Intelligent systems for the autonomous exploration of Titan and Enceladus

Fuzzy-logic-based systems enable robots exploring the outer solar system to perform scientific tasks with almost no human instruction.
17 February 2008
Roberto Furfaro, Jonathan Lunine, Jeffrey Kargel, and Wolfgang Fink

Autonomous robots are those that can perform desired tasks without continuous human guidance. They will play a critical role in the future scientific exploration of challenging planetary environments, such as those found in the outer solar system, like moons Titan, Enceladus, and Europa.

These planetary bodies have a high potential of yielding significant geological and possibly astrobiological information, and have lately received a great deal of attention from NASA, the European Space Agency (ESA), and other space agencies. Indeed, the Outer Planet Assessment Group (OPAG) was established by NASA in 2004 to identify scientific priorities and pathways for exploration in the outer solar system.1 This group advocates large-effort flagship missions, designed to optimize the overall science return and to increase understanding of the outer solar planets. The full-scale, optimal deployment of flagship mission or tier-scalable reconnaissance mission agents such as orbiters, balloons, and landers, requires the design, implementation, and integration of an intelligent reconnaissance system.2,3 Such a system should enable fully automated and comprehensive characterization of an operational area, as well as integrate existing information with acquired, ‘in transit’ spatial and temporal sensor data. The aim is to identify and home in on prime candidate locales, including those with the greatest chance of containing life.

Figure 1. Radar image obtained by Cassini's on-board radar instrument during a near-polar flyby (13 May 2007). As with other bodies of liquid seen on Titan by Cassini, we can observe channels, islands, bays, and other forms typical of terrestrial coastlines. The liquid, which is most likely a combination of methane and ethane, appears very dark to the radar. Photo courtesy of NASA/JPL.

Figure 2. Intelligent reconnaissance system embedded on a hot-air balloon deployed for Titan exploration. Data are acquired while in transit from the on-board instruments. After preprocessing and categorization, the appropriate indicators are fed to a fuzzy-logic-based expert system, which assesses the potential for scientific findings. The system interacts with lower-level controls to implement the desired course of action, based on the fuzzy assessment. Balloon artistic image courtesy of Tibor Balint, NASA/JPL.

To address such open questions, we have been working on a systematic set of fuzzy-logic-based expert systems capable of evaluating geologic and any astrobiological information acquired during the course of automated reconnaissance missions in the outer solar system. We believe these systems to be a straightforward and effective way to execute artificial reasoning for autonomous and intelligent real-time science.

Planetary exploration relies on understanding the history of celestial bodies through remote and in situ data collection. Discoveries are made using newly acquired data to infer new and unknown facts. They also test working hypotheses and help formulate new ones. Fuzzy logic provides an ideal framework for handling multiple layers of information of varying degrees of confidence, such as elevated methane content, low sulfate levels, or a medium number of valley networks. The absolute values of the input data can then be transformed into fuzzy values and incorporated into rules.

While our team has been tackling a broad range of fuzzy systems3–5 and conceiving architectures for celestial bodies with very different geologic histories (e.g., Mars and Enceladus), we have recently shifted our focus toward Titan. The sixth Saturnian moon is characterized by an extremely rich and complex environment. Cassini radar observations and the Huygens descent/landing probe unveiled apparent methane lakes, coastlines, and riverbeds (see Figure 1).6,7

One such environmental feature, Titan's thick atmosphere, is of special interest, and a flagship mission to deploy an exploratory hot-air balloon is under investigation. However, because of the moon's distance, real-time communication from Earth with the balloon is impossible, making autonomous navigation and reasoning imperative. In a highly automated scenario, the hot-air balloon would ideally be able to ingest multiple layers of information (e.g., elemental, stratigraphic, topographic, thermal, atmospheric, spectral) and independently assess the potential of the observed locale to yield significant information.

Figure 2 shows how an intelligent reconnaissance system might be integrated into a baseline balloon architecture, assumed to be used on Titan. First, the acquired data would be categorized and preprocessed via embedded software packages, such as the Automated Global Feature Analyzer.8 This would provide the numerical values for the indicators appropriate to scientific inquiries, which could be fed to the expert fuzzy system. The system would then process the information and assess the potential for features like fluvial activity, cryovolcanism, and local habitability. The fuzzy expert system would interact with the lower-level controls and could command the balloon to take appropriate actions, for example, initiating a descent for closer surface examination,9 station keeping,10 or even collecting surface samples. Figure 2 also shows the elements forming the backbone of the fuzzy expert system. The core, called the knowledge base, comprises membership functions and fuzzy rules. These rules are linguistic statements that condense expertise, methods, and skills derived from years of investigation.

For our design and simulation, we employ Mamdani-type IF-THEN rules.11 The fuzzy inference machine defines the process of formulating maps, based on the given data, and uses the rules, (fuzzy) data, and observations to infer new facts. Finally, a user interface (explanation module) is required to explain why and how the solution has been reached. The module is used by humans to remotely monitor the operations of the system. Details of the design, implementation, and simulation for this and other scenarios can be found elsewhere.3–5,9

Roberto Furfaro
Department of Aerospace and Mechanical Engineering
The University of Arizona
Tucson, AZ

Roberto Furfaro is currently assistant research professor. He graduated with a ‘laurea’ degree in aeronautical engineering (MS equivalent) from the University of Rome in 1998 and with a PhD in aerospace engineering from the University of Arizona in 2004. He has a broad spectrum of research interests, including neutron and photon computational transport for remote-sensing applications, neural and fuzzy systems, and nonlinear control of space-based systems. He has been interested in space exploration since 1998, when he joined the NASA Space Engineering Research Center at the University of Arizona to become the project manager for the development of two robotic devices designed to use Martian local resources. Recently, he has been working on developing novel engineering solutions for planetary exploration, including fuzzy-logic-based expert systems for autonomous life-searching in extraterrestrial bodies.

Jonathan Lunine
Lunar and Planetary Lab
The University of Arizona
Tucson, AZ

Jonathan I. Lunine is professor of planetary sciences and of physics at the University of Arizona. He is a distinguished visiting scientist at NASA's Jet Propulsion Laboratory, where he serves as a member of the Director's Advisory Council. His research interests include the evolution of giant planets and brown dwarf stars, the formation of planets, the evolution of Titan's atmosphere and surface processes, and organic chemistry leading to the origin of life. Lunine is an interdisciplinary scientist on the Cassini mission to Saturn and on the James Webb (Next-Generation) Space Telescope. He is a coinvestigator on the Juno mission under development for a launch to Jupiter, and on a Spitzer Space Telescope team investigating the evolution of planet-forming disks. He is a fellow of the American Geophysical Union and the American Association for the Advancement of Science, and an elected member of the International Academy of Astronautics. He earned a BS in physics and astronomy from the University of Rochester in 1980, followed by MS (1983) and PhD (1985) degrees in planetary science from the California Institute of Technology.

Jeffrey Kargel
Hydrology and Water Resources
The University of Arizona
Tucson, AZ

Jeffrey S. Kargel, an adjunct professor, is a geologist (BS, MS, geological sciences, Ohio State University) and planetary scientist (PhD, 1990, planetary sciences, University of Arizona) and an expert in cryospheric systems (glaciers and permafrost). He is the principle investigator of Global Land Ice Measurements from Space and leads or participates in a variety of planetary research projects. He has authored or coauthored over 70 peer-reviewed papers, including several on the topic of asteroid and Mars resources.

Wolfgang Fink
Visual and Autonomous Exploration Systems Research Laboratory
California Institute of Technology
Pasadena, CA

Wolfgang Fink is a senior researcher at NASA's Jet Propulsion Laboratory in Pasadena, a research associate professor of both ophthalmology and neurological surgery at the University of Southern California, and a visiting associate in physics at the California Institute of Technology. He is the founder and head of the Visual and Autonomous Exploration Systems Research Laboratory at Caltech. His research interests include autonomous planetary and space exploration, computational field geology, computer optimization, image processing and analysis, sensor data fusion, astrobiology, and biomedicine. He obtained BS and MS degrees in physics and physical chemistry from the University of Göttingen, Germany, and a PhD in theoretical physics from the University of Tübingen in 1997. His work is documented in numerous publications and patents. He also holds a commercial pilot's license for rotorcraft.

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