Search NASA⌕ Search

Engineering topics

Weisbin, Charles R.

Publications and source records attributed to Weisbin, Charles R..

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Titan Science Return Quantification

Each proposal for a NASA mission concept includes a Science Traceability Matrix (STM), intended to show that what is being proposed would contribute to satisfying one or more of the agency's top-level science goals. But the information traditionally provided cannot be used directly to quantitatively compare anticipated science return. We added numerical elements to NASA's STM and developed a software tool to process the data. We then applied this methodology to evaluate a group of competing concepts for a proposed mission to Saturn's moon, Titan.

Saturn↗
NASA NTRS2013

Mars Biosignature - Detection Capabilities: A Method for Objective Comparison of In Situ Measurements and Sample Return

A Mars sample-return mission has been proposed within NASA's Mars Exploration Program. Studying Martian samples in laboratories on Earth could address many important issues in planetary science, but arguably none is as scientifically compelling as the question of whether biosignatures indicative of past or present life exist on that planet. It is reasonable to ask before embarking on a sample-return mission whether equivalent investigation of Martian biosignatures could be conducted in situ. This study presents an approach to (1)identifying an optimal instrument suite for in situ detection of biosignatures on Mars,and (2)comparing the projected confidence level of in situ detection in a 2026 timeframe to that of Earth-based analysis. We identify a set of candidate instruments, the development of which is projected to be achievable by 2026 well within a $200 million cost cap. Assuming that any biosignatures near the surface of Mars are similar to those of terrestrial life, we find that this instrument suite, if successfully developed and deployed, would enable in situ biosignature detection at essentially the same level of confidence as that of Earth-based analysis of the same samples. At a cost cap of half that amount,the confidence level of in situbiosignature detection analysis could reach about 90% that of Earth-based investigations.

National Environmental Policy Act (NEPA)↗
NASA NTRS2012

HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

Hua, Hook↗
NASA NTRS2011

Reducing the Risk and Improving Mission Success for NASA's Human Mission to a Near-Earth Asteroid: How Many Robotic Surveyors?

NASA's recent attention and interest in sending a human mission to land on a Near-Earth asteroid raises the question of need for a robotic surveyor. This paper describes a Bayesian approach for comparing the productivity and cost-risk tradeoffs of sending (versus not sending) one or more robotic surveyor missions prior to a human mission to land on an asteroid. The probability of finding an asteroid suitable for landing was derived from an analysis of more than 1200 asteroids in order to define a quantitative estimate of suitability. The low cost of the surveyors relative to the human mission underlined the multi-surveyor strategy as relatively inexpensive insurance against the risks of encountering an unsuitable asteroid for landing on arrival by a human mission.

decision analysis↗
NASA NTRS2011

NASA's Human Mission to a Near-Earth Asteroid: Landing on a Moving Target

This paper describes a Bayesian approach for comparing the productivity and cost-risk tradeoffs of sending versus not sending one or more robotic surveyor missions prior to a human mission to land on an asteroid. The expected value of sample information based on productivity combined with parametric variations in the prior probability an asteroid might be found suitable for landing were used to assess the optimal number of spacecraft and asteroids to survey. The analysis supports the value of surveyor missions to asteroids and indicates one launch with two spacecraft going simultaneously to two independent asteroids appears optimal.

precursor decision making↗
NASA NTRS2010

Reconciling Scientific Aspirations and Engineering Constraints for a Lunar Mission via Hyperdimensional Interpolation

Virtually every NASA space-exploration mission represents a compromise between the interests of two expert, dedicated, but very different communities: scientists, who want to go quickly to the places that interest them most and spend as much time there as possible conducting sophisticated experiments, and the engineers and designers charged with maximizing the probability that a given mission will be successful and cost-effective. Recent work at NASA's Jet Propulsion Laboratory (JPL) seeks to enhance communication between these two groups, and to help them reconcile their interests, by developing advanced modeling capabilities with which they can analyze the achievement of science goals and objectives against engineering design and operational constraints. The analyses conducted prior to this study have been point-design driven. Each analysis has been of one hypothetical case which addresses the question: Given a set of constraints, how much science can be done? But the constraints imposed by the architecture team-e.g., rover speed, time allowed for extravehicular activity (EVA), number of sites at which science experiments are to be conducted- are all in early development and carry a great deal of uncertainty. Variations can be incorporated into the analysis, and indeed that has been done in sensitivity studies designed to see which constraint variations have the greatest impact on results. But if a very large number of variations can be analyzed all at once, producing a table that includes virtually the entire trade space under consideration, then we have a tool that enables scientists and mission architects to ask the inverse question: For a given desired level of science (or any other objective), what is the range of constraints that would be needed? With this tool, mission architects could determine, for example, what combinations of rover speed, EVA duration, and other constraints produce the desired results. Further, this tool would help them identify which technology-improvement investments would be likely to produce the largest or most important return. However, the number of variations that need to be considered for such analysis quickly balloons to an unwieldy size. If three variations are considered for each of six constraints-a very modest example-there are a total of 243 variations to consider. If it takes 40 minutes to compute each variation, as it does with HURON, our automated optimization system, then it would take 162 hours or nearly 7 days of round-the-clock computing to calculate the results. Adding further constraints or variations exponentially increases the amount of time that is needed.

Weisbin, Charles R.↗
NASA NTRS2009

Spreadsheets for Analyzing and Optimizing Space Missions

XCALIBR (XML Capability Analysis LIBRary) is a set of Extensible Markup Language (XML) database and spreadsheet- based analysis software tools designed to assist in technology-return-on-investment analysis and optimization of technology portfolios pertaining to outer-space missions. XCALIBR is also being examined for use in planning, tracking, and documentation of projects. An XCALIBR database contains information on mission requirements and technological capabilities, which are related by use of an XML taxonomy. XCALIBR incorporates a standardized interface for exporting data and analysis templates to an Excel spreadsheet. Unique features of XCALIBR include the following: It is inherently hierarchical by virtue of its XML basis. The XML taxonomy codifies a comprehensive data structure and data dictionary that includes performance metrics for spacecraft, sensors, and spacecraft systems other than sensors. The taxonomy contains >700 nodes representing all levels, from system through subsystem to individual parts. All entries are searchable and machine readable. There is an intuitive Web-based user interface. The software automatically matches technologies to mission requirements. The software automatically generates, and makes the required entries in, an Excel return-on-investment analysis software tool. The results of an analysis are presented in both tabular and graphical displays.

Some, Raphael R.↗
NASA NTRS2006

Technology Assessment in Support of the Presidential Vision for Space Exploration

This paper discusses the process and results of technology assessment in support of the United States Vision for Space Exploration of the Moon, Mars and Beyond. The paper begins by reviewing the Presidential Vision: a major endeavor in building systems of systems. It discusses why we wish to return to the Moon, and the exploration architecture for getting there safely, sustaining a presence, and safely returning. Next, a methodology for optimal technology investment is proposed with discussion of inputs including a capability hierarchy, mission importance weightings, available resource profiles as a function of time, likelihoods of development success, and an objective function. A temporal optimization formulation is offered, and the investment recommendations presented along with sensitivity analyses. Key questions addressed are sensitivity of budget allocations to cost uncertainties, reduction in available budget levels, and shifting funding within constraints imposed by mission timeline.

technology↗
NASA NTRS2006

Temporal Investment Strategy to Enable JPL Future Space Missions

The Jet Propulsion Laboratory (JPL) formulates and conducts deep space missions for NASA (the National Aeronautics and Space Administration). The Chief Technologist of JPL has the responsibility for strategic planning of the laboratory's advanced technology program to assure that the required technological capabilities to enable future JPL deep space missions are ready as needed; as such he is responsible for the development of a Strategic Plan. As part of the planning effort, he has supported the development of a structured approach to technology prioritization based upon the work of the START (Strategic Assessment of Risk and Technology) team. A major innovation reported here is the addition of a temporal model that supports scheduling of technology development as a function of time. The JPL Strategic Technology Plan divides the required capabilities into 13 strategic themes. The results reported here represent the analysis of an initial seven.

investment strategy↗
NASA NTRS2006

Postoptimality Analysis in the Selection of Technology Portfolios

This slide presentation reviews a process of postoptimally analysing the selection of technology portfolios. The rationale for the analysis stems from the need for consistent, transparent and auditable decision making processes and tools. The methodology is used to assure that project investments are selected through an optimization of net mission value. The main intent of the analysis is to gauge the degree of confidence in the optimal solution and to provide the decision maker with an array of viable selection alternatives which take into account input uncertainties and possibly satisfy non-technical constraints. A few examples of the analysis are reviewed. The goal of the postoptimality study is to enhance and improve the decision-making process by providing additional qualifications and substitutes to the optimal solution.

prioritization↗
NASA NTRS2006

Postoptimality analysis in the selection of technology portfolios

This paper describes an approach for qualifying optimal technology portfolios obtained with a multi-attribute decision support system. The goal is twofold: to gauge the degree of confidence in the optimal solution and to provide the decision-maker with an array of viable selection alternatives, which take into account input uncertainties and possibly satisfy non-technical constraints.

portfolios↗
NASA NTRS2006

Capability Investment Strategy to Enable JPL Future Space Missions

The Jet Propulsion Laboratory (JPL) formulates and conducts deep space missions for NASA (the National Aeronautics and Space Administration). The Chief Technologist of JPL has responsibility for strategic planning of the laboratory's advanced technology program to assure that the required technological capabilities to enable future missions are ready as needed. The responsibilities include development of a Strategic Plan (Antonsson, E., 2005). As part of the planning effort, a structured approach to technology prioritization, based upon the work of the START (Strategic Assessment of Risk and Technology) (Weisbin, C.R., 2004) team, was developed. The purpose of this paper is to describe this approach and present its current status relative to the JPL technology investment.

investment strategies↗
NASA NTRS2006

START Analysis for ESAS Capability Needs Prioritization

START is a tool to optimize research and development primarily for NASA missions. It was developed within the Strategic Systems Technology Program Office, a division of the Office of the Chief Technologist at NASA's Jet Propulsion Laboratory. START is capable of quantifying and comparing the risks, costs, and potential returns of technologies that are candidates for funding. START can be enormously helpful both in selecting technologies for development -- within the constraints of budget, schedule, and other resources -- and in monitoring their progress. START's methods are applicable to everything from individual tasks to multiple projects comprising entire programs of investigation. They can address virtually any technology assessment and capability prioritization issue. In this report, START is used to analyze the capability needs using data from NASA's Exploration Systems Architecture Study (ESAS).

optimization↗
NASA NTRS2004

Toward a Systematic Approach for Selection of NASA Technology Portfolios

There is an important need for a consistent analytical foundation supporting the selection and monitoring of R&D tasks that support new system concepts that enable future NASA missions. This capability should be applicable at various degrees of abstraction, depending upon whether one is interested in formulation, development, or operations. It should also be applicable to a single project, a program comprised of a group of projects, an enterprise typically including multiple programs, and the overall agency itself. Emphasis here is on technology selection and new initiatives, but the same approach can be generalized to other applications, dealing, for example, with new system architectures, risk reduction, and task allocation among humans and machines. The purpose of this paper is to describe one such approach, which is in its early stages of implementation within NASA programs, and to discuss several illustrative examples.

return on investment↗
NASA NTRS1998

Rover and Telerobotics Technology Program

The Jet Propulsion Laboratory's (JPL's) Rover and Telerobotics Technology Program, sponsored by the National Aeronautics and Space Administration (NASA), responds to opportunities presented by NASA space missions and systems, and seeds commerical applications of the emerging robotics technology. The scope of the JPL Rover and Telerobotics Technology Program comprises three major segments of activity: NASA robotic systems for planetary exploration, robotic technology and terrestrial spin-offs, and technology for non-NASA sponsors. Significant technical achievements have been reached in each of these areas, including complete telerobotic system prototypes that have built and tested in realistic scenarios relevant to prospective users. In addition, the program has conducted complementary basic research and created innovative technology and terrestrial applications, as well as enabled a variety of commercial spin-offs.

Weisbin, Charles R.↗