Near Earth Asteroid Scout (NEA Scout) Science Concept of Operations Utilizing Onboard Data Analysis
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Bassett, Christophe↗
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Results for “onboard science analysis”
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Science Autonomy for Ocean Worlds Astrobiology: A Perspective
Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” (e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.
ocean worlds↗
The Quest for Organic Carbon on Mars
We are entering an era of Mars exploration in which organic carbon detection, characterization, and structural identification will be key to addressing some of the outstanding science objectives of the Mars Exploration Program. Success of these missions will depend on technical, scientific, and strategic elements--all of which are strongly determined based on terrestrial experience and knowledge of organic matter formation, concentration, and preservation. Analog studies including Precambrian sediments, modern endolithic communities, and experiments help us fine-tune these approaches, but we also need to expect the unexpected. This presentation will provide perspective on the challenges of detecting organic carbon on Mars, how we may achieve such detections with the in situ instruments, such as the SAM (Science Analysis at Mars) instrument suite onboard Curiosity, the rover for the 2011 Mars Science Laboratory mission.
Eigenbrode, Jennifer↗
Fast Image Texture Classification Using Decision Trees
Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.
Thompson, David R.↗
Rover traverse science for increased mission science return
In this paper, we will describe out methods for the prioritization of geologic data acquired by an in-situ rover. Our techniques are applicable to a wide range of data modalites, however out initial demonstration is focused on image analysis, as images consume a large volume of the downlink bandwidth for such missions.
data prioritization↗
A COTS-Based Fault Tolerant Parallel Processing Supercomputer for Spacecraft Onboard Scientic Data Analysis
NASA's future spaceborne science missions will require supercomputing capabilities for both near earth and deep space exploration.
supercomputing fault tolerance REE SIFT↗
Onboard Autonomous Rock Shape Analysis for Mars Rovers
In the quest to maximize the scientific return of future robotic missions, it is imperative that our rovers be capable of determining the importance of the science they collect so that they may prioritize the acquisition and relay of that data. As an important step in this process, we present an automated technique to allow a rover to classify the shape and other geologic characteristics of rocks from two-dimensional photographic images and three-dimensional stereographically produced data. Experiments were conducted in the Matlab environment using images returned by JPL's Mars Pathfinder mission. Our method begins by first segmenting the rocks from the background using a combination of image intensity and height data. Various metrics are then used to classify the region's sphericity, roundness, and other geometric properties. Preliminary experiments to determine the most useful metrics were conducted by characterizing the two-dimensional rock shape while the three-dimensional shape was later studied with metrics derived from these two-dimensional techniques. Seven measures were developed and implemented. The performance of each measure was characterized by analyzing imagesfrom the Pathfinder mission and ranking the rocks according to the measured properties. Combined, the measures would provide a tool by which an automated rover could discover a greater amount of information about the data it collects, leading to a more productive mission.
image segmentation↗
The Intelligent Data Understanding Element of NASA's Intelligent Systems Program
Within the NASA Intelligent Systems Program, the Intelligent Data Understanding (IDU) element develops techniques for transforming data into scientific understanding. Automating such tools is critical for space science, space-based earth science, and planetary exploration with onboard scientific data analysis. Intelligent data understanding (IDU) is about extracting meaning from large, diverse science and engineering databases, via autonomous techniques that transform very large datasets into understanding. The earth science community in particular needs new tools for analyzing multi-formatted and geographically distributed datasets and for identifying cause-effect relationships in the complex data. Research within the IDU program element seeks to automate data analysis tasks so that humans can focus on creative hypothesis generation and knowledge synthesis. It may also enable NASA space missions in which autonomous agents must generate knowledge and take actions, and missions where limited bandwidth permits transmission of only the most interesting scientific observations, summaries, and conclusions. Twenty-seven research projects are-currently funded.
Coughlan, Joseph C.↗
Partially Chloritized Smectites: Analogues of Smectites at Gale Crater, Mars
Characterizing the structure and composition of phyllosilicates is important for interpreting the aqueous history of Mars and identifying potential habitable environments. Smectites and chlorites are the most dominant clay types on Mars, and there is evidence of the presence of smectite/chlorite intergrades. Smectite has been detected at Gale Crater, Mars, via orbital observations and in-situ measurements, in abundances up to approximately 25 weight percentage of bulk rock. John Klein (JK) and Cumberland (CB) were analyzed by the Chemistry and Mineralogy (CheMin) and Samples Analysis at Mars (SAM) evolved gas analysis experiment (EGA) instruments, onboard Mars Science Laboratory (MSL), Curiosity, to distinguish clay mineralogy. John Klein has a collapsed 2:1 smectite with a d-spacing of 10 Angstroms, whereas Cumberland smectite did not fully collapse and has a d-spacing of approximately 13.2 Angstroms. It has been suggested that partial chloritization or ‘pillaring’ could be responsible for the expanded Cumberland smectite because pillaring inhibits the collapse of smectites down to 10 Angstrom, even under the desiccating conditions on the martian surface. Clay minerals have been detected in ancient fluvio-lacustrine rocks throughout Curiosity’s traverse and catalog the changes of the lake water chemistry and diagenetic conditions at Gale Crater, Mars. Investigating clay minerals is important for identifying them on the Martian surface, in particular as Curiosity proceeds into the upcoming Clay-bearing Unit.
Tu, V. M.↗
A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions
Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.
astrobiology↗
Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds Science
Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.
Distributed Systems↗
DEMO: the Autonomous Sciencecraft Experiment onboard the EO-1 spacecraft
The Autonomous Sciencececraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-1) spacecraft, integrates several autnomoy software technologies enabling autnomous science analysis and mission planning. The experiment demonstrates the potential for future space missions to use onboard decision-making to respond autonomously to capture short-lived science phenomena. The AAAI software demonstration will consist of two sections: a real-time display of an ASE-commanded ground contact from the EO-1 spacecraft, and a simulation of the full ASE autonomous science-response scenario.
EO-1 spacecraft↗
The autonomous sciencecraft experiment onboard the EO-1 spacecraft
The Autonomous Sciencecraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-I) spacecraft, integrates several autonomy software technologies enabling autonomous science analysis and mission planning. The experiment demonstrates the potential for future space missions to use onboard decision-making to respond autonomously to capture short-lived science phenomena. The AAAI software demonstration will consist of two sections: a real-time display of an ASE-commanded ground contact from the EO-1 spacecraft, and a simulation of the full ASE autonomous science-response scenario.
Earth Observatory (EO)↗
Onboard science data processing: ST6 autonomous science experiment
Explore the source record for details and available documents.
onboard processing science data analysis spacecraf↗
Tele-Supervised Adaptive Ocean Sensor Fleet
The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate for the OASIS ASV system and allows for independent development and testing of higher-level software components. The Platform Communicator acts as a proxy for both actual and simulated platforms. It translates platform-independent messages from the higher control systems to the device-dependent communication protocols. This enables the higher-level control systems to interact identically with heterogeneous actual or simulated platforms.
Lefes, Alberto↗
Using a Multicore Processor for Rover Autonomous Science
Multicore processing promises to be a critical component of future spacecraft. It provides immense increases in onboard processing power and provides an environment for directly supporting fault-tolerant computing. This paper discusses using a state-of-the-art multicore processor to efficiently perform image analysis onboard a Mars rover in support of autonomous science activities.
Mars↗
Coordinated Analyses of Antarctic Sediments as Mars Analog Materials Using Reflectance Spectroscopy and Current Flight-Like Instruments for CheMin, SAM and MOMA
Coordinated analyses of mineralogy and chemistry of sediments from the Antarctic Dry Valleys illustrate how data obtained using flight-ready technology of current NASA and ESA missions can be combined for greater understanding of the samples. Mineralogy was measured by X-ray diffraction (XRD) and visible/ near-infrared (VNIR) reflectance spectroscopy. Chemical analyses utilized a quadrupole mass spectrometer (QMS) to perform pyrolysis-evolved gas analysis (EGA) and gas chromatography-mass spectrometry (GC/MS) both with and without derivatization, as well as laser desorption-mass spectrometry (LD/MS) techniques. These analyses are designed to demonstrate some of the capabilities of near-term landed Mars missions, to provide ground truthing of VNIR reflectance data acquired from orbit by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on MRO and to provide detection limits for surface- operated instruments: the Chemistry and Mineralogy (CheMin) and Sample Analysis at Mars (SAM) instrument suites onboard Mars Science Laboratory (MSL) and the Mars Organic Molecule Analyzer (MOMA) onboard ExoMars-2018. The new data from this study are compared with previous analyses of the sediments performed with other techniques. Tremolite was found in the oxic region samples for the first time using the CheMin-like XRD instrument. The NIR spectral features of tremolite are consistent with those observed in these samples. Although the tremolite bands are weak in spectra of these samples, spectral features near 2.32 and 2.39 micrometers could be detected by CRISM if tremolite is present on the martian surface. Allophane was found to be a good match to weak NIR features at 1.37-1.41, 1.92, and 2.19 micrometers in spectra of the oxic region sediments and is a common component of immature volcanic soils. Biogenic methane was found to be associated with calcite in the oxic region samples by the SAM/EGA instrument and a phosphoric acid derivative was found in the anoxic region sample using the SAM/MTBSTFA technique.
Mars↗
Synthesis of Akaganeite in the Presence of Sulfate: Implications for Akaganeite Formation in Yellowknife Bay, Gale Crater, Mars
Akaganeite (beta-FeOOH) is an Fe(III) (hydr)oxide with a tunnel structure usually occupied by chloride. Akaganeite has been recently discovered in a mudstone on the surface of Mars by the Chemistry and Mineralogy (CheMin) and Sample Analysis at Mars (SAM) instruments onboard the Mars Science Laboratory (MSL) Curiosity Rover in Gale crater [1, 2]. Akaganeite was detected together with sulfate minerals [anhydrite (CaSO4) and basanite (2CaSO4·2H2O)] in the drilled Cumberland and John Clein mudstone samples at Yellowknife Bay [2]. Discovery of akaganeite and sulfates in the same samples suggests that sulfate ions could be present in aqueous solution during akaganeite formation. However, mechanism and aqueous environmental conditions of akaganeite formation (e.g., pH and range of sulfate concentration) in Yellowknife Bay remain unknown. The objective of our work was to perform synthesis of akaganeite without or with sulfate addition at variable pHs in order to constrain formation conditions of akaganeite in Yellowknife Bay, Gale crater on Mars.
Peretyazhko, T. S.↗