Engineering topics
Publications and source records attributed to Castano, R..
We describe ongoing efforts to integrate and coordinate space and marine assets to enable autonomous response to dynamic ocean phenomena such as algal blooms, eddies, and currents. Thus far we have focused on the use of remote sensing assets (e.g. satellites) but future plans include expansions to use a range of in-situ sensors such as gliders, autonomous underwater vehicles, and buoys/moorings.
The cratered plains of Gusev traversed by Spirit are generally low-relief rocky plains dominated by impact and eolian processes. Ubiquitous shallow, soil-filled, circular depressions, called hollows, are modified impact craters. Rocks are dark, fine-grained basalts, and the upper 10 m of the cratered plains appears to be an impact-generated regolith developed over intact basalt flows. Systematic field observations across the cratered plains identified vesicular clasts and rare scoria similar to original lava flow tops, consistent with an upper inflated surface of lava flows with adjacent collapse depressions. Crater and hollow morphometry are consistent with most being secondaries. The size frequency distribution of rocks >0.1 m diameter generally follows exponential functions similar to other landing sites for total rock abundances of 5-35%. Systematic clast counts show that areas with higher rock abundance and more large rocks have higher thermal inertia. Plains with lower thermal inertia have fewer rocks and substantially more pebbles that are well sorted and evenly spaced, similar to a desert pavement or lag. Eolian bed forms (ripples and wind tails) have coarse surface lags, and many are dust covered and thus likely inactive. Deflation of the surface _5-25 cm likely exposed two-toned rocks and elevated ventifacts and transported fines into craters creating the hollows. This observed redistribution yields extremely slow average erosion rates of _0.03 nm/yr and argues for very little long-term net change of the surface and a dry and desiccating environment similar to today's since the Hesperian (or _3 Ga).
Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals.
The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. OASIS is a NASA-funded research project that is currently being tested on the FIDO rover at JPL for the use on future missions.
ASE has successfully demonstrated that a spacecraft can be driven by science analysis and autonomously controlled. ASE is available for flight on other missions. Mission hardware design should consider ASE requirements for available onboard data storage, onboard memory size and processor speed.
Sulfate salt discoveries at the Eagle and Endurance craters in Meridiani Planum by the Mars Exploration Rover Opportunity have proven mineralogically the existence and involvement of water in Mars past. Visible and near infrared spectrometers like the Mars Express OMEGA, the Mars Reconnaissance Orbiter CRISM and the 2009 Mars Science Laboratory Rover cameras are powerful tools for the identification of water-bearing salts and other high priority minerals at Mars. The increasing spectral resolution and rover mission lifetimes represented by these missions currently necessitate data compression in order to ease downlink restrictions. On board data processing techniques can be used to guide the selection, measurement and return of scientifically important data from relevant targets, thus easing bandwidth stress and increasing scientific return. We have developed an automated support vector machine (SVM) detector operating in the visible/near-infrared (VisNIR, 300-2500 nm) spectral range trained to recognize the mineral jarosite (typically KFe3(SO4)2(OH)6), positively identified by the Mossbauer spectrometer at Meridiani Planum. Additional information is included in the original extended abstract.
We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.
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This paper provides a brief overview of the entire OASIS system and how it analyzes one type of data - grayscale images taken by the rover for engineering and hazard avoidance purposes.
As the number of currently active space missions increases, so does competition for Deep Space Network (DSN) resources. Even given unbounded DSN time, power and weight constraints onboard the spacecraft limit the maximum possible data transmission rate. These factors highlight a critical need for very effective data compression schemes. Images tend to be the most bandwidth-intensive data, so image compression methods are particularly valuable. In this paper, we describe a method for prioritizing regions in an image based on their scientific value. Using a wavelet compression method that can incorporate priority information, we ensure that the highest priority regions are transmitted with the highest fidelity.
Planetary geologists, geomorphologists, and hydrologists have hypothesized that Mars is a dynamic, water-enriched planet since the Mariner and Viking missions based on geologic, geomorphic, and topographic information. Recent acquisition of Gamma Ray and Neutron Spectrometer information has added further credence to this hypothesis. A unique investigation is underway to work towards being able to successfully map the extent and depth of water on Mars. Researchers from the University of Arizona and members of the Autonomous Sciencecraft Experiment (ASE) have been compiling multiple layers of information in time and space at the Central Avra Valley Storage and Recovery Project (CAVSARP) site, Tucson, Arizona, for eventual comparative analysis. This information has been acquired from a variety of observational/scientific platforms in controlled conditions. CAVSARP facility:
The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASAs New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The versatile ASE spacecraft command and control software, image formation software, and science processing software will be uploaded to the Earth Observer 1 (EO-1) spacecraft in early 2004 to detect surface modification related to volcanism, ice formation and retreat, and flooding.
This presentation will describe the concept of an autonomous, intelligent, rover-based rapid surveying system to identify and map several key lunar resources to optimize their ISRU (In Situ Resource Utilization) extraction potential. Prior to an extraction phase for any target resource, ground-based surveys are needed to provide confirmation of remote observation, to quantify and map their 3-D distribution, and to locate optimal extraction sites (e.g. ore bodies) with precision to maximize their economic benefit. The system will search for and quantify optimal minerals for oxygen production feedstock, water ice, and high glass-content regolith that can be used for building materials. These are targeted because of their utility and because they are, or are likely to be, variable in quantity over spatial scales accessible to a rover (i.e., few km). Oxygen has benefits for life support systems and as an oxidizer for propellants. Water is a key resource for sustainable exploration, with utility for life support, propellants, and other industrial processes. High glass-content regolith has utility as a feedstock for building materials as it readily sinters upon heating into a cohesive matrix more readily than other regolith materials or crystalline basalts. Lunar glasses are also a potential feedstock for oxygen production, as many are rich in iron and titanium oxides that are optimal for oxygen extraction. To accomplish this task, a system of sensors and decision-making algorithms for an autonomous prospecting rover is described. One set of sensors will be located in the wheel tread of the robotic search vehicle providing contact sensor data on regolith composition. Another set of instruments will be housed on the platform of the rover, including VIS-NIR imagers and spectrometers, both for far-field context and near-field characterization of the regolith in the immediate vicinity of the rover. Also included in the sensor suite are a neutron spectrometer, ground-penetrating radar, and an instrumented cone penetrometer for subsurface assessment. Output from these sensors will be evaluated autonomously in real-time by decision-making software to evaluate if any of the targeted resources has been detected, and if so, to quantify their abundance. Algorithms for optimizing the mapping strategy based on target resource abundance and distribution are also included in the autonomous software. This approach emphasizes on-the-fly survey measurements to enable efficient and rapid prospecting of large areas, which will improve the economics of ISRU system approaches. The mature technology will enable autonomous rovers to create in-situ resource maps of lunar or other planetary surfaces, which will facilitate human and robotic exploration.
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The successful implementation of machine learning in autonomous rover traverse science requires addressing challenges that range from the analytical technical realm, to the fuzzy, philosophical domain of entrenched belief systems within scientists and mission managers.
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In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically pursuing these science-collection opportunities during long-range rover traverses.
In this paper we will discuss how artificial intelligent technologies are synergistically integrated in multi-layer control architecture to enable a virtual spacecraft science agent.