Autonomy will play a key role in future science-driven, tier-scalable robotic planetary reconnaissance to extremely challenging (by existing means), locales on Mars and elsewhere that have the potential to yield significant geological and possibly exobiologic information. The full-scale and optimal deployment of the agents employed by tier-scalable architectures requires the design, implementation, and integration of an intelligent reconnaissance system. Such a system should be designed to enable fully automated and comprehensive characterization of an operational area, as well as to integrate existing information with acquired, ‘‘in transit’’ spatial and temporal sensor data, to identify and home in on prime candidate locales. These may include locales with the greatest potential of containing life. Founded on the premise that water and energy are key to life, we have designed a fuzzy system that can (1) acquire the appropriate past/present water/energy indicators while the tier-scalable mission architecture is deployed (first layer), and (2) evaluate habitability through a specialized fuzzy knowledge-base of the water and energy information (second layer) acquired in (1). The system has been tested through hypothetical deployments at two hypothesized regions on Mars. The fuzzy-based expert’s simulation results corroborate the same conclusions provided by the human expert, and thus highlight the system’s potential capability to effectively and autonomously reason as an interdisciplinary scientist in the quest for life. While the approach is demonstrated for Mars, the methodology is general enough to be extended to other planetary bodies. It can be readily modified and updated as our interdisciplinary understanding of planetary environments improves. We believe this work represents a foundational step toward implementing higher-level intelligence in robotic, tier-scalable planetary reconnaissance within and beyond the solar system.

The search for life beyond earth through fuzzy expert systems.

KOMATSU, Goro
2008-01-01

Abstract

Autonomy will play a key role in future science-driven, tier-scalable robotic planetary reconnaissance to extremely challenging (by existing means), locales on Mars and elsewhere that have the potential to yield significant geological and possibly exobiologic information. The full-scale and optimal deployment of the agents employed by tier-scalable architectures requires the design, implementation, and integration of an intelligent reconnaissance system. Such a system should be designed to enable fully automated and comprehensive characterization of an operational area, as well as to integrate existing information with acquired, ‘‘in transit’’ spatial and temporal sensor data, to identify and home in on prime candidate locales. These may include locales with the greatest potential of containing life. Founded on the premise that water and energy are key to life, we have designed a fuzzy system that can (1) acquire the appropriate past/present water/energy indicators while the tier-scalable mission architecture is deployed (first layer), and (2) evaluate habitability through a specialized fuzzy knowledge-base of the water and energy information (second layer) acquired in (1). The system has been tested through hypothetical deployments at two hypothesized regions on Mars. The fuzzy-based expert’s simulation results corroborate the same conclusions provided by the human expert, and thus highlight the system’s potential capability to effectively and autonomously reason as an interdisciplinary scientist in the quest for life. While the approach is demonstrated for Mars, the methodology is general enough to be extended to other planetary bodies. It can be readily modified and updated as our interdisciplinary understanding of planetary environments improves. We believe this work represents a foundational step toward implementing higher-level intelligence in robotic, tier-scalable planetary reconnaissance within and beyond the solar system.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/218413
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