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NASA NTRS1947

Scale effect and optimum relations for sea surface planning

From the general dimensional and mechanical similarity theory it follows that a condition of steady motion of a given shape\bottom with constant speed on the surface of water is determined by four nondimensional parameters. By considering the various systems of independent parameters which are applied in theory and practice and special tests, there is determined their mutual relations and their suitability as planning characteristics. In studying the scale effect on the basis of the Prnndtl formula for the friction coefficient for a turbulent condition the order of magnitude is given of the error in applying the model data to full scale in the case of a single-step bottom For a bottom of complicated shape it is shown how from the test data of the hydrodynamic characteristics for one speed with various loads, or one load with various speeds, there may be obtained by simple computation with good approximation the hydrodynamic characteristics for a different speed or for a different load. (These considerations may be of use in solving certain problems on the stability of planning.) This permits extrapolating the curve of resistance against speed for large speeds inaccessible in the tank tests or for other loads which were not tested. The data obtained by computation are in good agreement with the test results. Problems regarding the optimum trim angle or the optimum width in the case of planning of a flat plate are considered from the point of view of the minimum resistance for a given load on the water and planning speeds. Formulas and graphs are given for the optimum value of the planning coefficient and the corresponding values of the trim angle and width of the flat plate.

Sedov, L.↗

NASA NTRS2005

Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

Otterstatter, Matthew R.↗

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 NTRS2006

An Abstract Plan Preparation Language

This paper presents a new planning language that is more abstract than most existing planning languages such as the Planning Domain Definition Language (PDDL) or the New Domain Description Language (NDDL). The goal of this language is to simplify the formal analysis and specification of planning problems that are intended for safety-critical applications such as power management or automated rendezvous in future manned spacecraft. The new language has been named the Abstract Plan Preparation Language (APPL). A translator from APPL to NDDL has been developed in support of the Spacecraft Autonomy for Vehicles and Habitats Project (SAVH) sponsored by the Explorations Technology Development Program, which is seeking to mature autonomy technology for application to the new Crew Exploration Vehicle (CEV) that will replace the Space Shuttle.

Butler, Ricky W.↗

NASA NTRS2007

Genesis Contingency Planning and Mishap Recovery: The Sample Curation View

Planning for sample preservation and curation was part of mission design from the beginning. One of the scientific objectives for Genesis included collecting samples of three regimes of the solar wind in addition to collecting bulk solar wind during the mission. Collectors were fabricated in different thicknesses for each regime of the solar wind and attached to separate frames exposed to the solar wind during specific periods of solar activity associated with each regime. The original plan to determine the solar regime sampled for specific collectors was to identify to which frame the collector was attached. However, the collectors were dislodged during the hard landing making identification by frame attachment impossible. Because regimes were also identified by thickness of the collector, the regime sampled is identified by measuring fragment thickness. A variety of collector materials and thin films applied to substrates were selected and qualified for flight. This diversity provided elemental measurement in more than one material, mitigating effects of diffusion rates and/or radiation damage. It also mitigated against different material and substrate strengths resulting in differing effects of the hard landing. For example, silicon crystal substrates broke into smaller fragments than sapphire-based substrates and diamond surfaces were more resilient to flying debris damage than gold. The primary responsibility of the curation team for recovery was process documentation. Contingency planning for the recovery phase expanded this responsibility to include not only equipment to document, but also gather, contain and identify samples from the landing area and the recovered spacecraft. The team developed contingency plans for various scenarios as part of mission planning that included topographic maps to aid in site recovery and identification of different modes of transport and purge capability depending on damage. A clean tent, set-up at Utah Test & Training Range to control the environment for processing the sample return capsule and cleanly installing a nitrogen purge to the canister, was used to control the environment for extracting collector fragments from the damaged canister and to document and package over 10,000 collector fragments.

Stansbery, E. K.↗

NASA NTRS2003

Methods and apparatus for integrating, organizing, and accessing flight planning and other data on multifunction cockpit displays

A method and system for displaying a flight plan such that an entire flight plan is viewable through the use of scrolling devices is disclosed. The flight plan display may also include a method and system for collapsing and expanding a flight plan display, have provisions for the conspicuous marking of changes to a flight plan, the use of tabs to switch between various displays of data, and access to a navigation database that allows a user to view information about various navigational aids. The database may also the access to the information about the navigational aids to be prioritized based on proximity to the current position of the aircraft.

Gibbs, Michael J.↗

NASA NTRS2008

Verification and Planning Based on Coinductive Logic Programming

Coinduction is a powerful technique for reasoning about unfounded sets, unbounded structures, infinite automata, and interactive computations [6]. Where induction corresponds to least fixed point's semantics, coinduction corresponds to greatest fixed point semantics. Recently coinduction has been incorporated into logic programming and an elegant operational semantics developed for it [11, 12]. This operational semantics is the greatest fix point counterpart of SLD resolution (SLD resolution imparts operational semantics to least fix point based computations) and is termed co- SLD resolution. In co-SLD resolution, a predicate goal p( t) succeeds if it unifies with one of its ancestor calls. In addition, rational infinite terms are allowed as arguments of predicates. Infinite terms are represented as solutions to unification equations and the occurs check is omitted during the unification process. Coinductive Logic Programming (Co-LP) and Co-SLD resolution can be used to elegantly perform model checking and planning. A combined SLD and Co-SLD resolution based LP system forms the common basis for planning, scheduling, verification, model checking, and constraint solving [9, 4]. This is achieved by amalgamating SLD resolution, co-SLD resolution, and constraint logic programming [13] in a single logic programming system. Given that parallelism in logic programs can be implicitly exploited [8], complex, compute-intensive applications (planning, scheduling, model checking, etc.) can be executed in parallel on multi-core machines. Parallel execution can result in speed-ups as well as in larger instances of the problems being solved. In the remainder we elaborate on (i) how planning can be elegantly and efficiently performed under real-time constraints, (ii) how real-time systems can be elegantly and efficiently model- checked, as well as (iii) how hybrid systems can be verified in a combined system with both co-SLD and SLD resolution. Implementations of co-SLD resolution as well as preliminary implementations of the planning and verification applications have been developed [4]. Co-LP and Model Checking: The vast majority of properties that are to be verified can be classified into safety properties and liveness properties. It is well known within model checking that safety properties can be verified by reachability analysis, i.e, if a counter-example to the property exists, it can be finitely determined by enumerating all the reachable states of the Kripke structure.

Bansal, Ajay↗

NASA NTRS2008

Guidance and Control Software Project Data - Volume 1: Planning Documents

The Guidance and Control Software (GCS) project was the last in a series of software reliability studies conducted at Langley Research Center between 1977 and 1994. The technical results of the GCS project were recorded after the experiment was completed. Some of the support documentation produced as part of the experiment, however, is serving an unexpected role far beyond its original project context. Some of the software used as part of the GCS project was developed to conform to the RTCA/DO-178B software standard, "Software Considerations in Airborne Systems and Equipment Certification," used in the civil aviation industry. That standard requires extensive documentation throughout the software development life cycle, including plans, software requirements, design and source code, verification cases and results, and configuration management and quality control data. The project documentation that includes this information is open for public scrutiny without the legal or safety implications associated with comparable data from an avionics manufacturer. This public availability has afforded an opportunity to use the GCS project documents for DO-178B training. This report provides a brief overview of the GCS project, describes the 4-volume set of documents and the role they are playing in training, and includes the planning documents from the GCS project. Volume 1 contains five appendices: A. Plan for Software Aspects of Certification for the Guidance and Control Software Project; B. Software Development Standards for the Guidance and Control Software Project; C. Software Verification Plan for the Guidance and Control Software Project; D. Software Configuration Management Plan for the Guidance and Control Software Project; and E. Software Quality Assurance Activities.

Hayhurst, Kelly J.↗

NASA NTRS2009

Human Research Program Integrated Research Plan

The Integrated Research Plan (IRP) describes the portfolio of Human Research Program (HRP) research and technology tasks. The IRP is the HRP strategic and tactical plan for research necessary to meet HRP requirements. The need to produce an IRP is established in HRP-47052, Human Research Program - Program Plan, and is under configuration management control of the Human Research Program Control Board (HRPCB). Crew health and performance is critical to successful human exploration beyond low Earth orbit. The Human Research Program (HRP) is essential to enabling extended periods of space exploration because it provides knowledge and tools to mitigate risks to human health and performance. Risks include physiological and behavioral effects from radiation and hypogravity environments, as well as unique challenges in medical support, human factors, and behavioral or psychological factors. The Human Research Program (HRP) delivers human health and performance countermeasures, knowledge, technologies and tools to enable safe, reliable, and productive human space exploration. Without HRP results, NASA will face unknown and unacceptable risks for mission success and post-mission crew health. This Integrated Research Plan (IRP) describes HRP s approach and research activities that are intended to address the needs of human space exploration and serve HRP customers and how they are integrated to provide a risk mitigation tool. The scope of the IRP is limited to the activities that can be conducted with the resources available to the HRP; it does not contain activities that would be performed if additional resources were available. The timescale of human space exploration is envisioned to take many decades. The IRP illustrates the program s research plan through the timescale of early lunar missions of extended duration.

Source record↗

NASA NTRS2009

Computing Bounds on Resource Levels for Flexible Plans

A new algorithm efficiently computes the tightest exact bound on the levels of resources induced by a flexible activity plan (see figure). Tightness of bounds is extremely important for computations involved in planning because tight bounds can save potentially exponential amounts of search (through early backtracking and detection of solutions), relative to looser bounds. The bound computed by the new algorithm, denoted the resource-level envelope, constitutes the measure of maximum and minimum consumption of resources at any time for all fixed-time schedules in the flexible plan. At each time, the envelope guarantees that there are two fixed-time instantiations one that produces the minimum level and one that produces the maximum level. Therefore, the resource-level envelope is the tightest possible resource-level bound for a flexible plan because any tighter bound would exclude the contribution of at least one fixed-time schedule. If the resource- level envelope can be computed efficiently, one could substitute looser bounds that are currently used in the inner cores of constraint-posting scheduling algorithms, with the potential for great improvements in performance. What is needed to reduce the cost of computation is an algorithm, the measure of complexity of which is no greater than a low-degree polynomial in N (where N is the number of activities). The new algorithm satisfies this need. In this algorithm, the computation of resource-level envelopes is based on a novel combination of (1) the theory of shortest paths in the temporal-constraint network for the flexible plan and (2) the theory of maximum flows for a flow network derived from the temporal and resource constraints. The measure of asymptotic complexity of the algorithm is O(N O(maxflow(N)), where O(x) denotes an amount of computing time or a number of arithmetic operations proportional to a number of the order of x and O(maxflow(N)) is the measure of complexity (and thus of cost) of a maximumflow algorithm applied to an auxiliary flow network of 2N nodes. The algorithm is believed to be efficient in practice; experimental analysis shows the practical cost of maxflow to be as low as O(N1.5). The algorithm could be enhanced following at least two approaches. In the first approach, incremental subalgorithms for the computation of the envelope could be developed. By use of temporal scanning of the events in the temporal network, it may be possible to significantly reduce the size of the networks on which it is necessary to run the maximum-flow subalgorithm, thereby significantly reducing the time required for envelope calculation. In the second approach, the practical effectiveness of resource envelopes in the inner loops of search algorithms could be tested for multi-capacity resource scheduling. This testing would include inner-loop backtracking and termination tests and variable and value-ordering heuristics that exploit the properties of resource envelopes more directly.

Muscvettola, Nicola↗

NASA NTRS2009

Peer-to-Peer Planning for Space Mission Control

Planning and scheduling for space operations entails the development of applications that embed intimate domain knowledge of distinct areas of mission control, while allowing for significant collaboration among them. The separation is useful because of differences in the planning problem, solution methods, and frequencies of replanning that arise in the different disciplines. For example, planning the activities of human spaceflight crews requires some reasoning about all spacecraft resources at timescales of minutes or seconds, and is subject to considerable volatility. Detailed power planning requires managing the complex interplay of power consumption and production, involves very different classes of constraints and preferences, but once plans are generated they are relatively stable.

Barreiro, Javier↗

NASA NTRS2011

Solar Dynamics Observatory On-Orbit Jitter Testing, Analysis, and Mitigation Plans

The Solar Dynamics Observatory (SDO) was designed to understand the Sun and the Sun s influence on Earth. SDO was launched on February 11, 2010 carrying three scientific instruments: the Atmospheric Imaging Assembly (AIA), the Helioseismic and Magnetic Imager (HMI), and the Extreme Ultraviolet Variability Experiment (EVE). Both AIA and HMI are sensitive to high frequency pointing perturbations and have sub-arcsecond level line-of-sight (LOS) jitter requirements. Extensive modeling and analysis efforts were directed in estimating the amount of jitter disturbing the science instruments. To verify the disturbance models and to validate the jitter performance prior to launch, many jitter-critical components and subassemblies were tested either by the mechanism vendors or at the NASA Goddard Space Flight Center (GSFC). Although detailed analysis and assembly level tests were performed to obtain good jitter predictions, there were still several sources of uncertainties in the system. The structural finite element model did not have all the modes correlated to test data at high frequencies (greater than 50 Hz). The performance of the instrument stabilization system was not known exactly but was expected to be close to the analytical model. A true disturbance-to-LOS observatory level test was not available due to the tight schedule of the flight spacecraft, the cost in time and manpower, difficulties in creating gravity negation systems, and risks of damaging flight hardware. To protect the observatory jitter performance against model uncertainties, the SDO jitter team devised several on-orbit jitter reduction plans in addition to reserve margins on analysis results. Since some of these plans severely restricted the capabilities of several spacecraft components (e.g. wheels and High Gain Antennas), the SDO team performed on-orbit jitter tests to determine which jitter reduction plans, if any, were necessary to satisfy science LOS jitter requirements. The SDO on-orbit jitter tests were designed to satisfy the following four objectives: 1. Determine the acceptable reaction wheel operational speed range during Science Mode. 2. Determine HGA algorithm jitter parameters (number of stagger steps and enable/disable no-steprequests). 3. Determine acceptable EVE instrument filter wheels spin rates. 4. Determine if AIA instrument filter wheels excite the first AIA telescope structural mode. This paper provides detailed information on the SDO wheel jitter test plan, shows on-orbit jitter measurements and how ground predictions compare to those measurements, and describes the final jitter mitigation plan executed on SDO.

Liu, Kuo-Chia (Alice)↗

NASA NTRS2011

Supersonics/Airport Noise Plan: An Evolutionary Roadmap

This presentation discusses the Plan for the Airport Noise Tech Challenge Area of the Supersonics Project. It is given in the context of strategic planning exercises being done in other Projects to show the strategic aspects of the Airport Noise plan rather than detailed task lists. The essence of this strategic view is the decomposition of the research plan by Concept and by Tools. Tools (computational, experimental) is the description of the plan that resources (such as researchers) most readily identify with, while Concepts (here noise reduction technologies or aircraft configurations) is the aspects that project management and outside reviewers most appreciate as deliverables and milestones. By carefully cross-linking these so that Concepts are addressed sequentially (roughly one after another) by researchers developing/applying their Tools simultaneously (in parallel with one another), the researchers can deliver milestones at a reasonable pace while doing the longer-term development that most Tools in the aeroacoustics science require. An example of this simultaneous application of tools was given for the Concept of High Aspect Ratio Nozzles. The presentation concluded with a few ideas on how this strategic view could be applied to the Subsonic Fixed Wing Project's Quiet Aircraft Tech Challenge Area as it works through its current roadmapping exercise.

Bridges, James↗

NASA NTRS2005

Centralized Planning for Multiple Exploratory Robots

A computer program automatically generates plans for a group of robotic vehicles (rovers) engaged in geological exploration of terrain. The program rapidly generates multiple command sequences that can be executed simultaneously by the rovers. Starting from a set of high-level goals, the program creates a sequence of commands for each rover while respecting hardware constraints and limitations on resources of each rover and of hardware (e.g., a radio communication terminal) shared by all the rovers. First, a separate model of each rover is loaded into a centralized planning subprogram. The centralized planning software uses the models of the rovers plus an iterative repair algorithm to resolve conflicts posed by demands for resources and by constraints associated with the all the rovers and the shared hardware. During repair, heuristics are used to make planning decisions that will result in solutions that will be better and will be found faster than would otherwise be possible. In particular, techniques from prior solutions of the multiple-traveling- salesmen problem are used as heuristics to generate plans in which the paths taken by the rovers to assigned scientific targets are shorter than they would otherwise be.

Estlin, Tara↗

NASA NTRS2011

Single Event Effect (SEE) Test Planning 101

This is a course on SEE Test Plan development. It is an introductory discussion of the items that go into planning an SEE test that should complement the SEE test methodology used. Material will only cover heavy ion SEE testing and not proton, LASER, or other though many of the discussed items may be applicable. While standards and guidelines for how-to perform single event effects (SEE) testing have existed almost since the first cyclotron testing, guidance on the development of SEE test plans has not been as easy to find. In this section of the short course, we attempt to rectify this lack. We consider the approach outlined here as a "living" document: mission specific constraints and new technology related issues always need to be taken into account. We note that we will use the term "test planning" in the context of those items being included in a test plan.

LaBel, Kenneth A.↗

NASA NTRS2004

Controlling Distributed Planning

A system of software implements an extended version of an approach, denoted shared activity coordination (SHAC), to the interleaving of planning and the exchange of plan information among organizations devoted to different missions that normally communicate infrequently except that they need to collaborate on joint activities and/or the use of shared resources. SHAC enables the planning and scheduling systems of the organizations to coordinate by resolving conflicts while optimizing local planning solutions. The present software provides a framework for modeling and executing communication protocols for SHAC. Shared activities are represented in each interacting planning system to establish consensus on joint activities or to inform the other systems of consumption of a common resource or a change in a shared state. The representations of shared activities are extended to include information on (1) the role(s) of each participant, (2) permissions (defined as specifications of which participant controls what aspects of shared activities and scheduling thereof), and (3) constraints on the parameters of shared activities. Also defined in the software are protocols for changing roles, permissions, and constraints during the course of coordination and execution.

Clement, Bradley↗

NASA NTRS2003

The Technical Work Plan Tracking Tool

The Technical Work Plan Tracking Tool is a web-based application that enables interactive communication and approval of contract requirements that pertain to the administration of the Science, Engineering, Analysis, and Test (SEAT) contract at Johnson Space Center. The implementation of the application has (1) shortened the Technical Work Plan approval process, (2) facilitated writing and documenting requirements in a performance-based environment with associated surveillance plans, (3) simplified the contractor s estimate of the cost for the required work, and (4) allowed for the contractor to document how they plan to accomplish the work. The application is accessible to over 300 designated NASA and contractor employees via two Web sites. For each employee, the application regulates access according to the employee s authority to enter, view, and/or print out diverse information, including reports, work plans, purchase orders, and financial data. Advanced features of this application include on-line approval capability, automatic e-mail notifications requesting review by subsequent approvers, and security inside and outside the firewall.

Chullen, Cinda↗

NASA NTRS2012

Geometric Reasoning for Automated Planning

An important aspect of mission planning for NASA s operation of the International Space Station is the allocation and management of space for supplies and equipment. The Stowage, Configuration Analysis, and Operations Planning teams collaborate to perform the bulk of that planning. A Geometric Reasoning Engine is developed in a way that can be shared by the teams to optimize item placement in the context of crew planning. The ISS crew spends (at the time of this writing) a third or more of their time moving supplies and equipment around. Better logistical support and optimized packing could make a significant impact on operational efficiency of the ISS. Currently, computational geometry and motion planning do not focus specifically on the optimized orientation and placement of 3D objects based on multiple distance and containment preferences and constraints. The software performs reasoning about the manipulation of 3D solid models in order to maximize an objective function based on distance. It optimizes for 3D orientation and placement. Spatial placement optimization is a general problem and can be applied to object packing or asset relocation.

Clement, Bradley J.↗