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The Autonomous Sciencecraft Constellation (ASC) experiment, which will fly onboard the Air Force's TechSat-21 constellation, will demonstrate the use of onboard science analysis and replanning to increase science return.
An onboard processing mission concept is under development for a possible Direct Broadcast capability for the HyspIRI mission, a Hyperspectral remote sensing mission under consideration for launch in the next decade. The concept would intelligently spectrally and spatially subsample the data as well as generate science products onboard to enable return of key rapid response science and applications information despite limited downlink bandwidth. This rapid data delivery concept focuses on wildfires and volcanoes as primary applications, but also has applications to vegetation, coastal flooding, dust, and snow/ice applications. Operationally, the HyspIRI team would define a set of spatial regions of interest where specific algorithms would be executed. For example, known coastal areas would have certain products or bands downlinked, ocean areas might have other bands downlinked, and during fire seasons other areas would be processed for active fire detections. Ground operations would automatically generate the mission plans specifying the highest priority tasks executable within onboard computation, setup, and data downlink constraints. The spectral bands of the TIR (thermal infrared) instrument can accurately detect the thermal signature of fires and send down alerts, as well as the thermal and VSWIR (visible to short-wave infrared) data corresponding to the active fires. Active volcanism also produces a distinctive thermal signature that can be detected onboard to enable spatial subsampling. Onboard algorithms and ground-based algorithms suitable for onboard deployment are mature. On HyspIRI, the algorithm would perform a table-driven temperature inversion from several spectral TIR bands, and then trigger downlink of the entire spectrum for each of the hot pixels identified. Ocean and coastal applications include sea surface temperature (using a small spectral subset of TIR data, but requiring considerable ancillary data), and ocean color applications to track biological activity such as harmful algal blooms. Measuring surface water extent to track flooding is another rapid response product leveraging VSWIR spectral information.
To explore high priority landing sites and to prepare for eventual human exploration, future Mars missions will involve rovers capable of traversing tens of kilometers. However, the current process by which scientists interact with a rover does not scale to such distances. Specifically, numerous command cycles are required to complete even simple tasks, such as, pointing the spectrometer at a variety of nearby rocks. In addition, the time required by scientists to interpret image data before new commands can be given and the limited amount of data that can be downlinked during a given command cycle constrain rover mobility and achievement of science goals. Experience with rover tests on Earth supports these concerns. As a result, traverses to science sites as identified in orbital images would require numerous science command cycles over a period of many weeks, months or even years, perhaps exceeding rover design life and other constraints. Autonomous onboard science analysis can address these problems in two ways. First, it will allow the rover to transmit only "interesting" images, defined as those likely to have higher science content. Second, the rover will be able to anticipate future commands. For example, a rover might autonomously acquire and return spectra of "interesting" rocks along with a high resolution image of those rocks in addition to returning the context images in which they were detected. Such approaches, coupled with appropriate navigational software, help to address both the data volume and command cycle bottlenecks that limit both rover mobility and science yield. We are developing fast, autonomous algorithms to enable such intelligent on-board decision making by spacecraft. Autonomous algorithms developed to date have the ability to identify rocks and layers in a scene, locate the horizon, and compress multi-spectral image data. Output from these algorithms could be used to autonomously obtain rock spectra, determine which images should be transmitted to the ground, or to aid in image compression. We will discuss these and other algorithms and demonstrate their performance during a recent rover field test.
Recent autonomy experiments conducted on Earth Observing 1 (EO-1) using the Autonomous Sciencecraft Experiment (ASE) flight software has been used to classify key features in hyperspectral images captured by EO-1. Furthermore, analysis is performed by this software onboard EO-1 and then used to modify the operational plan without interaction from the ground. This paper will outline the overall operations concept and provide some details and examples of the onboard science processing, science analysis, and replanning.
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Much of the initial focus for spacecraft autonomy has been on developing new software and systems concepts to automate engineering functions of the spacecraft: guidance, navigation and control, fault protection, and resources management. However, the ultimate objectives of NASA missions are science objectives, which implies that we need a new framework for perfoming science data evaluation and observation planning autonomously onboard spacecraft.
We present an integrated system for the intelligent progressive transmission of data for deep space communications.
The Combustion Integrated Rack (CIR) is one of three facility payload racks being developed for the International Space Station (ISS) Fluids and Combustion Facility (FCF). Most microgravity combustion experiments will be performed onboard the Space Station in the Combustion Integrated Rack. Experiment-specific equipment will be installed on orbit in the CIR to customize it to perform many different scientific experiments during the ten or more years that it will operate on orbit. This paper provides an overview of the CIR, including a description of its preliminary design and planned accommodations for microgravity combustion science experiments, and descriptions of the combustion science experiments currently planned for the CIR.
Coming years will bring several comet rendezvous missions. The Rosetta spacecraft arrives at Comet 67P/Churyumov-Gerasimenko in 2014. Subsequent rendezvous might include a mission such as the proposed Comet Hopper with multiple surface landings, as well as Comet Nucleus Sample Return (CNSR) and Coma Rendezvous and Sample Return (CRSR). These encounters will begin to shed light on a population that, despite several previous flybys, remains mysterious and poorly understood. Scientists still have little direct knowledge of interactions between the nucleus and coma, their variation across different comets or their evolution over time. Activity may change on short timescales so it is challenging to characterize with scripted data acquisition. Here we investigate automatic onboard image analysis that could act faster than round-trip light time to capture unexpected outbursts and plume activity. We describe one edge-based method for detect comet nuclei and plumes, and test the approach on an existing catalog of comet images. Finally, we quantify benefits to specific measurement objectives by simulating a basic plume monitoring campaign.
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Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. This circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions. Our technology evaluates the geologic data gathered by the 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 additional science gathering opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity.
Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. We have developed an onboard science analysis technology for increasing science return from missions.
The Dynamic Albedo of Neutrons (DAN) instrument onboard Mars Science Laboratory (MSL) consists of a pulsed neutron generator (PNG) and the neutron detector/electronics (DE) module. There are two 3He-proportional counters used in the DE module. One counter is surrounded with a Cd shell enclosure and measures epi-thermal neutrons with >0.4 eV. The second detector does not have a Cd shell enclosure and measures both thermal and epi-thermal neutrons. The main scientific objectives of DAN are two-fold: (1) the primary objective is to measure the bulk hydrogen abundance (in forms of water or hydrated minerals) of the sub-surface and (2) the secondary objective is to measure the background neutron environment at the surface. DAN achieves the first objective by using a pulsed 14 MeV neutron source (ACTIVE mode). Emitted neutrons undergo a series of nuclear interactions with the regolith and lose energy. The DAN DE module then measures the time profile of the neutrons returned from the regolith. Even a small amount of hydrogen, as low as 0.1 weight % in the regolith, can effectively moderate the high energy neutrons [Mitrofanov, et al., ]. The less-moderated (or epi-thermal) neutrons arrive at the detector earlier than the more-moderated (or thermal) neutrons. The shape and magnitude of neutron time profile curves after pulses can be used to estimate the depth distribution of the hydrogen content. When no neutron pulse is used (PASSIVE mode) DAN measures the neutron background environment at the Mars surface. There are two sources of the background neutrons which DAN would measure: one is secondary neutrons generated by galactic cosmic ray (GCR) interactions with atmospheric and surface materials and the other is neutrons from the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) onboard MSL as a power source. The passive mode data for the first 1300 sols of the MSL mission are presented in this paper. A summary of the DAN surface operation is described by [Mitrofanov et al., 2012; Jun et al., 2013.
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
The Advanced Topographic Laser Altimetry System (ATLAS) is the sole instrument on the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2). Without some method of reducing the transmitted data, the volume of ATLAS telemetry would far exceed the normal X-band downlink capability or require many more ground station contacts. The ATLAS Onboard Flight Science Receiver Algorithms (hereinafter Receiver Algorithms or Algorithms) control the amount of science data that is telemetered from the instrument, limiting the data volume by distinguishing surface echoes from background noise, and allowing the instrument to telemeter data from only a small vertical region about the signal. This is accomplished through the transfer of the spacecraft's location and attitude to the instrument every second, use of an onboard Digital Elevation Model, implementation of signal processing techniques, and use of onboard relief and surface type reference maps. Extensive ground testing verified the performance of the Algorithms. On-orbit analysis shows that the Algorithms are working as expected from the ground testing; they are performing well and meeting the mission requirements.
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.