future derived metrics to be added to the tool. In addition to the current data and results gathered in year two we will also discuss the preparation and goals of our International Space Station (ISS) onboard technology demonstration with Playbook. This technology demonstration will be preformed as part of the CAST payload starting in late 2016.

onboard planning↗
NASA NTRSDate not supplied

The Role of Trust and Usability in Enabling Spaceflight Crew Autonomy

Future long duration exploration missions will require an increased use of onboard automated systems as spaceflight crews venture further than before and have longer communications delays with ground support. The design of these systems must support appropriate crew trust and have sufficient usability to enable spaceflight crew autonomy or risk being misused while crews wait to communicate with ground support. We evaluated trust & usability in our self-scheduling tool, Playbook, for crew mission timelines. Data was collected in a controlled lab experiment with 31 participants. Participants in the study conducted two tasks: scheduling, where participants were responsible for scheduling a majority of a day's operational tasks, and rescheduling, where participants were provided a schedule and asked to reschedule higher priority activities. We found a significant correlation between system trust and usability, irrespective of self-scheduling tasks. We conclude that system usability may play a bigger role in how trust is learned while conducting novel crew autonomy tasks such as self-scheduling. Future research should investigate the role of usability to encourage appropriate trust in onboard automated crew systems and enable crew autonomy.

crew autonomy↗
NASA NTRSDate not supplied

AMO-EXPRESS-2.5: Crew Autonomy Onboard the International Space Station

NASA is committed to landing American astronauts, including the first woman and the next man, on the Moon by 2024. Currently, the crew cannot take on all functions performed by the ground today, so the future crews will need more automation to reduce the crew workload for future missions. Of significant importance for these missions is the balance between crew autonomy and vehicle automation. The Advanced Exploration Systems (AES) Autonomous Systems and Operations (ASO) Project has been investigating the ability to evaluate crew self-scheduling and activity monitoring for future space missions. The ASO project designed the Autonomous Mission and Operations- EXpedite the PRocessing of Experiments to Space Station Rack-2.5 (AMO-EXPRESS-2.5) payload to evaluate crew self-scheduling and activity monitoring. The AMO-EXPRESS-2.5 builds on the previous AMO-EXPRESS and AMO-EXPRESS-2.0 demonstrations on ISS. The AMO-EXPRESS-2.5 demonstration goals are to prove crew self-scheduling by planning through diagnosing systems expertise, failure detection, procedure recommendation and situational awareness. This paper will describe the development, test and execution results of the AMO-EXPRESS-2.5 demonstration, and will also outline the future planned development and operational efforts to enable autonomy for future deep space manned missions.

Brooke C. Allen↗
NASA NTRS2021

Human Performance of Novice Schedulers for Complex Spaceflight Operations Timelines

Objective: Investigate the effects on human performance as a function of scheduling task complexity for novice schedulers creating spaceflight timelines. Background: Future astronauts will be expected to self-schedule, yet will not be experts in creating timelines that meet complex constraints inherent to spaceflight operations. Method: Conducted a within-subject experiment to measure scheduling efficiency, effectiveness, workload and situation awareness while varying scheduling task complexity factors, namely number of constraints and types of constraints. Results: Fifteen participants completed various scheduling problems. Performance differences were identified between the independent variables. There was a main effect due to the number of constraints and type of constraint for efficiency, effectiveness, and workload. Significant interactions were observed in situation awareness and workload for certain types of constraints. Results also suggest that a lower number of constraints may be manageable by novice schedulers when compared to scheduling activities without constraints. Conclusion: Results suggest that novice schedulers performance decreases with high number of constraints and future scheduling aids may have to be targeted to type of constraint. Application: Knowledge on the effect of scheduling task complexity will help design scheduling systems that will enable self-scheduling for future astronauts. It will also inform other domains that conduct complex scheduling, such as nursing and manufacturing.

scheduling↗
NASA NTRS2019

Crew Autonomy Scheduling: Scheduling Performance Pilot Study

The purpose of this pilot study is to quantify crew performance in self-scheduling through Playbook, a mobile-based scheduling and planning tool. By investigating human performance within the task of self-scheduling, we can further develop countermeasures that can mitigate deficient scheduling performance, and evaluate changes as a result of these countermeasures. Moreover, this research can advise the development of standards and guidelines for autonomous crews in future missions. In human spaceflight today, the task of planning crew member’s schedules falls to Ops Planners. It takes many weeks to plan due to the complex impact each day-to-day activity can have on other activities, crew members, and resources. These impacts are measured as constraints, and these constraints can result in temporal, ordering, or resource requirements. As future spaceflight missions span longer distances and the latency of communication between the crew and Mission Control Center (MCC) increases, the need for crew members to work independently from MCC will also increase. This results in a need for crew members to be able to autonomously plan and adjust their own schedules.

planning↗
NASA NTRSDate not supplied

Promoting Astronaut Autonomy in Human Spaceflight Missions

Mission operations will have to adapt for long duration, long distance human spaceflight missions. This change is driven mainly by the significantly different communication availability between Earth and space. As astronauts travel farther from Earth, the one-way communication latency increases; the amount of bandwidth will be limited; and there will be period of long and/or no communication. Currently, ground flight controllers collaborate and cooperate with astronauts in space to accomplish essential operational functions. Astronaut autonomy, i.e., the crew’s ability to work more independently from mission control, will be a key enabler in future exploration missions. Over the last several years, the NASA Ames Human-Computer Interaction (HCI) Group has investigated various ways to promote and support astronaut autonomy in human spaceflight missions. Software prototypes are researched, designed, implemented, and assessed for their ability to enable astronaut autonomy. From integrated Internet of Thing for Space, advanced procedures interfaces, comm-delayed chats, and self-scheduling tools, the HCI Group has explored different aspects of astronaut autonomy. Specifically, the self-scheduling tool Playbook has been evaluated in analog extreme environments and onboard the International Space Station, successfully paving the way for future autonomous astronauts.

crew autonomy↗
NASA NTRS1987

Parallel software support for computational structural mechanics

The application of the parallel programming methodology known as the Force was conducted. Two application issues were addressed. The first involves the efficiency of the implementation and its completeness in terms of satisfying the needs of other researchers implementing parallel algorithms. Support for, and interaction with, other Computational Structural Mechanics (CSM) researchers using the Force was the main issue, but some independent investigation of the Barrier construct, which is extremely important to overall performance, was also undertaken. Another efficiency issue which was addressed was that of relaxing the strong synchronization condition imposed on the self-scheduled parallel DO loop. The Force was extended by the addition of logical conditions to the cases of a parallel case construct and by the inclusion of a self-scheduled version of this construct. The second issue involved applying the Force to the parallelization of finite element codes such as those found in the NICE/SPAR testbed system. One of the more difficult problems encountered is the determination of what information in COMMON blocks is actually used outside of a subroutine and when a subroutine uses a COMMON block merely as scratch storage for internal temporary results.

Jordan, Harry F.↗
NASA NTRS2019

Lessons Learned from International Space Station Crew Autonomous Scheduling Test

In 2017, our team investigated and evaluated the novel concept of operations of astronaut self-scheduling (rescheduling their own timeline without creating violations) onboard International Space Station (ISS). Five test sessions were completed for this technology demonstration called Crew Autonomous Scheduling Test (CAST). For the first time in a spaceflight operational environment, an ISS crewmember planned, rescheduled, and executed their activities in real-time on a mobile device while abiding by flight and scheduling constraints. This paper discusses the lessons learned from deployment to execution.

planning and scheduling↗
DOE OSTI2015

Systematic Approach to Better Understanding Integration Costs: Preprint

When someone mentions integration costs, thoughts of the costs of integrating renewable generation into an existing system come to mind. We think about how variability and uncertainty can increase power system cycling costs as increasing amounts of wind or solar generation are incorporated into the generation mix. However, seldom do we think about what happens to system costs when new baseload generation is added to an existing system or when generation self-schedules. What happens when a highly flexible combined-cycle plant is added? Do system costs go up, or do they go down? Are other, non-cycling, maintenance costs impacted? In this paper we investigate six technologies and operating practices--including VG, baseload generation, generation mix, gas prices, self-scheduling, and fast-start generation--and how changes in these areas can impact a system's operating costs. This paper provides a working definition of integration costs and four components of variable costs. It describes the study approach and how a production cost modeling-based method was used to determine the cost effects, and, as a part of the study approach section, it describes the test system and data used for the comparisons. Finally, it presents the research findings, and, in closing, suggests three areas for future work.

17 WIND ENERGY↗
NASA NTRSDate not supplied

What is the Role of Usability and Trust in Autonomy?

Usability encompasses learnability, efficiency, memorability, effectiveness, and satisfaction. NASA’s standards for usability acceptance criteria focus on interfaces that help operators achieve their tasks efficiently, effectively, and with satisfaction. However, discussions on usability, especially regarding future highly automated and autonomous systems, rarely include trust. As NASA plans for long-duration exploration missions, it envisions astronauts operating more independently from Mission Control on Earth. This independence will drive the development of these highly automated and autonomous systems that astronauts will use daily. To prepare for this future, our team has developed a scheduling and execution software tool that facilitates self-scheduling, allowing astronauts to independently manage their own schedule without Mission Control’s involvement. Over many years, we have developed, matured, and evaluated our software tool in extreme environments, prioritizing user-centered design and high usability. These evaluations have included multiple campaigns in NASA analogs, including NEEMO, BASALT, and HERA, as well as technology demonstrations onboard the International Space Station. Our recent research on software interfaces for future astronaut autonomy revealed a strong correlation between usability and trust measures. In a controlled lab experiment, we asked novice users to perform a complex scheduling task, during which the software immediately validated the schedule’s constraints and checked for violations. We collected usability (User Experience Questionnaire, UEQ) and trust (Trust in Automated Systems scale, TAS) measures; significant, strong, and moderate correlations emerged between several of the UEQ metrics and TAS. These results support the argument for investing in usability early to enable and sustain trust in highly automated and autonomous systems.

usability↗
NASA NTRS2019

Designing for Astronaut-Centric Planning and Scheduling Aids

We have investigated and evaluated a novel concept of operations for human spaceflight: allowing astronauts to manage and schedule their own timeline. In order to evaluate this self-scheduling concept of operations, we have designed, implemented, and field-tested astronaut-centric planning and scheduling aid. Our mobile-based software aid, Playbook, has been used in a variety of Earth analogs as well as onboard the International Space Station. We will demonstrate the unique Playbook features that we have developed based on research findings during field testing that facilitate planning and scheduling in extreme environments.

planning and scheduling↗
NASA NTRS2016

Designing and Building a Crew-Centric Mobile Scheduling and Planning Tool for Exploring Crew Autonomy Concepts Onboard the International Space Station

During 2015, as part of our ongoing research on crew autonomy, our team adapted and extended the plan viewing tool, Playbook, into a mobile tool for exploring crew autonomy onboard the International Space Station (ISS). This work enables crewmembers to intuitively re-plan and schedule on their own with limited input from mission control. Designed for ease of use, re-planning is composed of simple drag and drop interactions and learning the tool requires little to no training. Playbook was successfully uplinked onto ISS as a technology demonstration and completed its onboard ground and crew checkouts in August and September 2015, respectively. We will discuss and describe how Playbook was able to integrate with actual ISS operational plans and operational procedures without disrupting real mission operations. This work will also outline the design and technical constraints required to build a collaborative tool that allows multiple users to simultaneously self-schedule and synchronize during communication dropouts in the restricted networking and computing environment onboard ISS. Finally, we will explore the aspects of crew autonomy that this technology enables as well as discuss our next steps and possible extensions.

ISS↗
DOE OSTI2018

A comparison of price-taker and production cost models for determining system value, revenue, and scheduling of concentrating solar power plants

Concentrating solar power (CSP) plants convert solar energy into electricity and can be coupled with low-cost thermal energy storage to provide a dispatchable renewable resource. Maximizing the economic benefits of CSP systems requires deliberate selection of the timing of thermal energy dispatch to coincide with high-price or high-value periods. The performance and value of CSP systems have traditionally been evaluated using two distinct approaches: (1) production cost models (PCMs), which optimize commitment and dispatch schedules for an entire fleet of generators to minimize the cost of satisfying electricity demand; and (2) price-taker (PT) models, which optimize dispatch of the CSP system against historical or forecasted electricity prices to maximize revenue available to the CSP operator. The PCM approach is, in principle, superior because of the ability to capture system-level details such as transmission pathways and associated constraints, and feedback between CSP dispatch schedules and operation of other generators. The computationally simpler PT technique cannot explicitly address these concerns, but the comparative reduction in solution time is attractive for sensitivity analysis and rigorous optimization of CSP plant design and operation. In this work, we directly compare the dispatch profiles, net revenue, and operational value of CSP systems using implementations of each technique to assess how closely the solutions from the PT technique can approach those from the PCM for a specific market scenario. The PT implementation produced characteristically similar dispatch profiles as the PCM for both a large solar-multiple configuration and a small solar-multiple peaking configuration, while requiring only 1% of the computational time of the PCM. In addition, dispatching the CSP plant according to the self-scheduled PT solution - as opposed to the system-optimized PCM solution - only reduced the total CSP operational value by 1-5%, with consistent trends across CSP configurations and locations. This analysis validates the ability of the PT approach to substantially capture the economic benefits of CSP systems when pricing profiles are available, and indicates that the PT approach is suitable for initial design and optimization of CSP technologies.

14 SOLAR ENERGY↗
DOE OSTI2021

Plentiful electricity turns wholesale prices negative

In 2020, average wholesale electricity prices in the United States fell to $21/MWh, their lowest level since the beginning of the 21st century. Low natural gas prices and the proliferation of low marginal cost resources like wind and solar had already established a trend toward lower wholesale prices, and this trend was exacerbated by declining electricity demand due to the Covid-19 pandemic in 2020. Negative real-time hourly wholesale prices occurred in about 4% of all hours and wholesale market nodes across the United States, but these were not distributed evenly. Regional clusters emerged, for example, in the Permian Basin in western Texas, and in Kansas and western Oklahoma in the Southwest Power Pool (SPP), negative prices accounted for more than 25% of all hours. Negative electricity prices result either from local congestion of the transmission system leading supply to exceed demand locally or due to system-wide oversupply. Looking at the latter condition in SPP, we find that all major generator types contribute to this excess supply, because of limited ramping flexibility or self-scheduled out-of-market unit commitments. Additional monetary production incentives such as renewable energy credits or tax credits also enable negative bids; indeed, negative prices predominantly occur when demand levels are low and wind production levels are high. Frequent negative prices can inform the value of additional renewable energy investments at specific locations, the need for transmission and storage development, and opportunities load growth or adaptation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

An accurate bilinear cavern model for compressed air energy storage

Compressed air energy storage is suitable for large-scale electrical energy storage, which is important for integrating renewable energy sources into electric power systems. A typical compressed air energy storage plant consists of compressors, expanders, caverns, and a motor/generator set. Current cavern models used for compressed air energy storage are either accurate but highly nonlinear or linear but inaccurate. The application of highly nonlinear cavern models in power system optimization problems renders them computationally challenging to solve. In this regard, an accurate bilinear cavern model for compressed air energy storage is proposed. The charging and discharging processes in a cavern are divided into several real/virtual states. The first law of thermodynamics and ideal gas law are then utilized to derive a cavern model, i.e., a model for the variation of temperature and pressure in these processes. Thereafter, the heat transfer between the air in the cavern and the cavern wall is considered and integrated into the cavern model. By subsequently eliminating several negligible terms, the cavern model reduces to a bilinear model. The accuracy of the bilinear cavern model is verified via comparison with both an accurate nonlinear model and two sets of field-measured data. The bilinear cavern model can be easily linearized and is then suitable for integration into optimization problems considering compressed air energy storage. This is verified via comparatively solving a self-scheduling problem of compressed air energy storage using different cavern models.

25 ENERGY STORAGE↗
DOE OSTI2017

Evolution of a minimal parallel programming model

Here, we take a historical approach to our presentation of self-scheduled task parallelism, a programming model with its origins in early irregular and nondeterministic computations encountered in automated theorem proving and logic programming. We show how an extremely simple task model has evolved into a system, asynchronous dynamic load balancing (ADLB), and a scalable implementation capable of supporting sophisticated applications on today’s (and tomorrow’s) largest supercomputers; and we illustrate the use of ADLB with a Green’s function Monte Carlo application, a modern, mature nuclear physics code in production use. Our lesson is that by surrendering a certain amount of generality and thus applicability, a minimal programming model (in terms of its basic concepts and the size of its application programmer interface) can achieve extreme scalability without introducing complexity.

97 MATHEMATICS AND COMPUTING↗