Search NASA⌕ Search

SEARCH · Search NASA

Results for “automated scheduling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

A human factors approach to range scheduling for satellite control

Range scheduling for satellite control presents a classical problem: supervisory control of a large-scale dynamic system, with unwieldy amounts of interrelated data used as inputs to the decision process. Increased automation of the task, with the appropriate human-computer interface, is highly desirable. The development and user evaluation of a semi-automated network range scheduling system is described. The system incorporates a synergistic human-computer interface consisting of a large screen color display, voice input/output, a 'sonic pen' pointing device, a touchscreen color CRT, and a standard keyboard. From a human factors standpoint, this development represents the first major improvement in almost 30 years to the satellite control network scheduling task.

Wright, Cameron H. G.↗
NASA NTRS1996

Mission simulation as an approach to develop requirements for automation in Advanced Life Support Systems

This paper examines mission simulation as an approach to develop requirements for automation and robotics for Advanced Life Support Systems (ALSS). The focus is on requirements and applications for command and control, control and monitoring, situation assessment and response, diagnosis and recovery, adaptive planning and scheduling, and other automation applications in addition to mechanized equipment and robotics applications to reduce the excessive human labor requirements to operate and maintain an ALSS. Based on principles of systems engineering, an approach is proposed to assess requirements for automation and robotics using mission simulation tools. First, the story of a simulated mission is defined in terms of processes with attendant types of resources needed, including options for use of automation and robotic systems. Next, systems dynamics models are used in simulation to reveal the implications for selected resource allocation schemes in terms of resources required to complete operational tasks. The simulations not only help establish ALSS design criteria, but also may offer guidance to ALSS research efforts by identifying gaps in knowledge about procedures and/or biophysical processes. Simulations of a planned one-year mission with 4 crewmembers in a Human Rated Test Facility are presented as an approach to evaluation of mission feasibility and definition of automation and robotics requirements.

NASA Discipline Number 61-10↗
NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
NASA NTRS1988

An expert system for shuttle and satellite radar tracker scheduling

This expert system automates and optimizes radar tracker selection for shuttle missions. The expert system is written in the FORTRAN and C languages on an HP9000. It is portable to any UNIX machine having both ANSI-77 FORTRAN and C language compilers. It is a rule based expert system that selects tracking stations from the S-band and C-band radar stations and the TDRSS east and TDRSS west satellites under a variety of conditions. The expert system was prototyped on the Symbolics in the Automated Reasoning Tool (ART) and ZetaLisp. After the prototype demonstrated an acceptable automation of the process of selecting tracking stations to support the orbit determination requirements of Shuttle missions, the basic ART rules of the prototype were ported to the HP9000 computer using the CLIPS language. CLIPS is a forward-chaining rule-based expert system language written in C. Prior to the development of this expert system the selection process was a tedious manual process and expensive in terms of human resources. Manual tracking station selection required from 1 to 2 man weeks per mission; whereas the expert system can complete the selection process in about 2 hours.

Mitchell, Paul↗
NASA NTRS2002

Automating CPM-GOMS

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apex's UI automatically generates PERT charts, which allow modelers to visualize a model's complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling. available to model human users, the Goals, Operators, Methods, and Selection (GOMS) method [6, 21] has been the most widely used, providing accurate, often zero-parameter, predictions of the routine performance of skilled users in a wide range of procedural tasks [6, 13, 15, 27, 28]. GOMS is meant to model routine behavior. The user is assumed to have methods that apply sequences of operators and to achieve a goal. Selection rules are applied when there is more than one method to achieve a goal. Many routine tasks lend themselves well to such decomposition. Decomposition produces a representation of the task as a set of nested goal states that include an initial state and a final state. The iterative decomposition into goals and nested subgoals can terminate in primitives of any desired granularity, the choice of level of detail dependent on the predictions required. Although GOMS has proven useful in HCI, tools to support the construction of GOMS models have not yet come into general use.

GOMS↗
NASA NTRS2021

A Simulation Study of Bin-and-Sort Policies in a Distributed System for Flights Scheduling

This is a report on the results of a simulation experiment conducted on an automated distributed system for scheduling flights in a shared airspace. The scheduling is carried out by computer systems owned by the private operators of the flights, which interface through a centralized independent server. The scheduling requires determining which of the group of flights at hand (whether owned by the same operator or different ones) are to have the advantage of being scheduled before the others. A chosen method of grouping and prioritizing flights must: have the ability to schedule a flight promptly, prevent scheduling conflicts between pairs of overlapping flight routes, and be computationally feasible. Scheduling flights one by one is the First Come First Served policy. Scheduling too many at a time is infeasible. The type of scheduling policy proposed herein, called bin-and-sort, is an attempt to reach a compromise between these two extremes. Bin-and-sort allows for each operator to choose its criteria for prioritizing its flights, and for the centralized component to use different arbitration criteria. A number of these criteria are used in the simulation presented herein. The results of each criterion choice are reported and analyzed.

flight scheduling↗
NASA NTRS2021

A Simulation Study of Bin-and-Sort Policies in a Distributed System for Flights Scheduling

This is a report on the results of a simulation experiment conducted on an automated distributed system for scheduling flights in a shared airspace. The scheduling is carried out by computer systems owned by the private operators of the flights, which interface through a centralized independent server. The scheduling requires determining which of the group of flights at hand (whether owned by the same operator or different ones) are to have the advantage of being scheduled before the others. A chosen method of grouping and prioritizing flights must: have the ability to schedule a flight promptly, prevent scheduling conflicts between pairs of overlapping flight routes, and be computationally feasible. Scheduling flights one by one is the First Come First Served policy. Scheduling too many at a time is infeasible. The type of scheduling policy proposed herein, called bin-and-sort, is an attempt to reach a compromise between these two extremes. Bin-and-sort allows for each operator to choose its criteria for prioritizing its flights, and for the centralized component to use different arbitration criteria. A number of these criteria are used in the simulation presented herein. The results of each criterion choice are reported and analyzed.

flight scheduling↗
NASA NTRS2008

Supporting Increased Autonomy for a Mars Rover

This paper presents an architecture and a set of technology for performing autonomous science and commanding for a planetary rover. The MER rovers have outperformed all expectations by lasting over 1100 sols (or Martian days), which is an order of magnitude longer than their original mission goal. The longevity of these vehicles will have significant effects on future mission goals, such as objectives for the Mars Science Laboratory rover mission (scheduled to fly in 2009) and the Astrobiology Field Lab rover mission (scheduled to potentially fly in 2016). Common objectives for future rover missions to Mars include the handling of opportunistic science, long-range or multi-sol driving, and onboard fault diagnosis and recovery. To handle these goals, a number of new technologies have been developed and integrated as part of the CLARAty architecture. CLARAty is a unified and reusable robotic architecture that was designed to simplify the integration, testing and maturation of robotic technologies for future missions. This paper focuses on technology comprising the CLARAty Decision Layer, which was designed to support and validate high-level autonomy technologies, such as automated planning and scheduling and onboard data analysis.

autonomous science↗
NASA NTRS1991

The Ames-Lockheed orbiter processing scheduling system

A general purpose scheduling system and its application to Space Shuttle Orbiter Processing at the Kennedy Space Center (KSC) are described. Orbiter processing entails all the inspection, testing, repair, and maintenance necessary to prepare the Shuttle for launch and takes place within the Orbiter Processing Facility (OPF) at KSC, the Vehicle Assembly Building (VAB), and on the launch pad. The problems are extremely combinatoric in that there are thousands of tasks, resources, and other temporal considerations that must be coordinated. Researchers are building a scheduling tool that they hope will be an integral part of automating the planning and scheduling process at KSC. The scheduling engine is domain independent and is also being applied to Space Shuttle cargo processing problems as well as wind tunnel scheduling problems.

Zweben, Monte↗
NASA NTRS1985

Software Cost-Estimation Model

Software Cost Estimation Model SOFTCOST provides automated resource and schedule model for software development. Combines several cost models found in open literature into one comprehensive set of algorithms. Compensates for nearly fifty implementation factors relative to size of task, inherited baseline, organizational and system environment and difficulty of task.

Tausworthe, R. C.↗
NASA NTRS1987

Expert systems tools for Hubble Space Telescope observation scheduling

This paper discusses the utility of expert systems techniques for Hubble Space Telescope (HST) planning and scheduling and describes a plan for development of expert system tools which will augment the existing ground system. Additional capabilities provided by these tools will include graphics-oriented plan evaluation, long-range analysis of the observation pool, analysis of optimal scheduling time intervals, constructing sequences of spacecraft activities which minimize operational overhead, and optimization of linkages between observations. Initial prototyping of a scheduler used the Automated Reasoning Tool running on a LISP workstation.

Miller, Glenn↗
NASA NTRS1987

Expert systems tools for Hubble Space Telescope observation scheduling

The utility of expert systems techniques for the Hubble Space Telescope (HST) planning and scheduling is discussed and a plan for development of expert system tools which will augment the existing ground system is described. Additional capabilities provided by these tools will include graphics-oriented plan evaluation, long-range analysis of the observation pool, analysis of optimal scheduling time intervals, constructing sequences of spacecraft activities which minimize operational overhead, and optimization of linkages between observations. Initial prototyping of a scheduler used the Automated Reasoning Tool running on a LISP workstation.

Miller, Glenn↗
NASA NTRS1990

Expert systems and advanced automation for space missions operations

Increased complexity of space missions during the 1980s led to the introduction of expert systems and advanced automation techniques in mission operations. This paper describes several technologies in operational use or under development at the National Aeronautics and Space Administration's Goddard Space Flight Center. Several expert systems are described that diagnose faults, analyze spacecraft operations and onboard subsystem performance (in conjunction with neural networks), and perform data quality and data accounting functions. The design of customized user interfaces is discussed, with examples of their application to space missions. Displays, which allow mission operators to see the spacecraft position, orientation, and configuration under a variety of operating conditions, are described. Automated systems for scheduling are discussed, and a testbed that allows tests and demonstrations of the associated architectures, interface protocols, and operations concepts is described. Lessons learned are summarized.

Durrani, Sajjad H.↗
NASA NTRS1991

Space Network Control Conference on Resource Allocation Concepts and Approaches

The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

Moe, Karen L.↗
NASA NTRS1993

Using AI/expert system technology to automate planning and replanning for the HST servicing missions

This paper describes a knowledge-based system that has been developed to automate planning and scheduling for the Hubble Space Telescope (HST) Servicing Missions. This new system is the Servicing Mission Planning and Replanning Tool (SM/PART). SM/PART has been delivered to the HST Flight Operations Team (FOT) at Goddard Space Flight Center (GSFC) where it is being used to build integrated time lines and command plans to control the activities of the HST, Shuttle, Crew and ground systems for the next HST Servicing Mission. SM/PART reuses and extends AI/expert system technology from Interactive Experimenter Planning System (IEPS) systems to build or rebuild time lines and command plans more rapidly than was possible for previous missions where they were built manually. This capability provides an important safety factor for the HST, Shuttle and Crew in case unexpected events occur during the mission.

Bogovich, L.↗
NASA NTRS1997

ASPEN: EO-1 Mission Activity Planning Made Easy

This paper describes the application of an automated planning and scheduling system to the NASA Earth Orbitin 1 (EO-1) missions. The planning system, ASPEN, is used to autonomously schedule the daily activites of the satellite.

ASPEN Activity Planning↗