Richard J. Doyle

Spacecraft autonomy and the missions of exploration

All practitioners are playing a critical role in developing NASAs next generation of flight software. The goal is spacecraft autonomy. If we achieve this key ingredient of space explorations next phase, NASA can have many more space platforms operating at once, make more effective use of limited communications resources and attempt bolder mission concepts involving direct investigation of remote environments.

Sensor selection techniques in device monitoring

For complex systems with large sensor complements, the authors develop a selective monitoring strategy to avoid information overload on system operators. They describe an approach to determining from moment to moment which subset of the available sensor data for a system is most informative about the state of the system and about interactions occurring within the system. They term this process sensor selection. The approach draws on concepts from causal reasoning and information theory. After describing the NASA test domain, they conclude with a report on the status of the implementation of the SELMON system.<<ETX>>

Ice & Fire: Missions to the most difficult solar system destinations… on a budget

Abstract Three radii from the surface of the Sun… more natural radiation around Jupiter than would be encountered immediately following a nuclear war… to the farthest planet and beyond… these challenges are faced by the three “Ice & Fire” missions: Solar Probe, Europa Orbiter, and PlutoKuiper Express. These three missions will be beneficiaries of the X2000 and related advanced technology development programs. Technology developments now in progress make these missions achievable at costs recently thought adequate only for missions of relatively short durations to “nearby” destinations. The next mission to Europa after Galileo will determine whether a global subsurface liquid water ocean is currently present, and will identify locations where the ocean, if it exists, may be most accessible to future missions. Pluto-Kuiper Express will complete the reconnaissance of the known planets in our Solar System with geological, compositional, and atmospheric mapping of Pluto and Charon while Pluto remains relatively near the Sun during its 248 year orbit. An extended mission to a Kuiper Disk object may be possible, depending on remaining sciencecraft resources. Using a unique combination of Sun shield/high gain antenna and quadrature encounter geometry, Solar Probe will deeply penetrate our nearest stars atmosphere to make local measurements of the birth of solar wind, and to remotely image features as small as 60 kilometers across on the Suns surface. Avionics technology, leading to integration of functions among a set of multichip modules with standard interfaces, will enable lower production costs, lower power and mass, and the ability to package with modest shielding to enable survival in orbit around Europa inside Jupiters intense radiation belts. The same avionics and software can be utilized on the other Ice & Fire missions. Each mission is characterized by a long cruise to its destination, facilitated by planetary flybys. The flight systems will represent a unique early integration of science “payload” and “spacecraft,” becoming a more integrated “sciencecraft.” To reduce operations and tracking costs, sciencecraft will be more autonomous. They will self-monitor and self-command, while sending a continuous beacon alerting ground receivers to general sciencecraft health and any need for immediate attention. Where solar power proves impractical for achieving mission goals, an advanced radioisotope power source may be utilized with a much smaller amount of fuel than on prior missions. The three missions described are to begin the Outer Planets/Solar Probe exploration program, as first proposed in the FY1998 Federal Budget. Sciencecraft, launch systems and mission operations must all fit within a single program, encouraging system- and program-wide tradeoffs to minimize costs. Some of the system and technological solutions utilized by these missions may find application in a variety of other science-driven missions.

Applying machine learning classification techniques to automate sky object cataloguing

We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion.Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and t...

Selective simulation and selective sensor interpretation in monitoring

When a system to be monitored exhibits a wide range of behavior and contains a large number of sensors, the ability to predict events and verify sensor data---whether by humans, machines, or some combination---becomes overwhelmed. A monitoring strategy must take into account the reality that in a complex system not all of the sensor channels can be interpreted all of the time. We describe an approach to selective processing in monitoring---only part of a system model is simulated and only a subset of the available sensors are interpreted at any given time. This approach is designed to provide informative feedback on whether a system is performing nominally in the current operating context without exceeding available resources for prediction and interpretation.

PIPE: An intelligent scientific data preparation assistant

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g., spectral densities or running averages).This paper describes the PIPE system. PIPE provides intelligent assistance developing scientific data preparation plans developed using Master Plumber, a general language for scientific data processing plans. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developers’ attention upon tho...

Inspiration for Future Autonomous Space Systems

NASA is embarking on a new phase of space exploration. In the solar system, an initial reconnaissance of all of the planets except Pluto has been accomplished. In the next phase of planetary exploration, the emphasis will be on direct, i.e., in-situ scientific investigation in the remote environments. In the next phase of astrophysics investigation, the emphasis is on new observing instruments, often based on principles of interferometry, to accomplish unprecedented resolution in remote observing. A theme that runs through all of these science missions is the search for life.