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Johnston, Mark D.

Publications and source records attributed to Johnston, Mark D..

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

Deep Space Network↗
NASA NTRS2012

Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system is architected as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users who utilize the DSN (representing 37 projects including international partners and ground-based science and calibration users). The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. S(sup 3) has been used for negotiating schedules since April 2011, including the baseline schedules for three launching missions in late 2011. S(sup 3) supports a distributed scheduling model, in which changes can potentially be made by multiple users based on multiple schedule "workspaces" or versions of the schedule. This has led to several challenges in the design of the scheduling database, and of a change proposal workflow that allows users to concur with or to reject proposed schedule changes, and then counter-propose with alternative or additional suggested changes. This paper describes some key aspects of the S(sup 3) system and lessons learned from its operational deployment to date, focusing on the challenges of multi-user collaborative scheduling in a practical and mission-critical setting. We will also describe the ongoing project to extend S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

scheduling↗
NASA NTRS2011

Multi-Objective Scheduling for the Cluster II Constellation

This paper describes the application of the MUSE multiobjecctive scheduling framework to the Cluster II WBD scheduling domain. Cluster II is an ESA four-spacecraft constellation designed to study the plasma environment of the Earth and it's magnetosphere. One of the instruments on each of the four spacecraft is the Wide Band Data (WBD) plasma wave experiment. We have applied the MUSE evolutionary algorithm to the scheduling problem represented by this instrument, and the result has been adopted and utilized by the WBD schedulers for nearly a year. This paper describes the WBD scheduling problem, its representation in MUSE, and some of the visualization elements that provide insight into objective value tradeoffs.

observatory scheduilng↗
NASA NTRS2011

Developer Tools for Evaluating Multi-Objective Algorithms

Multi-objective algorithms for scheduling offer many advantages over the more conventional single objective approach. By keeping user objectives separate instead of combined, more information is available to the end user to make trade-offs between competing objectives. Unlike single objective algorithms, which produce a single solution, multi-objective algorithms produce a set of solutions, called a Pareto surface, where no solution is strictly dominated by another solution for all objectives. From the end-user perspective a Pareto-surface provides a tool for reasoning about trade-offs between competing objectives. From the perspective of a software developer multi-objective algorithms provide an additional challenge. How can you tell if one multi-objective algorithm is better than another? This paper presents formal and visual tools for evaluating multi-objective algorithms and shows how the developer process of selecting an algorithm parallels the end-user process of selecting a solution for execution out of the Pareto-Surface.

multi-objective optimization↗
NASA NTRS2011

Automated scheduling for NASA’s Deep Space Network

This paper describes the DSN Scheduling Engine (DSE) component of a new scheduling system being deployed for NASA’s Deep Space Network. The DSE provides core automation functionality for scheduling the network, including the interpretation of scheduling requirements expressed by users, their elaboration into tracking passes, and resolving conflicts and constraint violations. The DSE incorporates both systematic search and repair-based algorithms, used for different phases and purposes in the overall system. It has been integrated in with a web application which provides its functionality to all DSN users through a standard web browser. The system is in the process of deployment to operational status.

Tran, Daniel↗
NASA NTRS2010

Request-Driven Schedule Automation for the Deep Space Network

The DSN Scheduling Engine (DSE) has been developed to increase the level of automated scheduling support available to users of NASA s Deep Space Network (DSN). We have adopted a request-driven approach to DSN scheduling, in contrast to the activity-oriented approach used up to now. Scheduling requests allow users to declaratively specify patterns and conditions on their DSN service allocations, including timing, resource requirements, gaps, overlaps, time linkages among services, repetition, priorities, and a wide range of additional factors and preferences. The DSE incorporates a model of the key constraints and preferences of the DSN scheduling domain, along with algorithms to expand scheduling requests into valid resource allocations, to resolve schedule conflicts, and to repair unsatisfied requests. We use time-bounded systematic search with constraint relaxation to return nearby solutions if exact ones cannot be found, where the relaxation options and order are under user control. To explore the usability aspects of our approach we have developed a graphical user interface incorporating some crucial features to make it easier to work with complex scheduling requests. Among these are: progressive revelation of relevant detail, immediate propagation and visual feedback from a user s decisions, and a meeting calendar metaphor for repeated patterns of requests. Even as a prototype, the DSE has been deployed and adopted as the initial step in building the operational DSN schedule, thus representing an important initial validation of our overall approach. The DSE is a core element of the DSN Service Scheduling Software (S(sup 3)), a web-based collaborative scheduling system now under development for deployment to all DSN users.

Johnston, Mark D.↗
NASA NTRS2010

Multi-Objective Multi-User Scheduling for Space Science Missions

We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to re-use existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives are modeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angular momentum that would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

science planning↗
NASA NTRS2008

Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty

We have developed a novel technique to incorporate uncertainty modeling within an evolutionary algorithm approach to multi-objective scheduling, with the goal of identifying a Pareto frontier (tradeoff curve) that recognizes the likelihood of events that can impact the schedule outcome. Our approach is particularly applicable to the generation of multiobjective optimized robust schedules, where objectives are assigned a service level, for example that we require an objective value to be greater than or equal to X with Y% confidence. We have demonstrated that such an approach can, for example, minimize scheduling on less reliable resources, based solely on a resource reliability model and not on any ad hoc heuristics. We have also investigated an alternative method of optimizing for robustness, in which we add to the set of objectives a failure risk objective to minimize. We compare the advantages and disadvantages of these two approaches. Future plans for further developing this technology include its application to space-based observatory scheduling problems.

Johnston, Mark D.↗
NASA NTRS2006

Integrated Planning for Telepresence with Time Delays

Teleoperation of remote robotic systems over time delays in the range of 2-10 seconds poses a unique set of challenges. In the context of a supervisory control system for the JSC Robonaut humanoid robot, we have developed an 'intelligent assistant' that integrates an Artificial Intelligence planner (JSHOP2) with execution monitoring of the state of both the human supervisor and the remote robot. The assistant reasons simultaneously about the world state on both sides of the time delay, which represents a novel application of this technology. The purpose of the assistant is to provide advice to the human supervisor about current and future activities, derived from a sequence of high-level goals to be achieved. To do this, the assistant must simultaneously monitor and react to various data sources, including actions taken by the supervisor who is issuing commands to the robot (e.g. with a data glove), actions taken by the robot, and the environment of the robot, both as currently perceived over the time delay, along with the current sequence of goals. We have developed a 'leader/follower' software architecture to handle the dual time-shifted streams of execution feedback. In this paper we describe the integrated planner and its executive, and how it operates in normal and anomaly situations.

remote robotic systems↗
NASA NTRS2006

Integrated Planning for Telepresence with Time Delays

Integrated planning and execution of teleoperations in space with time delays is shown. The topics include: 1) The Problem; 2) Future Robot Surgery? 3) Approach Overview; 4) Robonaut; 5) Normal Planning and Execution; 6) Planner Context; 7) Implementation; 8) Use of JSHOP2; 9) Monitoring and Testing GUI; 10) Normal sequence: first the supervisor acts; 11) then the robot; 12) Robot might be late; 13) Supervisor can work ahead; 14) Deviations from Plan; 15) Robot State Change Example; 16) Accomplished goals skipped in replan; 17) Planning continuity; 18) Supervisor Deviation From Plan; 19) Intentional Deviation; and 20) Infeasible states.

teleoperation↗
NASA NTRS2005

Optimizing observing sequence design for periodic and non-periodic phenomena : a Bayesian approach

In this paper we report on our progress on addressing these issues. We have developed an approximate expression for the uniformity of phase coverage that can be used when scheduling to assess candidate sample times. We describe the results obtained using this estimator, and compare them with detailed simulations. We describe our progress and plans for integrating optimizing criteria for both periodic and non-periodic observations into a single observation sequence.

Bayesian↗
NASA NTRS2005

Automating Deep Space Network scheduling and conflict resolution

The Deep Space Network (DSN) is a central part of NASA's infrastructure for communicating with active space missions, from earth orbit to beyond the solar system. We describe our recent work in modeling the complexities of user requirements, and then scheduling and resolving conflicts on that basis. We emphasize our innovative use of background 'intelligent' assistants' that carry out search asynchrnously while the user is focusing on various aspects of the schedule.

conflict resolution↗
NASA NTRS1993

Development of an expert data reduction assistant

We propose the development of an expert system tool for the management and reduction of complex datasets. the proposed work is an extension of a successful prototype system for the calibration of CCD (charge coupled device) images developed by Dr. Johnston in 1987. (ref.: Proceedings of the Goddard Conference on Space Applications of Artificial Intelligence). The reduction of complex multi-parameter data sets presents severe challenges to a scientist. Not only must a particular data analysis system be mastered, (e.g. IRAF/SDAS/MIDAS), large amounts of data can require many days of tedious work and supervision by the scientist for even the most straightforward reductions. The proposed Expert Data Reduction Assistant will help the scientist overcome these obstacles by developing a reduction plan based on the data at hand and producing a script for the reduction of the data in a target common language.

Miller, Glenn E.↗
NASA NTRS1992

Spike: AI scheduling for Hubble Space Telescope after 18 months of orbital operations

This paper is a progress report on the Spike scheduling system, developed by the Space Telescope Science Institute for long-term scheduling of Hubble Space Telescope (HST) observations. Spike is an activity-based scheduler which exploits artificial intelligence (AI) techniques for constraint representation and for scheduling search. The system has been in operational use since shortly after HST launch in April 1990. Spike was adopted for several other satellite scheduling problems; of particular interest was the demonstration that the Spike framework is sufficiently flexible to handle both long-term and short-term scheduling, on timescales of years down to minutes or less. We describe the recent progress made in scheduling search techniques, the lessons learned from early HST operations, and the application of Spike to other problem domains. We also describe plans for the future evolution of the system.

Johnston, Mark D.↗
NASA NTRS1992

An extended abstract: A heuristic repair method for constraint-satisfaction and scheduling problems

The work described in this paper was inspired by a surprisingly effective neural network developed for scheduling astronomical observations on the Hubble Space Telescope. Our heuristic constraint satisfaction problem (CSP) method was distilled from an analysis of the network. In the process of carrying out the analysis, we discovered that the effectiveness of the network has little to do with its connectionist implementation. Furthermore, the ideas employed in the network can be implemented very efficiently within a symbolic CSP framework. The symbolic implementation is extremely simple. It also has the advantage that several different search strategies can be employed, although we have found that hill-climbing methods are particularly well-suited for the applications that we have investigated. We begin the paper with a brief review of the neural network. Following this, we describe our symbolic method for heuristic repair.

Minton, Steven↗
NASA NTRS1992

Development of an expert data reduction assistant

We propose the development of an expert system tool for the management and reduction of complex data sets. The proposed work is an extension of a successful prototype system for the calibration of CCD images developed by Dr. Johnston in 1987. The reduction of complex multi-parameter data sets presents severe challenges to a scientist. Not only must a particular data analysis system be mastered, (e.g. IRAF/SDAS/MIDAS), large amounts of data can require many days of tedious work and supervision by the scientist for even the most straightforward reductions. The proposed Expert Data Reduction Assistant will help the scientist overcome these obstacles by developing a reduction plan based on the data at hand and producing a script for the reduction of the data in a target common language.

Miller, Glenn E.↗