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Autonomous Sciencecraft Experiment


Title Image

National Aeronautics and Space Administration

Background

Since the dawn of the space age, unmanned spacecraft have flown blind with little or no ability to make autonomous decisions based on the content of the data they collect. The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Earth Orbiter 1 mission in 2003. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. This software will demonstrate the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the highest value science data.

ASE Concept Image

AI Technology

The ASE onboard flight software includes several autonomy software components:

Onboard science algorithms
that will analyze the image data to detect trigger conditions such as science events,interesting features, changes relative to previous observations, and cloud detection for onboard image editing
Robust execution management software
using the Spacecraft Command Language (SCL) package to enable event-driven processing and low-level autonomy
Continuous Activity Scheduling Planning Execution and Replanning (CASPER) software
that will replan activities, including downlink, based on science observations in the previous orbit cycles
Europa Ice
Tracking Europa Surface Ice

The onboard science algorithms will analyze the images to extract static features and detect changes relative to previous observations. Prototype software has already been demonstrated on EO-1 Hyperion data to automatically identify regions of interest including regions of change (such as flooding, ice melt, and lava flows). Using these algorithms onboard will enable retargeting and search, e.g., retargeting the instrument on a subsequent orbit cycle to identify and capture the full extent of a flood. On future interplanetary space missions, onboard science analysis will enable capture of short-lived science phenomena at the finest time-scales without overwhelming onboard memory or downlink capacities. Examples include: eruption of volcanoes on Io, formation of jets on comets, and phase transitions in ring systems. Generation of derived science products (e.g., boundary descriptions, catalogs) and change-based triggering will also reduce data volumes to a manageable level for extended duration missions that study long-term phenomena such as atmospheric changes at Jupiter and flexing and cracking of the ice crust on Europa.

The onboard planner (CASPER) will generate mission operations plans from goals provided by the onboard science analysis module. The model-based planning algorithms will enable rapid response to a wide range of operations scenarios based on a deep model of spacecraft constraints, including faster recovery from spacecraft anomalies. The onboard planner will accept as inputs the science and engineering goals and ensure high-level goal-oriented behavior.

The robust execution system (SCL) accepts the CASPER-derived plan as an input and expands the plan into low-level commands. SCL monitors the execution of the plan and has the flexibility and knowledge to perform event-driven commanding to enable local improvements in execution as well as local responses to anomalies.


Problem

Constrained downlink resources limit the science return of current and future space missions.

Impact

Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology. This technology would:

IO Volcano
Short-Lived Eruption on Io

  • Dramatically increase the science per fixed downlink by enabling downlink of the highest priority science data.
  • Enable study of short-lived science events (such as volanic eruptions, dust storms, etc.)
  • Reduce downtime lost to anomalies due to robust execution enabled by autonomy software.
  • Reduce instrument setup time by using autonomy software take advantage of execution information to streamline operations.

Status

The following table lists the tests performed onboard the EO-1 satellite to validate the ASE.

2003.03.22 First test of onboard cloud detection
2003.05.23 First test of onboard SCL execution software
2003.07.21 First test of automated ground developed plans running onboard EO1 (Yukon River)
2003.07.27 Second test of automated ground developed plans running onboard EO1 (Fire sensorweb)
2003.07.31 Third test of automated ground developed plans running onboard EO1 (Ganda Angola sensorweb)
2003.08.13 Test of decompression software onboard
2003.08.13 Fourth test of automated ground developed plans running onboard EO1 (Montana fire sensorweb)
2003.10.29 ASE commanded the instrument (hyperion) covers to open/close, then to perform a dark calibration, then an xband downlink
2003.11.19 ASE commanded the instruments to acquire an image - the CPU performed a reset
2004.01.08 ASE commanded the instruments to acquire an image, then downlink that image; 10% validation achieved
2004.01.14 ASE commanded the instruments to acquire an image, then downlink that image; 20% validation achieved
2004.01.22 ASE commanded the instruments to acquire an image of Great Sand Dunes, then downlink that image; 30% validation achieved
2004.01.22 ASE commanded the instruments to acquire an image of Lake Monona (Wisconsin), then downlink that image; 40% validation achieved
2004.01.29 ASE commanded the instruments to acquire an image of Kokee State Park, Kauai, Hawaii, then downlink that image; 50% validation achieved
2004.02.20 ASE commanded the instruments to acquire an image of Winnibigoshish and Leech Lakes then downlink that image, (sensorweb, ASPEN on ground chose which image based on GOES cloud predict)
2004.02.25 ASE commanded the instruments to acquire an image of Upper/Lower Red Lakes then downlink that image, (sensorweb, ASPEN on ground chose which image based on GOES cloud predict)
2004.05.07 ASE commanded the instruments to acquire an image of Mount Erebus, analyzed the image for thermal activity, detected an unusually high number of hot pixels, and triggered another image of Mount Erebus.
2004.05.14 ASE completed 5th complete cycle of collecting data, analyzing the data, and triggering a response; 100% validation achieved.
Proceeding towards full week of autonomous EO-1 operations.

Description

A typical ASE demonstration scenario involves monitoring of active volcano regions such as Mt. Etna in Italy. Hyperion data have been used in ground-based analysis to study this phenomenon. The ASE concept will be applied as follows:

ASE Mission Concept Animation
+ ASE Mission Concept Animation
  1. Initially, ASE has a list of science targets to monitor that have been sent as high-level goals from the ground.
  2. As part of normal operations, CASPER generates a plan to monitor the targets on this list by periodically imaging them with the Hyperion instrument. For volcanic studies, the IR and near IR bands are used.
  3. During execution of this plan, the EO-1 spacecraft images Mt. Etna with the Hyperion instrument.
  4. The onboard science algorithms analyze the image and detect a fresh lava flow. Based on this detection the image is downlinked. Had no new lava flow been detected, the science software would generate a goal for the planner to acquire the next highest priority target in the list of targets. The addition of this goal to the current goal set triggers CASPER to modify the current operations plan to include numerous new activities in order to enable the new science observation.
  5. The SCL software executes the CASPER generated plans in conjunction with several autonomy elements.
  6. This cycle is then repeated on subsequent observations.

Project Team

JPL: Steve Chien
Rob Sherwood
Becky Castano
Ashley Davies
Gregg Rabideau
Daniel Tran
Ben Cichy
Nghia Tang
Rachel Lee
Russell Knight
Steve Schaffer
NASA Goddard Space Flight Center: Dan Mandl
Stuart Frye
Seth Shulman
Rob Bote
Interface and Control Systems: Darrell Boyer
Jim VanGaasbeck
Microtel: Bruce Trout
Nick Hengemihle
Jerry Hengemihle
Hammers: Jeff D'Agostino
Kathie Blackman
Arizona State University: Ronald Greeley
Thomas Doggett
University of Arizona: Victor Baker
James Dohm
Felipe Ip
Center for Earth and Planetary Studies
National Air and Space Museum
Smithsonian Institute:
Kevin Williams

Sponsors

+ New Millennium Program

For additional information, please visit the ASE project home at http://asc.jpl.nasa.gov.

Page content courtesy of NASA



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