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NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗
NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗
NASA NTRS1985

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics.

Firschein, O.↗
NASA NTRS1985

Toward the fully capable AI space mission planner

The necessity of expending numerous ground-side manhours to write extremely concise command codes for deep-space probes in-flight, which carry limited on-board processing capabilities, is encouraging the development of AI modules to perform the same task. An experimental automated sequence planner, Deviser, has proven successful enough, when used on a mainframe computer, to continue with the next generation of the concept, PLAN-IT, an expert scheduler for spacecraft observation targets. The new system will provide the ground-based user with interactive graphic displays for choosing targets and generates an expected time-line. An updated Deviser, using the new modules, will be tested after the Voyager Uranus encounter, and PLAN-IT will be employed to formulate Spacelab activity schedules.

Grenander, S.↗
NASA NTRS1985

The Use of AIS Data for Identifying and Mapping Calcareous Soils in Western Nebraska

The identification of calcareous soils, through unique spectral responses of the vegetation to the chemical nature of calcareous soils, can improve the accuracy of delineating the boundaries of soil mapping units over conventional field techniques. The objective of this experiment is to evaluate the use of the Airborne Imaging Spectrometer (AIS) in the identification and delineation of calcareous soils in the western Sandhills of Nebraska. Based upon statistical differences found in separating the spectral curves below 1.3 microns, calcareous and non-calcareous soils may be identified by differences in species of vegetation. Additional work is needed to identify biogeochemical differences between the two soils.

Samson, S. A.↗
NASA NTRS1985

Calibration of AIS Data Using Ground-based Spectral Reflectance Measurements

Present methods of correcting airborne imaging spectrometer (AIS) data for instrumental and atmospheric effects include the flat- or curved-field correction and a deviation-from-the-average adjustment performed on a line-by-line basis throughout the image. Both methods eliminate the atmospheric absorptions, but remove the possibility of studying the atmosphere for its own sake, or of using the atmospheric information present as a possible basis for theoretical modeling. The method discussed here relies on use of ground-based measurements of the surface spectral reflectance in comparison with scanner data to fix in a least-squares sense parameters in a simplified model of the atmosphere on a wavelength-by-wavelength basis. The model parameters (for optically thin conditions) are interpretable in terms of optical depth and scattering phase function, and thus, in principle, provide an approximate description of the atmosphere as a homogeneous body intervening between the sensor and the ground.

Conel, J. E.↗
NASA NTRS1985

Application of AIS Technology to Forest Mapping

Concerns about environmental effects of large scale deforestation have prompted efforts to map forests over large areas using various remote sensing data and image processing techniques. Basic research on the spectral characteristics of forest vegetation are required to form a basis for development of new techniques, and for image interpretation. Examination of LANDSAT data and image processing algorithms over a portion of boreal forest have demonstrated the complexity of relations between the various expressions of forest canopies, environmental variability, and the relative capacities of different image processing algorithms to achieve high classification accuracies under these conditions. Airborne Imaging Spectrometer (AIS) data may in part provide the means to interpret the responses of standard data and techniques to the vegetation based on its relatively high spectral resolution.

Yool, S. R.↗
NASA NTRS1985

Urban, Forest, and Agricultural AIS Data: Fine Spectral Structure

Spectra acquired by the Airborne Imaging Spectrometer (AIS) near Lafayette, IN, Ely, MN, and over the Stanford University campus, CA were analyzed for fine spectral structure using two techniques: the ratio of radiance of a ground target to the radiance of a standard and also the correlation coefficient of radiances at adjacent wavelengths. The results show ramp like features in the ratios. These features are due to the biochemical composition of the leaf and to the optical scattering properties of its cuticle. The size and shape of the ramps vary with ground cover.

Vanderbilt, V. C.↗
NASA NTRS1986

STAR - A computer language for hybrid AI applications

Constructing Artificial Intelligence application systems which rely on both symbolic and non-symbolic processing places heavy demands on the communication of data between dissimilar languages. This paper describes STAR (Simple Tool for Automated Reasoning), a computer language for the development of AI application systems which supports the transfer of data structures between a symbolic level and a non-symbolic level defined in languages such as FORTRAN, C and PASCAL. The organization of STAR is presented, followed by the description of an application involving STAR in the interpretation of airborne imaging spectrometer data.

Borchardt, G. C.↗
NASA NTRS1986

Comparison of various techniques for calibration of AIS data

The Airborne Imaging Spectrometer (AIS) samples a region which is strongly influenced by decreasing solar irradiance at longer wavelengths and strong atmospheric absorptions. Four techniques, the Log Residual, the Least Upper Bound Residual, the Flat Field Correction and calibration using field reflectance measurements were investigated as a means for removing these two features. Of the four techniques field reflectance calibration proved to be superior in terms of noise and normalization. Of the other three techniques, the Log Residual was superior when applied to areas which did not contain one dominant cover type. In heavily vegetated areas, the Log Residual proved to be ineffective. After removing anomalously bright data values, the Least Upper Bound Residual proved to be almost as effective as the Log Residual in sparsely vegetated areas and much more effective in heavily vegetated areas. Of all the techniques, the Flat Field Correction was the noisest.

Dar A. Roberts↗
NASA NTRS1986

Destriping AIS data using Fourier filtering techniques

Airborne Imaging Spectrometers (AIS) data collected in 1984 and 1985 showed pronounced striping in the vertical and horizontal directions. This striping reduced the signal to noise ratio so that features of the spectra of forest canopies were obscured or altered by noise. This noise was removed by application of a notch filter to the Fourier transform of the imagery in each waveband.

Hlavka, C.↗
NASA NTRS1986

Comparison of the 1984 and 1985 AIS data over the Singatse Range (Yerington), Nevada

The Singatse Range is composed of a series of 53 types of volcanic, plutonic, metamorphic, and sedimentary rocks. In addition the Jurassic plutonic rocks are also of economic interest for their copper mineralization which is contained in a porphyry dike swarm. The 1984 and 1985 flight results from the Airborne Imaging Spectrometer (AIS) instrument flown in the NASA/JPL C-130 aircraft are contrasted and compared. The 1984 data are less noisy than the 1985, in which many sets of vertical stripings from bad detectors can be seen. Significantly however, enough of the hydrothermal alteration patterns can be seen in each line at the mutual crossing points that one can say that the specific targets were detected in both year's flights. The spectra of both years are corrupted by the second-order effect from the grating, but 0-H bond absorption at essentially correct wavelengths for sericite and/or kaolinite can be seen.

Lyon, R. J. P.↗
NASA NTRS1987

Air traffic controller aids for planning of arrival traffic - An AI approach

Air traffic management is considered as an AI problem, and planning concepts are developed for incorporation into an automation aid for enroute arrival controllers being developed by NASA. An Assumption-Based Truth Maintenance System is modified to include the nonmonotonicities inherent in the Air Traffic Control (ATC) domain, and it is noted under what circumstances the advantages of the ATMS in standard problem-solving domains carry over to planning. The noninteracting actions of the conceptualization are contrasted with the interacting actions of other domains. It is shown that the noninteracting nature of the ATC domain makes it possible to provide an efficient planner that avoids the frame problem.

Chrisley, Ron↗
NASA NTRS1988

DAISY-DAMP: A distributed AI system for the dynamic allocation and management of power

One of the critical parameters that must be addressed when designing a loosely coupled Distributed AI SYstem (DAISY) has to do with the degree to which authority is centralized or decentralized. The decision to implement the Dynamic Allocation and Management of Power (DAMP) system as a network of cooperating agents mandated this study. The DAISY-DAMP problem is described; the component agents of the system are characterized; and the communication protocols system elucidated. The motivations and advantages in designing the system with authority decentralized is discussed. Progress in the area of Speech Act theory is proposed as playing a role in constructing decentralized systems.

Hall, Steven B.↗