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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 6960 |
| DOIs | |
| State | Published - 2008 |
| Event | Space Exploration Technologies - Orlando, FL, United States Duration: Mar 17 2008 → Mar 18 2008 |
Other
| Other | Space Exploration Technologies |
|---|---|
| Country | United States |
| City | Orlando, FL |
| Period | 3/17/08 → 3/18/08 |
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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
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 proceeding › Conference contribution
}
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 -