Engineering Papers⌕ Search

SEARCH · Engineering Papers

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Sailing Towards an Expressive Scheduling Language for Europa Clipper

The mission planners for NASA's Europa Clipper deep-space mission use automated scheduling software to generate activity plans and command sequences for multiple instruments and subsystems before sending the sequences for execution on board. Both science and engineering planners must translate their intents into expressions of mission constraints, goals, and preferences that the scheduling engine understands. This paper describes the ongoing development of a dedicated domain-specific java-embedded language that can efficiently and accurately capture such concerns for the Europa Clipper mission, along with a user-interface companion for the language.

Schaffer, Steve↗
NASA NTRS1990

Concepts in distributed planning, scheduling, and control

To support instrument and experiment operations effectively in the Space Station era, planning, scheduling, and control must allow for: (1) interactive real-time remote operations; (2) responsive scheduling and rescheduling; (3) support of the full range of distributed science, application and commercial users; (4) interaction and cooperation among distributed users; and (5) efficient use of often onboard, communications, and ground-based resources. We suggest conceptual and managerial approaches that address these needs. Specifically, we describe approaches to distributed planning, scheduling and control functions that are based on resources and on a distributed knowledge hierarchy. We describe the scheduling functions as the component of the integrated space-ground Operations Management System. We include the integration of the planning and scheduling functions with the real-time operations control system, and we discuss automated scheduling assistants. These suggested approaches, taken from the users' point-of-view, have resulted in two prototype systems: Operations and Science Instrument Support package and Science User Resource Planning and Scheduling System.

Hansen, Elaine↗
NASA NTRS2013

Automated Long - Term Scheduling for the SOFIA Airborne Observatory

The NASA Stratospheric Observatory for Infrared Astronomy (SOFIA) is a joint US/German project to develop and operate a gyro-stabilized 2.5-meter telescope in a Boeing 747SP. SOFIA's first science observations were made in December 2010. During 2011, SOFIA accomplished 30 flights in the "Early Science" program as well as a deployment to Germany. The new observing period, known as Cycle 1, is scheduled to begin in 2012. It includes 46 science flights grouped in four multi-week observing campaigns spread through a 13-month span. Automation of the flight scheduling process offers a major challenge to the SOFIA mission operations. First because it is needed to mitigate its relatively high cost per unit observing time compared to space-borne missions. Second because automated scheduling techniques available for ground-based and space-based telescopes are inappropriate for an airborne observatory. Although serious attempts have been made in the past to solve part of the problem, until recently mission operations staff was still manually scheduling flights. We present in this paper a new automated solution for generating SOFIA long-term schedules that will be used in operations from the Cycle 1 observing period. We describe the constraints that should be satisfied to solve the SOFIA scheduling problem in the context of real operations. We establish key formulas required to efficiently calculate the aircraft course over ground when evaluating flight schedules. We describe the foundations of the SOFIA long-term scheduler, the constraint representation, and the random search based algorithm that generates observation and instrument schedules. Finally, we report on how the new long-term scheduler has been used in operations to date.

Civeit, Thomas↗
NASA NTRS1999

Automated Planning for a Deep Space Communications Station

This paper describes the application of Artificial Intelligence planning techniques to the problem of antenna track plan generation for a NASA Deep Space Communications Station. Me described system enables an antenna communications station to automatically respond to a set of tracking goals by correctly configuring the appropriate hardware and software to provide the requested communication services. To perform this task, the Automated Scheduling and Planning Environment (ASPEN) has been applied to automatically produce antenna trucking plans that are tailored to support a set of input goals. In this paper, we describe the antenna automation problem, the ASPEN planning and scheduling system, how ASPEN is used to generate antenna track plans, the results of several technology demonstrations, and future work utilizing dynamic planning technology.

Estlin, Tara↗
NASA NTRS1991

Human factors issues in the design of user interfaces for planning and scheduling

The purpose is to provide and overview of human factors issues that impact the effectiveness of user interfaces to automated scheduling tools. The following methods are employed: (1) a survey of planning and scheduling tools; (2) the identification and analysis of human factors issues; (3) the development of design guidelines based on human factors literature; and (4) the generation of display concepts to illustrate guidelines.

Murphy, Elizabeth D.↗
NASA NTRS2015

Human Factors Assessment of Disturbances to Scheduled Performance-Based Navigation Arrival Operations

The introduction of Performance-Based Navigation (PBN) specifications to air traffic management has resulted in many benefits during nominal operations, including shorter flight paths, reduced fuel costs, and improved terminal area arrival rates. However, these benefits become less noticeable during off-nominal operations where aircraft are routinely interrupted from staying on PBN procedures due to disturbances such as missed approaches. This human-in-the-loop (HITL) study used multiple types of disturbance events to perturb the arrival schedule. Perturbed schedules were managed with different types of schedule adjustments, including a condition with no adjustments. The study collected data on a host of dependent variables, including human factors measures on controller workload and system performance measures such as schedule nonconformance (nc). Initial analyses showed strong correlations between aggregated controller workload and aggregated nc, as well as benefits of both automatic and manual schedule adjustments for increasing system performance, such as reduced PBN procedure interruptions. The goal of this paper is to further test these initial findings. The results indicated that an increase in schedule nonconformance correlated with an increase in controller workload at specific time intervals, and automated schedule adjustments consistently reduced controller workload associated with nonconformance.

Human factors↗
NASA NTRS1989

Scheduling with chronology-directed search

The intelligent use of scheduling heuristics can enable the production of effective schedules in spite of the inherent intractability of scheduling in complex, real-world domains. In order to effectively use these heuristics, information is needed on the current state of the evolving schedule. One method to obtain this information is to use chronologies - limited histories of the scheduling process. Chronology-directed search is an important component of the heuristic approach to automated scheduling.

Biefeld, Eric W.↗
NASA NTRS2003

Abstract-Reasoning Software for Coordinating Multiple Agents

A computer program for scheduling the activities of multiple agents that share limited resources has been incorporated into the Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles. In the original intended application, the agents would be multiple spacecraft and/or robotic vehicles engaged in scientific exploration of distant planets. The program could also be used on Earth in such diverse settings as production lines and military maneuvers. This program includes a planning/scheduling subprogram of the iterative repair type that reasons about the activities of multiple agents at abstract levels in order to greatly improve the scheduling of their use of shared resources. The program summarizes the information about the constraints on, and resource requirements of, abstract activities on the basis of the constraints and requirements that pertain to their potential refinements (decomposition into less-abstract and ultimately to primitive activities). The advantage of reasoning about summary information is that time needed to find consistent schedules is exponentially smaller than the time that would be needed for reasoning about the same tasks at the primitive level.

Clement, Bradley↗
NASA NTRS2000

Iterative Repair Planning for Spacecraft Operations Using the Aspen System

This paper describes the Automated Scheduling and Planning Environment (ASPEN). ASPEN encodes complex spacecraft knowledge of operability constraints, flight rules, spacecraft hardware, science experiments and operations procedures to allow for automated generation of low level spacecraft sequences. Using a technique called iterative repair, ASPEN classifies constraint violations (i.e., conflicts) and attempts to repair each by performing a planning or scheduling operation. It must reason about which conflict to resolve first and what repair method to try for the given conflict. ASPEN is currently being utilized in the development of automated planner/scheduler systems for several spacecraft, including the UFO-1 naval communications satellite and the Citizen Explorer (CX1) satellite, as well as for planetary rover operations and antenna ground systems automation. This paper focuses on the algorithm and search strategies employed by ASPEN to resolve spacecraft operations constraints, as well as the data structures for representing these constraints.

Rabideau, G.↗
NASA NTRS2002

The Relationship of Self-Efficacy and Complacency in Pilot-Automation Interaction

Pilot 'complacency' has been implicated as a contributing factor in numerous aviation accidents and incidents. The term has become more prominent with the increase in automation technology in modern cockpits and, therefore, research has been focused on understanding the factors that may mitigate its effect on pilot-automation interaction. The study examined self-efficacy of supervisory monitoring and the relationship between complacency on strategy of pilot use of automation for workload management under automation schedules that produce the potential for complacency. The results showed that self-efficacy can be a 'double-edged' sword in reducing potential for automation-induced complacency but limiting workload management strategies and increasing other hazardous states of awareness.

Prinzel, Lawrence J., III↗
NASA NTRS1990

Concepts in Distributed Scheduling and Control

To support instrument and experiment operations effectively in the Space Station era, planning, scheduling and control must allow for: interactive, realtime, remote operations; responsive scheduling and rescheduling; support of the full range of distributed science, application and commercial users; interaction and cooperation among distributed users; and efficient use of on-board, communications, and ground-based resources. We suggest conceptual and managerial approaches that address these needs. Specifically, we describe an approach to distributed planning, scheduling and control functions that is based on resources and on a distributed knowledge hierarchy. We describe these functions as components of an integrated management system. We discuss automated scheduling assistants and integration of planning and scheduling functions with realtime operations control. The suggested approach, taken from a users' point-of-view, has resulted in the Science User Resource Planning and Scheduling System (SURPASS). In this paper, we describe the major components of SURPASS and discuss the features of this innovative prototype. Further ideas concerning instrument planning, scheduling, and control may be found in the Space Station Instrument Control System Study.

Hansen, Elaine R.↗
NASA NTRS1991

Long range science scheduling for the Hubble Space Telescope

Observations with NASA's Hubble Space Telescope (HST) are scheduled with the assistance of a long-range scheduling system (SPIKE) that was developed using artificial intelligence techniques. In earlier papers, the system architecture and the constraint representation and propagation mechanisms were described. The development of high-level automated scheduling tools, including tools based on constraint satisfaction techniques and neural networks is described. The performance of these tools in scheduling HST observations is discussed.

Miller, Glenn↗
NASA NTRS2012

Initial Investigations of Controller Tools and Procedures for Schedule-Based Arrival Operations with Mixed Flight-Deck Interval Management Equipage

NASA's Air Traffic Management Demonstration-1 (ATD-1) is a multi-year effort to demonstrate high-throughput, fuel-efficient arrivals at a major U.S. airport using NASA-developed scheduling automation, controller decision-support tools, and ADS-B-enabled Flight-Deck Interval Management (FIM) avionics. First-year accomplishments include the development of a concept of operations for managing scheduled arrivals flying Optimized Profile Descents with equipped aircraft conducting FIM operations, and the integration of laboratory prototypes of the core ATD-1 technologies. Following each integration phase, a human-in-the-loop simulation was conducted to evaluate and refine controller tools, procedures, and clearance phraseology. From a ground-side perspective, the results indicate the concept is viable and the operations are safe and acceptable. Additional training is required for smooth operations that yield notable benefits, particularly in the areas of FIM operations and clearance phraseology.

Callantine, Todd J.↗
NASA NTRSDate not supplied

Automation and AI in Space Drilling

Future planetary surface sampling missions, such as delving past the near-surface ice layers on Mars in search of organics and possibly signs of past/extant life, will require lightweight, low-mass planetary drilling and sample handling. Unlike terrestrial drills, these exploration drills must work dry (without drilling muds or gas), blind (no prior local or regional seismic or other surveys), and light (very low downward force or weight on bit, and perhaps 100 W available from solar power or batteries). Given the lightspeed transmission delays to Mars and outward, an exploratory planetary drill cannot be controlled directly from Earth. Drills that penetrate deeper than a few centimeters are likely to get stuck if operated open-loop (the MSL drill only penetrates 5 cm, and the MER Rock Abrasion Tools 5 mm by comparison), so some form of local drill control is required. In the relatively near-term, human crews cannot be presumed to be available for surface instrument teleoperation. Therefore highly automated drill and sample-transfer operations will be required, to explore the subsurface with the ability to safe robotic drilling systems and recover and continue on from the most probable fault conditions. Current automation, scheduling and diagnostic approaches will be discussed that roughly track the actions and roles of humans in terrestrial manual drilling operations.

drill automation↗
NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗
NASA NTRS2015

An Updated Process for Automated Deepspace Conjunction Assessment

There is currently a high level of interest in the areas of conjunction assessment and collision avoidance from organizations conducting space operations. Current conjunction assessment activity is mainly focused on spacecraft and debris in the Earth orbital environment [1]. However, collisions are possible in other orbital environments as well [2]. This paper will focus on the current operations of and recent updates to the Multimission Automated Deep Space Conjunction Assessment Process (MADCAP) used at the Jet Propulsion Laboratory for NASA to perform conjunction assessment at Mars and the Moon. Various space agencies have satellites in orbit at Mars and the Moon with additional future missions planned. The consequences of collisions are catastrophically high. Intuitive notions predict low probability of collisions in these sparsely populated environments, but may be inaccurate due to several factors. Orbits of scientific interest often tend to have similar characteristics as do the orbits of spacecraft that provide a communications relay for surface missions. The MADCAP process is controlled by an automated scheduler which initializes analysis based on a set timetable or the appearance of new ephemeris files either locally or on the Deep Space Network (DSN) Portal. The process then generates and communicates reports which are used to facilitate collision avoidance decisions. The paper also describes the operational experience and utilization of the automated tool during periods of high activity and interest such as: the close approaches of NASA's Lunar Atmosphere & Dust Environment Explorer (LADEE) and Lunar Reconnaissance Orbiter (LRO) during the LADEE mission. In addition, special consideration was required for the treatment of missions with rapidly varying orbits and less reliable long term downtrack estimates; in particular this was necessitated by perturbations to MAVEN's orbit induced by the Martian atmosphere. The application of special techniques to non-operational spacecraft with large uncertainties is also studied. Areas for future work are also described. Although the applications discussed in this paper are in the Martian and Lunar environments, the techniques are not unique to these bodies and could be applied to other orbital environments.

collision↗
NASA NTRS1988

A scheduling and resource management system for space applications

Every spacecraft, whether in orbit around the earth or an a deep space flight, has at its disposal limited amounts of the resources for it to accomplish its mission. Activity scheduling is currently a costly, human intensive task which requires a great deal of expertise. It belongs to a class of problems whose complexity increases exponentially with the number of operations. NASA has in the past accomplished this task by using a great deal of manpower, a large number of negotiating sessions, interminable bouts of phone tag, and mountains of paperwork. Lately the situation has improved with the introduction of automated scheduling techniques, but these to date still require expert involvement and fall short in some important ways. A prototype activity scheduler, MAESTRO, is introduced which is capable of meeting the needs of many NASA missions, eventually to include the Space Station. The approach to resource constrained scheduling is first discussed, then the intended domain for MAESTRO is described along with its design and current capabilities. A description of planned enhancements and revisions to the systems is also presented.

Britt, Daniel L.↗