Multi-layer Fuzzy Logic-based Expert System for Conducting Tier-scalable Planetary Reconnaissance

Introduction: Even though orbiting spacecraft and ground-based landers and rovers have successfully collected significant data through instrument suites, working hypotheses are yet to be confirmed. For example, in the case of Mars, such hypotheses include whether the mountain ranges contain a greater diversity of rock types than just volcanic, sites of suspected hydrothermal activity are indeed hydrothermal environments, or prime candidate sites of potential life-containing habitability [1] actually contain life. To effectively address this problem, a new scientific mission concept for remote planetary surface and subsurface reconnaissance recently has been devised [2]. The novel mission concept involves a paradigm shift from traditional missions, performing either local ground-level reconnaissance via rovers and immobile landers, or global mapping through orbiters, to what is termed " tier-scalable planetary reconnaissance " ; the new paradigm integrates a multi-tier (orbitÙatmosphereÙground) and multi-agent (orbiterÙblimpsÙrovers/landers/sensors) hierarchical mission architecture to enable unconstrained, science-driven planetary exploration [2]. The full-scale and optimal deployment of agents as part of a tier-scalable mission requires the design, implementation, and architecture integration of an intelligent reconnaissance system. Such a system should (1) include software packages that enable fully automated and comprehensive characterization of an operational area (e.g., Automated Geologic Field Analyzer (AGFA) [3]) and (2) integrate existing information with such AGFA-acquired, " in transit " spatial and temporal sensor data, to automatically perform smart planetary reconnaissance, such as identifying and homing in on prime candidate sites for potentially life-containing habitats on Mars. Methodology: Our goal is to define the framework for the design of an expert system (intelligent reconnaissance system) to be integrated into the tier-scalable mission architecture. Such a system is conceived to comprise two cascading subsystems capable of performing (1) and (2) respectively. Here we focus on defining an expert system capable of explicating functions as defined in (2). Since the tier-scalable approach is defined by the deployment of