Results for “automated scheduling” — Page 10

NASA NTRS2006

Using Apex To Construct CPM-GOMS Models

process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.

John, Bonnie↗

NASA NTRS2013

Automating Stowage Operations for the International Space Station

A challenge for any proposed mission is to demonstrate convincingly that the proposed systems will in fact deliver the science promised. Funding agencies and mission design personnel are becoming ever more skeptical of the abstractions that form the basis of the current state of the practice with respect to approximating science return. To address this, we have been using automated planning and scheduling technology to provide actual coverage campaigns that provide better predictive performance with respect to science return for a given mission design and set of mission objectives given implementation uncertainties. Specifically, we have applied an adaptation of ASPEN and SPICE to the Eagle-Eye domain that demonstrates the performance of the mission design with respect to coverage of science imaging targets that address climate change and disaster response. Eagle-Eye is an Earth-imaging telescope that has been proposed to fly aboard the International Space Station (ISS).

box packing↗

NASA NTRS2013

Mission Design Evaluation Using Automated Planning for High Resolution Imaging of Dynamic Surface Processes from the ISS

A challenge for any proposed mission is to demonstrate convincingly that the proposed systems will in fact deliver the science promised. Funding agencies and mission design personnel are becoming ever more skeptical of the abstractions that form the basis of the current state of the practice with respect to approximating science return. To address this, we have been using automated planning and scheduling technology to provide actual coverage campaigns that provide better predictive performance with respect to science return for a given mission design and set of mission objectives given implementation uncertainties. Specifically, we have applied an adaptation of ASPEN and SPICE to the Eagle-Eye domain that demonstrates the performance of the mission design with respect to coverage of science imaging targets that address climate change and disaster response. Eagle-Eye is an Earth-imaging telescope that has been proposed to fly aboard the International Space Station (ISS).

International Space Station↗

NASA NTRS2016

Minimizing Human Risk: Human Performance Models in the Space Human Factors and Habitability and Behavioral Health and Performance Elements

Human space exploration has never been more exciting than it is today. Human presence to outer worlds is becoming a reality as humans are leveraging much of our prior knowledge to the new mission of going to Mars. Exploring the solar system at greater distances from Earth than ever before will possess some unique challenges, which can be overcome thanks to the advances in modeling and simulation technologies. The National Aeronautics and Space Administration (NASA) is at the forefront of exploring our solar system. NASA's Human Research Program (HRP) focuses on discovering the best methods and technologies that support safe and productive human space travel in the extreme and harsh space environment. HRP uses various methods and approaches to answer questions about the impact of long duration missions on the human in space including: gravity's impact on the human body, isolation and confinement on the human, hostile environments impact on the human, space radiation, and how the distance is likely to impact the human. Predictive models are included in the HRP research portfolio as these models provide valuable insights into human-system operations. This paper will provide an overview of NASA's HRP and will present a number of projects that have used modeling and simulation to provide insights into human-system issues (e.g. automation, habitat design, schedules) in anticipation of space exploration.

space human factors and habitability↗

NASA NTRS2017

Minimizing Human Risk: Human Performance Models in the Human Factors and Behavioral Performance Element

Human space exploration has never been more exciting than it is today. Human presence to outer worlds is becoming a reality as humans are leveraging much of our prior knowledge to the new mission of going to Mars. Exploring the solar system at greater distances from Earth than ever before will possess some unique challenges, which can be overcome thanks to the advances in modeling and simulation technologies. The National Aeronautics and Space Administration (NASA) is at the forefront of exploring our solar system. NASA's Human Research Program (HRP) focuses on discovering the best methods and technologies that support safe and productive human space travel in the extreme and harsh space environment. HRP uses various methods and approaches to answer questions about the impact of long duration missions on the human in space including: gravitys impact on the human body, isolation and confinement on the human, hostile environments impact on the human, space radiation, and how the distance is likely to impact the human. Predictive models are included in the HRP research portfolio as these models provide valuable insights into human-system operations. This paper will provide an overview of NASA's HRP and will present a number of projects that have used modeling and simulation to provide insights into human-system issues (e.g. automation, habitat design, schedules) in anticipation of space exploration.

human systems integrations↗

NASA NTRS2016

Transitioning Autonomous Systems Technology Research to a Flight Software Environment

NASA has developed methods and algorithms for autonomous spacecraft operations,including automated planning and scheduling, fault diagnostics and impact determination,procedure management and display. Making the transition from technology research tooperational flight software requires overcoming significant technical, programmatic andcultural challenges. Technology research is aimed at developing methods that performspecific functions correctly, but the resulting software may not be designed for flightprocessors with limited CPU, memory and network resources, and may not be easilyintegrated into spacecraft flight software. Our objective in the Autonomous Systems andOperations Project is to make significant strides toward the transformation from technologyto operational use. Our focus was twofold: maturing research grade autonomy software intoa flight software environment using broadly accepted languages and tools; and integratingautonomy applications with each other and with representative systems and their data andcommand interfaces. For a target flight software environment, we chose Core FlightSoftware, developed by Goddard Space Flight Center as a common operating systemindependent framework. Our hardware integration environment was provided by theIntegrated Power and Avionics Systems (iPAS) Lab at Johnson Space Center, in whichvarious subsystem development has been conducted to address engineering challenges forthe vehicles and systems required for long-duration missions into the solar system. The iPASand its network of connected facilities provides realistic subsystem hardware or simulationsof spacecraft power, life support, guidance, navigation and control, and command and datahandling subsystems. Interfaces between autonomy applications and the subsystems beingassessed and controlled were developed, assessed and refined. The hardware and softwareenvironment using CFS and the iPAS facility has proven to be a highly flexible and realisticenvironment in which to rapidly integrate applications in an iterative, low cost setting. Usingthe integration environment we have developed, we will turn our focus to performance andsizing analysis to determine the computational requirements for full-scale deployment ofautonomy technology. Scalability of reasoners and the spacecraft models upon which theyoperate, and robustness across the full range of spacecraft conditions and environments willbe explored and improved. We are making significant contributions to the future programsthat will build the spacecraft that will take humans beyond the Earth-Moon system, in whichprogram Systems Engineers will be able to accurately and confidently design in accurate,robust and mature autonomous operations systems.

Flight Software↗

NASA NTRS2019

Cognitive Communications for NASA Space Systems

The growing complexity of spacecraft constellations, communication relay offerings, and mission architectures drives the need for the development of autonomous communication systems. NASA has traditionally launched single spacecraft missions that are served by the Space Communication and Navigation (SCaN) program. Operations on SCaN networks are typically scheduled weeks in advance, and often each asset serves a single user spacecraft at a time. Recent movement towards swarm missions could make the current approach unsustainable. Additionally, the integration of commercial communication service providers will substantially increase the data transfer options available to new missions. NASA science missions have found benefit in launching swarms of spacecraft, allowing coordinated simultaneous observations from different perspectives. Inter-spacecraft communication (mesh networking) is an enabler for this architecture, as are CubeSats that allow cost-effective provisioning of distributed mission assets. As more complex swarm missions launch, one challenge is coordinating communication within the swarm and choosing the appropriate mechanism for telemetry, tracking, control, and data services to and from Earth. Cognitive communications research conducted by SCaN aims to mitigate the increasing communication complexity for mission users by increasing the autonomy of links, networks, and service scheduling. By considering automation techniques including recent advances in artificial intelligence and machine learning, cognitive algorithms and related approaches enable increased mission science return, improved resource utilization for service provider networks, and resiliency in unpredictable or unplanned environments. The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, non-deterministic methods to improve the autonomy of space communication. The project emphasizes development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset management. This paper discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chelmins, David↗

NASA NTRS2006

Conflict-Aware Scheduling Algorithm

conflict-aware scheduling algorithm is being developed to help automate the allocation of NASA s Deep Space Network (DSN) antennas and equipment that are used to communicate with interplanetary scientific spacecraft. The current approach for scheduling DSN ground resources seeks to provide an equitable distribution of tracking services among the multiple scientific missions and is very labor intensive. Due to the large (and increasing) number of mission requests for DSN services, combined with technical and geometric constraints, the DSN is highly oversubscribed. To help automate the process, and reduce the DSN and spaceflight project labor effort required for initiating, maintaining, and negotiating schedules, a new scheduling algorithm is being developed. The scheduling algorithm generates a "conflict-aware" schedule, where all requests are scheduled based on a dynamic priority scheme. The conflict-aware scheduling algorithm allocates all requests for DSN tracking services while identifying and maintaining the conflicts to facilitate collaboration and negotiation between spaceflight missions. These contrast with traditional "conflict-free" scheduling algorithms that assign tracks that are not in conflict and mark the remainder as unscheduled. In the case where full schedule automation is desired (based on mission/event priorities, fairness, allocation rules, geometric constraints, and ground system capabilities/ constraints), a conflict-free schedule can easily be created from the conflict-aware schedule by removing lower priority items that are in conflict.

Wang, Yeou-Fang↗

NASA NTRSDate not supplied

Crew Autonomy through Self-Scheduling: Scheduling Performance Pilot Study

Within the domain of human spaceflight, crew scheduling for International Space Station (ISS) remains a human-driven planning task. Large teams of flight controllers (called Ops Planners) spend weeks creating violation-free schedules for all crewmembers. As NASA considers long-duration exploration missions, the necessary shift of scheduling and planning management from Ops Planners to crew members requires significant research and investigation of crew performance to complete these scheduling tasks. This pilot study was conducted to evaluate non-expert human performance for the task of planning and scheduling, focusing on scheduling problems that increased in complexity based on the number of activities to be scheduled and the number of planning constraints. Nine non-expert planners were recruited to complete scheduling tasks using Playbook, a scheduling software. The results of this pilot study show that scheduling performance decreased as scheduling workload (i.e. number of activities and percent of activities with planning constraints) increased. This paper provides evidence towards developing a model for scheduling task difficulty and identifies potential implications for future automated aids for flight crew scheduling.

planning↗

NASA NTRSDate not supplied

Crew Autonomy Through Self-Scheduling: Scheduling Performance Pilot Study

Within the domain of human spaceflight, crew scheduling for International Space Station (ISS) remains a human-driven planning task. Large teams of flight controllers (called Ops Planners) spend weeks creating violation-free schedules for all crewmembers. As NASA considers long-duration exploration missions, the necessary shift of scheduling and planning management from Ops Planners to crew members requires significant research and investigation of crew performance to complete these scheduling tasks. This pilot study was conducted to evaluate non-expert human performance for the task of planning and scheduling, focusing on scheduling problems that increased in complexity based on the number of activities to be scheduled and the number of planning constraints. Nine non-expert planners were recruited to complete scheduling tasks using Playbook, a scheduling software. The results of this pilot study show that scheduling performance decreased as scheduling workload (i.e. number of activities and percent of activities with planning constraints) increased. This paper provides evidence towards developing a model for scheduling task difficulty and identifies potential implications for future automated aids for flight crew scheduling.

planning↗

NASA NTRS1998

A Photo Album of Earth Scheduling Landsat 7 Mission Daily Activities

Landsat7 is a member of a new generation of Earth observation satellites. Landsat7 will carry on the mission of the aging Landsat 5 spacecraft by acquiring high resolution, multi-spectral images of the Earth surface for strategic, environmental, commercial, agricultural and civil analysis and research. One of the primary mission goals of Landsat7 is to accumulate and seasonally refresh an archive of global images with full coverage of Earth's landmass, less the central portion of Antarctica. This archive will enable further research into seasonal, annual and long-range trending analysis in such diverse research areas as crop yields, deforestation, population growth, and pollution control, to name just a few. A secondary goal of Landsat7 is to fulfill imaging requests from our international partners in the mission. Landsat7 will transmit raw image data from the spacecraft to 25 ground stations in 20 subscribing countries. Whereas earlier Landsat missions were scheduled manually (as are the majority of current low-orbit satellite missions), the task of manually planning and scheduling Landsat7 mission activities would be overwhelmingly complex when considering the large volume of image requests, the limited resources available, spacecraft instrument limitations, and the limited ground image processing capacity, not to mention avoidance of foul weather systems. The Landsat7 Mission Operation Center (MOC) includes an image scheduler subsystem that is designed to automate the majority of mission planning and scheduling, including selection of the images to be acquired, managing the recording and playback of the images by the spacecraft, scheduling ground station contacts for downlink of images, and generating the spacecraft commands for controlling the imager, recorder, transmitters and antennas. The image scheduler subsystem autonomously generates 90% of the spacecraft commanding with minimal manual intervention. The image scheduler produces a conflict-free schedule for acquiring images of the "best" 250 scenes daily for refreshing the global archive. It then equitably distributes the remaining resources for acquiring up to 430 scenes to satisfy requests by international subscribers. The image scheduler selects candidate scenes based on priority and age of the requests, and predicted cloud cover and sun angle at each scene. It also selects these scenes to avoid instrument constraint violations and maximizes efficiency of resource usage by encouraging acquisition of scenes in clusters. Of particular interest to the mission planners, it produces the resulting schedule in a reasonable time, typically within 15 minutes.

Potter, William↗

NASA NTRS2011

Design and Evaluation of the Terminal Area Precision Scheduling and Spacing System

This paper describes the design, development and results from a high fidelity human-in-the-loop simulation of an integrated set of trajectory-based automation tools providing precision scheduling, sequencing and controller merging and spacing functions. These integrated functions are combined into a system called the Terminal Area Precision Scheduling and Spacing (TAPSS) system. It is a strategic and tactical planning tool that provides Traffic Management Coordinators, En Route and Terminal Radar Approach Control air traffic controllers the ability to efficiently optimize the arrival capacity of a demand-impacted airport while simultaneously enabling fuel-efficient descent procedures. The TAPSS system consists of four-dimensional trajectory prediction, arrival runway balancing, aircraft separation constraint-based scheduling, traffic flow visualization and trajectory-based advisories to assist controllers in efficient metering, sequencing and spacing. The TAPSS system was evaluated and compared to today's ATC operation through extensive series of human-in-the-loop simulations for arrival flows into the Los Angeles International Airport. The test conditions included the variation of aircraft demand from a baseline of today's capacity constrained periods through 5%, 10% and 20% increases. Performance data were collected for engineering and human factor analysis and compared with similar operations both with and without the TAPSS system. The engineering data indicate operations with the TAPSS show up to a 10% increase in airport throughput during capacity constrained periods while maintaining fuel-efficient aircraft descent profiles from cruise to landing.

Swenson, Harry N.↗

NASA NTRS2015

Exploring Management of Arrival Spacing Using Route Extensions with Terminal Spacing Tools

Airports with shared runway operations between arrivals and departures can experience severe departure gridlock and delays during a heavy arrival push due to insufficient gaps in the arrival stream for aircraft to depart. The problem is accentuated in situations when a large gap in the arrival spacing has to be created at the last minute due to wake vortex separation requirements. At LaGuardia airport, wake vortex separation problems arise when a heavy jet, such as a B757, departing on Runway 31 needs additional spacing between arrivals on Runway 22. A standard solution for controllers in many airports in situations such as this is to extend the downwind leg of arrival aircraft to create extra space between the arrivals. The question addressed in this paper is how such route extensions would work with terminal scheduling operations, namely (1) the automated Terminal Sequencing and Spacing (TSS) tools and (2) a new scheduling tool which increases the availability of gaps for departure aircraft (Departure Sensitive Arrival Spacing or DSAS). In a simulated LaGuardia airport (LGA) Terminal Radar Approach Control (TRACON) airspace, two new RNAV arrival routes were created along with extensions to these routes. The arrival route from the south had a downwind leg extension near the airport in the final sector. The arrival route from the north had an extension in a feeder sector further from the airport. An exploratory one-hour run with the route extensions was compared to an hour run without the extensions. Topics included in the paper are 1) how the route extensions were developed, 2) a procedure outlining how the aircraft could be scheduled to the extensions and who would do it, and 3) the results of the exploratory run compared to the original run without the extensions. The results indicated that the extended downwind leg route helped to create a B757 departure gap in the middle of a packed arrival stream, resulting in a reduction of 11 minutes in average wait time for the B757s, but at a cost of increased controller self-reported workload from low to moderate.

time based flow management↗

NASA NTRS1992

Automated Power-Distribution System

Autonomous power-distribution system includes power-control equipment and automation equipment. System automatically schedules connection of power to loads and reconfigures itself when it detects fault. Potential terrestrial applications include optimization of consumption of power in homes, power supplies for autonomous land vehicles and vessels, and power supplies for automated industrial processes.

Ashworth, Barry↗

NASA NTRSDate not supplied

Airspace Performance Observations of Scalable Autonomous Operations in a High Density Vertiplex Simulation

The National Aeronautics and Space Administration’s (NASA’s) High Density Vertiplex (HDV) subproject aims to develop and demonstrate progressive automation technologies that contribute to the Advanced Air Mobility (AAM) concept. Using Human-and-Hardware-In-TheLoop (HHITL) techniques, HDV demonstrates initial vertiport automation services at vertiports with increased air traffic volume in both simulated and live test environments. In 2023, the Scalable Autonomous Operations (SAO) simulation was conducted in which prototype vertiport, airspace, and ground control station technologies were assessed on technical performance. During the SAO simulation, an observational study captured an initial impression of the HDV airspace performance, potential disruptions to the airspace, and highlighted some capability and procedural gaps. Observations took place in two parts. In the first part, five scenario use cases (Nominal, Missed Approach, Speed Change, Divert, and MultiAircraft Divert) were conducted with three human operator roles (Vertiport Manager, Fleet Manager, and Ground Control Station Operator). Researchers collected metrics on throughput, closest point of approach, and airborne delay. In the second part of the study, the Missed Approach scenario was observed under three traffic density levels (20, 40, and 60 operations per hour) to challenge the automation to correctly identify slots in the vertiport arrival schedule. The results showed that the automation successfully found a slot for the Missed Approach vehicle in the 20 operations per hour condition, after some delay it found one in the 40 condition, and it did not find one in the 60 condition. The observations of technical and human performance throughout the five scenario use cases and the Missed Approach case study indicated that for HDV to increase traffic density and maintain or increase throughput, airspace monitoring services should be able to detect and resolve conflicts between aircraft. Furthermore, the roles and responsibilities of human operators need additional definition when it comes to responding to vehicle conflicts.

Advanced Air Mobility↗

NASA NTRSDate not supplied

Airspace Performance Observations of Scalable Autonomous Operations in a High Density Vertiplex Simulation

The National Aeronautics and Space Administration’s (NASA’s) High Density Vertiplex (HDV) sub- project aims to develop and demonstrate progressive automation technologies that contribute to the Advanced Air Mobility (AAM) concept. Using Human-and-Hardware-In-The- Loop (HHITL) techniques, HDV demonstrates initial vertiport automation services at vertiports with increased air traffic volume in both simulated and live test environments. In 2023, the Scalable Autonomous Operations (SAO) simulation was conducted in which prototype vertiport, airspace, and ground control station technologies were assessed on technical performance. During the SAO simulation, an observational study captured an initial impression of the HDV airspace performance, potential disruptions to the airspace, and highlighted some capability and procedural gaps. Observations took place in two parts. In the first part, five scenario use cases (Nominal, Missed Approach, Speed Change, Divert, and Multi- Aircraft Divert) were conducted with three human operator roles (Vertiport Manager, Fleet Manager, and Ground Control Station Operator). Researchers collected metrics on throughput, closest point of approach, and airborne delay. In the second part of the study, the Missed Approach scenario was observed under three traffic density levels (20, 40, and 60 operations per hour) to challenge the automation to correctly identify slots in the vertiport arrival schedule. The results showed that the automation successfully found a slot for the Missed Approach vehicle in the 20 operations per hour condition, after some delay it found one in the 40 condition, and it did not find one in the 60 condition. The observations of technical and human performance throughout the five scenario use cases and the Missed Approach case study indicated that for HDV to increase traffic density and maintain or increase throughput, airspace monitoring services should be able to detect and resolve conflicts between aircraft. Furthermore, the roles and responsibilities of human operators need additional definition when it comes to responding to vehicle conflicts.

advanced air mobility↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗