Intelligent systems for the autonomous exploration of titan and enceladus

Roberto Furfaro, Jonathan I. Lunine, Jeffrey S. Kargel, Wolfgang Fink

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Future planetary exploration of the outer satellites of the Solar System will require higher levels of onboard automation, including autonomous determination of sites where the probability of significant scientific findings is highest. Generally, the level of needed automation is heavily influenced by the distance between Earth and the robotic explorer(s) (e.g. spacecraft(s), rover(s), and balloon(s)). Therefore, planning missions to the outer satellites mandates the analysis, design and integration within the mission architecture of semiand/or completely autonomous intelligence systems. Such systems should (1) include software packages that enable fully automated and comprehensive identification, characterization, and quantification of feature information within an operational region with subsequent target prioritization and selection for close-up reexamination; and (2) integrate existing information with acquired, "in transit" spatial and temporal sensor data to automatically perform intelligent planetary reconnaissance, which includes identification of sites with the highest potential to yield significant geological and astrobiological information. In this paper we review and compare some of the available Artificial Intelligence (AI) schemes and their adaptation to the problem of designing expert systems for onboard-based, autonomous science to be performed in the course of outer satellites exploration. More specifically, the fuzzy-logic framework proposed is analyzed in some details to show the effectiveness of such a scheme when applied to the problem of designing expert systems capable of identifying and further exploring regions on Titan and/or Enceladus that have the highest potential to yield evidence for past or present life. Based on available information (e.g., Cassini data), the current knowledge and understanding of Titan and Enceladus environments is evaluated to define a path for the design of a fuzzy-based system capable of reasoning over collected data and capable of providing the inference required to autonomously optimize future outer satellites explorations.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Volume6960
DOIs
StatePublished - 2008
EventSpace Exploration Technologies - Orlando, FL, United States
Duration: Mar 17 2008Mar 18 2008

Other

OtherSpace Exploration Technologies
CountryUnited States
CityOrlando, FL
Period3/17/083/18/08

Fingerprint

Enceladus
Titan
Intelligent systems
Satellites
expert systems
automation
Expert systems
Automation
mission planning
artificial intelligence
design analysis
reconnaissance
intelligence
space exploration
Solar system
Balloons
balloons
transit
robotics
inference

Keywords

  • Autonomy
  • Enceladus
  • Exploration of the solar system
  • Fuzzy expert systems
  • Planetary reconnaissance
  • Titan

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Furfaro, R., Lunine, J. I., Kargel, J. S., & Fink, W. (2008). Intelligent systems for the autonomous exploration of titan and enceladus. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 6960). [69600A] https://doi.org/10.1117/12.777643

Intelligent systems for the autonomous exploration of titan and enceladus. / Furfaro, Roberto; Lunine, Jonathan I.; Kargel, Jeffrey S.; Fink, Wolfgang.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6960 2008. 69600A.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Furfaro, R, Lunine, JI, Kargel, JS & Fink, W 2008, Intelligent systems for the autonomous exploration of titan and enceladus. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 6960, 69600A, Space Exploration Technologies, Orlando, FL, United States, 3/17/08. https://doi.org/10.1117/12.777643
Furfaro R, Lunine JI, Kargel JS, Fink W. Intelligent systems for the autonomous exploration of titan and enceladus. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6960. 2008. 69600A https://doi.org/10.1117/12.777643
Furfaro, Roberto ; Lunine, Jonathan I. ; Kargel, Jeffrey S. ; Fink, Wolfgang. / Intelligent systems for the autonomous exploration of titan and enceladus. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6960 2008.
@inproceedings{5fd8ef61a2464629994b99523cf89462,
title = "Intelligent systems for the autonomous exploration of titan and enceladus",
abstract = "Future planetary exploration of the outer satellites of the Solar System will require higher levels of onboard automation, including autonomous determination of sites where the probability of significant scientific findings is highest. Generally, the level of needed automation is heavily influenced by the distance between Earth and the robotic explorer(s) (e.g. spacecraft(s), rover(s), and balloon(s)). Therefore, planning missions to the outer satellites mandates the analysis, design and integration within the mission architecture of semiand/or completely autonomous intelligence systems. Such systems should (1) include software packages that enable fully automated and comprehensive identification, characterization, and quantification of feature information within an operational region with subsequent target prioritization and selection for close-up reexamination; and (2) integrate existing information with acquired, {"}in transit{"} spatial and temporal sensor data to automatically perform intelligent planetary reconnaissance, which includes identification of sites with the highest potential to yield significant geological and astrobiological information. In this paper we review and compare some of the available Artificial Intelligence (AI) schemes and their adaptation to the problem of designing expert systems for onboard-based, autonomous science to be performed in the course of outer satellites exploration. More specifically, the fuzzy-logic framework proposed is analyzed in some details to show the effectiveness of such a scheme when applied to the problem of designing expert systems capable of identifying and further exploring regions on Titan and/or Enceladus that have the highest potential to yield evidence for past or present life. Based on available information (e.g., Cassini data), the current knowledge and understanding of Titan and Enceladus environments is evaluated to define a path for the design of a fuzzy-based system capable of reasoning over collected data and capable of providing the inference required to autonomously optimize future outer satellites explorations.",
keywords = "Autonomy, Enceladus, Exploration of the solar system, Fuzzy expert systems, Planetary reconnaissance, Titan",
author = "Roberto Furfaro and Lunine, {Jonathan I.} and Kargel, {Jeffrey S.} and Wolfgang Fink",
year = "2008",
doi = "10.1117/12.777643",
language = "English (US)",
isbn = "9780819471512",
volume = "6960",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

}

TY - GEN

T1 - Intelligent systems for the autonomous exploration of titan and enceladus

AU - Furfaro, Roberto

AU - Lunine, Jonathan I.

AU - Kargel, Jeffrey S.

AU - Fink, Wolfgang

PY - 2008

Y1 - 2008

N2 - Future planetary exploration of the outer satellites of the Solar System will require higher levels of onboard automation, including autonomous determination of sites where the probability of significant scientific findings is highest. Generally, the level of needed automation is heavily influenced by the distance between Earth and the robotic explorer(s) (e.g. spacecraft(s), rover(s), and balloon(s)). Therefore, planning missions to the outer satellites mandates the analysis, design and integration within the mission architecture of semiand/or completely autonomous intelligence systems. Such systems should (1) include software packages that enable fully automated and comprehensive identification, characterization, and quantification of feature information within an operational region with subsequent target prioritization and selection for close-up reexamination; and (2) integrate existing information with acquired, "in transit" spatial and temporal sensor data to automatically perform intelligent planetary reconnaissance, which includes identification of sites with the highest potential to yield significant geological and astrobiological information. In this paper we review and compare some of the available Artificial Intelligence (AI) schemes and their adaptation to the problem of designing expert systems for onboard-based, autonomous science to be performed in the course of outer satellites exploration. More specifically, the fuzzy-logic framework proposed is analyzed in some details to show the effectiveness of such a scheme when applied to the problem of designing expert systems capable of identifying and further exploring regions on Titan and/or Enceladus that have the highest potential to yield evidence for past or present life. Based on available information (e.g., Cassini data), the current knowledge and understanding of Titan and Enceladus environments is evaluated to define a path for the design of a fuzzy-based system capable of reasoning over collected data and capable of providing the inference required to autonomously optimize future outer satellites explorations.

AB - Future planetary exploration of the outer satellites of the Solar System will require higher levels of onboard automation, including autonomous determination of sites where the probability of significant scientific findings is highest. Generally, the level of needed automation is heavily influenced by the distance between Earth and the robotic explorer(s) (e.g. spacecraft(s), rover(s), and balloon(s)). Therefore, planning missions to the outer satellites mandates the analysis, design and integration within the mission architecture of semiand/or completely autonomous intelligence systems. Such systems should (1) include software packages that enable fully automated and comprehensive identification, characterization, and quantification of feature information within an operational region with subsequent target prioritization and selection for close-up reexamination; and (2) integrate existing information with acquired, "in transit" spatial and temporal sensor data to automatically perform intelligent planetary reconnaissance, which includes identification of sites with the highest potential to yield significant geological and astrobiological information. In this paper we review and compare some of the available Artificial Intelligence (AI) schemes and their adaptation to the problem of designing expert systems for onboard-based, autonomous science to be performed in the course of outer satellites exploration. More specifically, the fuzzy-logic framework proposed is analyzed in some details to show the effectiveness of such a scheme when applied to the problem of designing expert systems capable of identifying and further exploring regions on Titan and/or Enceladus that have the highest potential to yield evidence for past or present life. Based on available information (e.g., Cassini data), the current knowledge and understanding of Titan and Enceladus environments is evaluated to define a path for the design of a fuzzy-based system capable of reasoning over collected data and capable of providing the inference required to autonomously optimize future outer satellites explorations.

KW - Autonomy

KW - Enceladus

KW - Exploration of the solar system

KW - Fuzzy expert systems

KW - Planetary reconnaissance

KW - Titan

UR - http://www.scopus.com/inward/record.url?scp=44349088746&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44349088746&partnerID=8YFLogxK

U2 - 10.1117/12.777643

DO - 10.1117/12.777643

M3 - Conference contribution

AN - SCOPUS:44349088746

SN - 9780819471512

VL - 6960

BT - Proceedings of SPIE - The International Society for Optical Engineering

ER -