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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3

Supporting Increased Autonomy for a Mars Rover

This paper presents an architecture and a set of technology for performing autonomous science and commanding for a planetary rover. The MER rovers have outperformed all expectations by lasting over 1100 sols (or Martian days), which is an order of magnitude longer than their original mission goal. The longevity of these vehicles will have significant effects on future mission goals, such as objectives for the Mars Science Laboratory rover mission (scheduled to fly in 2009) and the Astrobiology Field Lab rover mission (scheduled to potentially fly in 2016). Common objectives for future rover missions to Mars include the handling of opportunistic science, long-range or multi-sol driving, and onboard fault diagnosis and recovery. To handle these goals, a number of new technologies have been developed and integrated as part of the CLARAty architecture. CLARAty is a unified and reusable robotic architecture that was designed to simplify the integration, testing and maturation of robotic technologies for future missions. This paper focuses on technology comprising the CLARAty Decision Layer, which was designed to support and validate high-level autonomy technologies, such as automated planning and scheduling and onboard data analysis.

autonomous science↗

Intensity-based rock detection for acquiring onboard rover science

Missions to Mars already under study call for very long autonomous traverses (on the order of hundreds of meters per sol) during which there will be limited contact with Earth. The onboard Autonomous Science Investigation System (OASIS) is a technology for increasing science return during rover traverses by prioritizing science opportunities. By prioritizing data for downlink onboard, it is expected that the set of images selected for downlink by OASIS will have a consistently higher scientific interest then any set of equal number of images of the same traverse obtained using random or periodic sampling. Thus, OASIS can be used to increase the science returned from a long traverse. OASIS prioritizes data and identifies science opportunities by extracting information from image data and then using this information to prioritize the data and identify potential new science opportunities. Currently, the information extracted from the images is the location of the rocks in the image and several properties of these rocks such as size, albedo, texture and shape, In this paper we give an overview of the role of rock detection in the OASIS system and describe the functionality of the rock detector. We then present results on representative images

Judd, Michele↗

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↗

Lessons Learned from Autonomous Sciencecraft Experiment

An Autonomous Science Agent has been flying onboard the Earth Observing One Spacecraft since 2003. This software enables the spacecraft to autonomously detect and responds to science events occurring on the Earth such as volcanoes, flooding, and snow melt. The package includes AI-based software systems that perform science data analysis, deliberative planning, and run-time robust execution. This software is in routine use to fly the EO-l mission. In this paper we briefly review the agent architecture and discuss lessons learned from this multi-year flight effort pertinent to deployment of software agents to critical applications.

Autonomous Sciencecraft Experiment (ASE)↗

Autonomous Coordination of Science Observations Using Multiple Spacecraft

This software provides capabilities for autonomous cross-cueing and coordinated observations between multiple orbital and landed assets. Previous work has been done in re-tasking a single Earth orbiter or a Mars rover in response to that craft detecting a science event. This work enables multiple spacecraft to communicate (over a network designed for deep-space communications) and autonomously coordinate the characterization of such a science event. This work investigates a new paradigm of space science campaigns where opportunistic science observations are autonomously coordinated among multiple spacecraft. In this paradigm, opportunistic science detections can be cued by multiple assets where a second asset is requested to take additional observations characterizing the identified surface feature or event. To support this new paradigm, an autonomous science system for multiple spacecraft assets was integrated with the Interplanetary Network DTN (Delay Tolerant Network) to provide communication between spacecraft assets. This technology enables new mission concepts that are not feasible with current technology. The ability to rapidly coordinate activities across spacecraft without requiring ground in the loop enables rapid reaction to dynamic events across platforms, such as a survey instrument followed by a targeted high resolution instrument, as well as regular simultaneous observations.

Estlin, Tara A.↗

Ten past and ten future GAS/MAUS-payloads

MAUS (materials science autonomous experiments) is one out of a series of flight opportunities which the Space Program of West Germany offers to scientists from the disciplines of materials research and processing for performing materials science investigations under microgravity conditions. Up to now, ten MAUS experiments were flown which were dealing with the following scientific topics: decomposition of binary alloys with miscibility gap in the liquid state, interaction of a solidification front with dispersed particles, critical Marangoni number, investigation of the magnetic compound MnBi, shrinkage of gas bubbles in glass melts and slip casting. The ten future experiments are partly reflights with modification of the scientific objectives as well as new experiments in the fields of chemical reactions, heat transfer, glass technology and Ostwald ripening. Looking to ten flown payloads, the peculiarities of instrument technology in GAS-cans and its evolution is discussed with emphasis on structure, electronics and thermal design. A typical modern payload using 100 percent of the resource is presented.

Staniek, S.↗

VML 3.0 Reactive Sequencing Objects and Matrix Math Operations for Attitude Profiling

VML (Virtual Machine Language) has been used as the sequencing flight software on over a dozen JPL deep-space missions, most recently flying on GRAIL and JUNO. In conjunction with the NASA SBIR entitled "Reactive Rendezvous and Docking Sequencer", VML version 3.0 has been enhanced to include object-oriented element organization, built-in queuing operations, and sophisticated matrix / vector operations. These improvements allow VML scripts to easily perform much of the work that formerly would have required a great deal of expensive flight software development to realize. Autonomous turning and tracking makes considerable use of new VML features. Profiles generated by flight software are managed using object-oriented VML data constructs executed in discrete time by the VML flight software. VML vector and matrix operations provide the ability to calculate and supply quaternions to the attitude controller flight software which produces torque requests. Using VML-based attitude planning components eliminates flight software development effort, and reduces corresponding costs. In addition, the direct management of the quaternions allows turning and tracking to be tied in with sophisticated high-level VML state machines. These state machines provide autonomous management of spacecraft operations during critical tasks like a hypothetic Mars sample return rendezvous and docking. State machines created for autonomous science observations can also use this sort of attitude planning system, allowing heightened autonomy levels to reduce operations costs. VML state machines cannot be considered merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. The state machine approach enabled by VML 3.0 is progressing toward flight capability with a wide array of applicable mission activities.

VML (Virtual Machine Language)↗

Future MAUS payload and the TWIN-MAUS configuration

The German MAUS project (materials science autonomous experiments in weightlessness) was initiated in 1979 for optimum utilization of NASA's Get Away Special (GAS) program. The standard MAUS system was developed to meet GAS requirements and can accommodate a wide variety of GAS-type experiments. The system offers a range of services to experimenters within the framework of standardized interfaces. Four MAUS payloads being prepared for future space shuttle flight opportunities are described. The experiments include critical Marangoni convection, oscillatory Marangoni convection, pool boiling, and gas bubbles in glass melts. Scientific objectives as well as equipment hardware are presented together with recent improvements to the MAUS standard system, e.g., a new experiment control and data management unit and a semiconductor memory. A promising means of increasing resources in the field of GAS experiments is the interconnection of GAS containers. This important feature has been studied to meet the challenge of future advanced payloads. In the TWIN-MAUS configuration, electrical power and data will be transferred between two containers mounted adjacent to each other.

Staniek, S.↗

The Lunar Ultraviolet Telescope Experiment (LUTE): Enabling technology for an early lunar surface payload

The Lunar Ultraviolet Telescope Experiment (LUTE) is a 1-m aperture, fixed declination, optical telescope to be operated on the surface of the Moon. This autonomous science payload will provide an unprecedented ultraviolet stellar survey even before manned lunar missions are resumed. This paper very briefly summarizes the LUTE concept analyzed by the LUTE Task Team of NASA's Marshall Space Flight Center (MSFC). Scientific capabilities and the Reference Design Concept are identified, and the expected system characteristics are summarized. Technologies which will be required to enable the early development, deployment, and operation of the LUTE are identified, and the principle goals and approaches for their advancement are described.

Nein, M. E.↗

Enabling Spacecraft Formation Flying in Any Earth Orbit Through Spaceborne GPS and Enhanced Autonomy Technologies

Formation flying is quickly revolutionizing the way the space community conducts autonomous science missions around the Earth and in space. This technological revolution will provide new, innovative ways for this community to gather scientific information, share this information between space vehicles and the ground, and expedite the human exploration of space. Once fully matured, this technology will result in swarms of space vehicles flying as a virtual platform and gathering significantly more and better science data than is possible today. Formation flying will be enabled through the development and deployment of spaceborne differential Global Positioning System (GPS) technology and through innovative spacecraft autonomy techniques, This paper provides an overview of the current status of NASA/DoD/Industry/University partnership to bring formation flying technology to the forefront as quickly as possible, the hurdles that need to be overcome to achieve the formation flying vision, and the team's approach to transfer this technology to space. It will also describe some of the formation flying testbeds, such as Orion, that are being developed to demonstrate and validate these innovative GPS sensing and formation control technologies.

Bauer, F. H.↗

NASA Tech Briefs, March 2006

Topics covered include: Medical Signal-Conditioning and Data-Interface System; Instruments for Reading Direct-Marked Data-Matrix Symbols; Processing EOS MLS Level-2 Data; Ground Processing of Data From the Mars Exploration Rovers; Estimating Total Electron Content Using 1,000+ GPS Receivers; NASA Solar Array Demonstrates Commercial Potential; Improved Control of Charging Voltage for Li-Ion Battery; Programmable Pulse-Position-Modulation Encoder; Wavelength-Agile External-Cavity Diode Laser for DWDM; Pattern-Recognition Processor Using Holographic Photopolymer; Submicrosecond Power-Switching Test Circuit; Three-Function Logic Gate Controlled by Analog Voltage; Integrated System for Autonomous Science; Montage Version 3.0; Utilizing AI in Temporal, Spatial, and Resource Scheduling; Satellite Image Mosaic Engine; Architecture for Control of the K9 Rover; HFGMC Enhancement of MAC/GMC; Automated Activation and Deactivation of a System Under Test; Cleaning Carbon Nanotubes by Use of Mild Oxygen Plasmas; Generating Aromatics From CO2 on Mars or Natural Gas on Earth; Attaching Thermocouples by Peening or Crimping; Heat Treatment of Friction-Stir-Welded 7050 Aluminum Plates; Generating Breathable Air Through Dissociation of N2O; High-Performance Scanning Acousto-Ultrasonic System; Correction for Thermal EMFs in Thermocouple Feedthroughs; Using Quasiparticle Poisoning To Detect Photons; Estimating Resolution Lengths of Hybrid Turbulence Models; Education and Training Module in Alertness Management; Cargo-Positioning System for Next-Generation Spacecraft; Micro-Imagers for Spaceborne Cell-Growth Experiments; Holographic Solar Photon Thrusters; Plasma-Based Detector of Outer-Space Dust Particles; and Generation of Data-Rate Profiles of Ka-Band Deep-Space Links.

Source record↗

Reactive Sequencing for Autonomous Navigation Evolving from Phoenix Entry, Descent, and Landing

Virtual Machine Language (VML) is an award-winning advanced procedural sequencing language in use on NASA deep-space missions since 1997, and was used for the successful entry, descent, and landing (EDL) of the Phoenix spacecraft onto the surface of Mars. Phoenix EDL utilized a state-oriented operations architecture which executed within the constraints of the existing VML 2.0 flight capability, compatible with the linear "land or die" nature of the mission. The intricacies of Phoenix EDL included the planned discarding of portions of the vehicle, the complex communications management for relay through on-orbit assets, the presence of temporally indeterminate physical events, and the need to rapidly catch up four days of sequencing should a reboot of the spacecraft flight computer occur shortly before atmospheric entry. These formidable operational challenges led to new techniques for packaging and coordinating reusable sequences called blocks using one-way synchronization via VML sequencing global variable events. The coordinated blocks acted as an ensemble to land the spacecraft, while individually managing various elements in as simple a fashion as possible. This paper outlines prototype VML 2.1 flight capabilities that have evolved from the one-way synchronization techniques in order to implement even more ambitious autonomous mission capabilities. Target missions for these new capabilities include autonomous touch-and-go sampling of cometary and asteroidal bodies, lunar landing of robotic missions, and ultimately landing of crewed lunar vehicles. Close proximity guidance, navigation, and control operations, on-orbit rendezvous, and descent and landing events featured in these missions require elaborate abort capability, manifesting highly non-linear scenarios that are so complex as to overtax traditional sequencing, or even the sort of one-way coordinated sequencing used during EDL. Foreseeing advanced command and control needs for small body and lunar landing guidance, navigation and control scenarios, work began three years ago on substantial upgrades to VML that are now being exercised in scenarios for lunar landing and comet/asteroid rendezvous. The advanced state-based approach includes coordinated state transition machines with distributed decision-making logic. These state machines are not merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. Combined with the JPL's AutoNav software used on Deep Space 1 and Deep Impact, the system allows spacecraft to autonomously navigate to an unmapped surface, soft-contact, and either land or ascend. The state machine architecture enabled by VML 2.1 has successfully performed sampling missions and lunar descent missions in a simulated environment, and is progressing toward flight capability. The authors are also investigating using the VML 2.1 flight director architecture to perform autonomous activities like rendezvous with a passive hypothetical Mars sample return capsule. The approach being pursued is similar to the touch-and-go sampling state machines, with the added complications associated with the search for, physical capture of, and securing of a separate spacecraft. Complications include optically finding and tracking the Orbiting Sample Capsule (OSC), keeping the OSC illuminated, making orbital adjustments, and physically capturing the OSC. Other applications could include autonomous science collection and fault compensation.

asteroid↗

Autonomous Sciencecraft Constellation science study report

The image processing and scientific analysis techniques described in this study report are not confined to radar date of Earth. The techniques have widespread applications, theoretically to any instrument at any wavelength (such as visible and infrared imagers). We illustrate how the same techniques demonstrated by ASC can be used to search for change, and identify features, on Mars and the Jovian satelites, Europa and IO.

Constellation ASC↗

Autonomous Acquisition of Science Quality Spectra from Unmanned Aerial Systems

This is a presentation on a technique for using Unmanned Aerial Systems with intelligent sensing to create science quality data. In this particular case, the team used two spectrometers with high spectral resolution and developed a technique to have the spectrometers self calibrate so that it is possible to take the measurements within the allocated 10 minute flight times. Because there is both upwelling and downwelling foreoptics on each of the two spectrometers, it becomes slow to set the calibration up for all four possibilities which relates to the integration time and then take the measurements. If light conditions change too rapidly then the measurements won't be scaled so that the spectra max is at least 85% of the instrument max but not saturated. This process was more difficult then it appeared due to the complexity of determining the integration time before light conditions changed and before the battery runs our of charge to fly the Unmanned Aerial System. This presentation goes over the results for the first few flight days and the planned future flight experiments.

Mandl, Daniel↗

Autonomous onboard traverse science system

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.

OASIS↗

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.↗