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Artificial Intelligence at the Jet Propulsion Laboratory AI Magazine Volume 18 Number 1 (1997) (© AAAI) Articles Making an Impact Artificial Intelligence at the Jet Propulsion Laboratory Steve Chien, Dennis DeCoste, Richard Doyle, and Paul Stolorz ■ The National Aeronautics and Space Administra- described here is in the context of the remote- tion (NASA) is being challenged to perform more agent autonomy technology experiment that frequent and intensive space-exploration mis- will fly on the New Millennium Deep Space sions at greatly reduced cost. Nowhere is this One Mission in 1998 (a collaborative effort challenge more acute than among robotic plane- involving JPL and NASA Ames). Many of the tary exploration missions that the Jet Propulsion AI technologists who work at NASA expected Laboratory (JPL) conducts for NASA. This article describes recent and ongoing work on spacecraft to have the opportunity to build an intelli- autonomy and ground systems that builds on a gent spacecraft at some point in their careers; legacy of existing success at JPL applying AI tech- we are surprised and delighted that it has niques to challenging computational problems in come this early. planning and scheduling, real-time monitoring By the year 2000, we expect to demonstrate and control, scientific data analysis, and design NASA spacecraft possessing on-board automat- automation. ed goal-level closed-loop control in the plan- ning and scheduling of activities to achieve mission goals, maneuvering and pointing to execute these activities, and detecting and I research and technology development resolving of faults to continue the mission reached critical mass at the Jet Propul- without requiring ground support. At this Asion Laboratory (JPL) about five years point, mission accomplishment can begin to ago. In the last three years, the effort has become largely autonomous, and dramatic begun to bear fruit in the form of numerous cost savings can be achieved in the form of JPL and National Aeronautics and Space reduced, shared ground staffing that responds Administration (NASA) applications of AI on demand to beacon-based requests for inter- technology in the areas of planning and action originating from the spacecraft. Indeed, scheduling, real-time monitoring and control, a New Millennium Program Mission Opera- scientific data analysis, and design automa- tions Study estimated that remote-agent tech- tion. Such successes, described in detail in nology could reduce mission operations cost, this article, have also set the stage for JPL AI exclusive of data analysis, by as much as 60 researchers and technologists to seize an percent. unprecedented opportunity: the commitment By 2005, we expect that a significant por- by NASA to the development of software tion of the information routinely returned technology to realize highly autonomous from space platforms would not be raw data, space platforms. This strategic shift by NASA, and would not simply and strictly match fea- in which AI technology will play a central tures of stated prior interest, but would be and critical role, includes an important his- deemed by the on-board software to be inter- torically missing piece of the picture: the esting and worthy of further examination by availability of space missions whose primary scientists on the ground. At this point, limit- purpose is the validation of new technologies. ed-communications bandwidth would be used NASA’s New Millennium Program fills exactly in an extremely efficient fashion, and science this gap. Some of the most important work alerts from various and far-flung platforms Copyright © 1997, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1997 / $2.00 SPRING 1997 103 Articles would be anticipated with great interest. data from digitized photographic plates of The first steps toward realizing this vision the night sky collected at the Mt. Palomar are happening now and are described in the astronomical observatory-identifying and following paragraphs. Although the early goal classifying sky objects indexed into a compre- for autonomy technology is the reduction of hensive catalog containing approximately mission operations costs, the ultimate payoff three billion entries. SKICAT was able to classify will be the enabling of new mission classes extremely faint objects that were beyond the and the launching of a new era of solar sys- reach of expert-astronomer visual inspection. tem exploration, beyond reconnaissance. It was recently utilized to discover 16 new Spacecraft missions in this new era will be quasars, among the most distant objects in characterized by sustained presence and in- the universe. For the first time, astronomers depth scientific studies performed by free have an objective basis for conducting flyers, orbiters, and ground vehicles, some- unprecedented, large-scale cosmological stud- … mission times arrayed in constellations. Autonomy ies. SKICAT remains one of the most outstand- will be the central capability for enabling ing successes for machine-learning technolo- accomplish- long-term scientific studies of a decade or gy to date. This work was led by Usama ment can more, currently prohibited by cost, and Fayyad, in collaboration with George Djor- enabling new classes of missions that inher- govski of the Astronomy Department at the begin to ently must be executed without the benefit of California Institute of Technology (Caltech). become ground support, either because of control The MVP system (Chien and Mortensen challenges, for example, small-body (asteroid 1996) applied AI planning techniques to the largely and comet) rendezvous and landing missions problem of automatic software configuration autonomous, or because of the impossibility of communi- for image analysis. The VICAR set of image- and dramatic cation for extended periods, for example, an processing routines has been developed over underice explorer at Europa or a Titan aer- a period of many years at JPL. These routines cost savings obot. The vision for future NASA missions support image-processing steps such as can be based on intelligent space platforms is mosaicking and color-triplet reconstruction. tremendously exciting. Powerful but cumbersome to use, the VICAR achieved in The need for autonomy technology is routines support an essential processing the form of nowhere greater than in the set of deep space phase before true scientific image analysis can planetary missions that JPL conducts for begin. MVP is a front end for VICAR and reduced, NASA. The extreme remoteness of the targets, allows scientists and other users to simply shared ground the impossibility of hands-on troubleshoot- state their image-analysis goals. The system staffing that ing or maintenance, and the difficulties of automatically analyzes data dependencies light-time delayed communication (over four and other constraints and configures the responds on hours round trip to the outer solar system) all appropriate VICAR routines to achieve these demand to contribute to make JPL science missions the goals. MVP reduces the time to construct a focus of the development and application of typical VICAR job from 4 hours to 15 min- beacon-based autonomy technology (Doyle, Gor, et al. utes for expert users and from days to hours requests for 1997). JPL has been designated the lead NASA for novice users. The system is being used to center for spacecraft autonomy not only support the analysis of images being returned interaction because of the nature of its missions but also from the Galileo spacecraft during its current originating because of its unique combination of resident tour of the Jupiter planetary system. This from the expertise in AI, spacecraft engineering, space work was led by Steve Chien, in collaboration mission design, and systems engineering. with Helen Mortensen of JPL’s Multimission spacecraft. Not surprisingly, the imperative of cost Image-Processing Laboratory. constraints as drivers for the development of AI research and development activities at autonomy capabilities are balanced against JPL are conducted primarily by three research significant perceived risk in on-board uses of groups: (1) the Artificial Intelligence (AI) AI technology. An important ingredient in Group, led by Steve Chien, which focuses on making this opportunity credible and real are automated planning and scheduling and the previous successes at JPL in the applica- design automation; (2) the Machine Learning tions of AI. Two of the most notable of these Systems (MLS) Group, led by Paul Stolorz; successful applications are the sky-image cata- and (3) the Monitoring and Diagnosis Tech- loging and analysis tool (SKICAT) and the mul- nology (MDT) Group, led by Dennis DeCoste. timission VICAR (video image communica- The effort was once housed in the single AI tion and retrieval) planner (MVP). SKICAT group but has steadily grown: Its 30 to 40 (Fayyad, Djorgovski, and Weir 1996b) com- practitioners now make up 3 of the 9 groups pletely automated the process of reducing in JPL’s Information and Computing Tech- 104 AI MAGAZINE Articles nologies Research Section, led by Richard Planning, Scheduling, and Task Execu- Doyle. tion for Spacecraft Commanding In the remainder of the article, we describe Spacecraft command generation and valida- in greater detail the major AI projects at JPL. tion is an expensive and labor- and knowl- In particular, we describe efforts in the areas edge-intensive process. Enabling direct high- of planning and scheduling, monitoring and level commanding of spacecraft by diagnosis, knowledge discovery and data engineering and
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