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At least 163 records · Page 9
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NASA NTRS2018

Cognitive Anti-Jamming Satellite-To-Ground Communications on NASA's SCaN Testbed

Machine learning aided cognitive anti-jamming communications is designed, developed and demonstrated on a live satellite-to-ground link. A wideband autonomous cognitive radio (WACR) is designed and implemented as a hardware-in-the- loop (HITL) prototype. The cognitive engine (CE) of the WACR is implemented on a PC while the software-defined radio (SDR) platform utilized two different radios for spectrum sensing and actual communications. The cognitive engine performs spectrum knowledge acquisition over the complete spectrum range available for the SATCOM system operation and learns an anti-jamming communications protocol to avoid both intentional jammers and inadvertent interferers using reinforcement learning. When the current satellite-to-ground link is jammed, the cognitive engine of the ground receiver directs the satellite transmitter to switch to a new channel that is predicted to be jammer-free for the longest possible duration. The end-to-end, closed-loop system was tested on the NASA Space Communications and Navigation (SCaN) Testbed on the International Space Station (ISS). The experimental results demonstrated the feasibility of satellite-to-ground cognitive anti-jamming communications along with excellent anti-jamming capability of machine learning aided cognitive protocols against several different types of jammers.

Jayaweera, Sudharman K.↗
NASA NTRS2018

Cognitive Anti-jamming Satellite-to-Ground Communications on NASA's SCaN Testbed

Machine learning aided cognitive anti-jamming communications is designed, developed and demonstrated on an experimental satellite-to-ground link. A wideband autonomous cognitive radio (WACR) is designed and implemented as a hardware-in the-loop (HITL) prototype. The cognitive engine (CE) of the WACR is implemented on a PC while the software-defined radio (SDR) platform utilized two different radios for spectrum sensing and actual communications. The cognitive engine performs spectrum knowledge acquisition over the complete spectrum range available for the SATCOM system operation and learns an anti-jamming communications protocol to avoid both intentional jammers and inadvertent interferers using reinforcement learning. When the current satellite-to-ground link is jammed, the cognitive engine of the ground receiver directs the satellite transmitter to switch to a new channel that is predicted to be jammer-free for the longest possible duration. The end-to-end, closed-loop system was tested with the NASA's Space Communications and Networking (SCaN) testbed on the International Space Station (ISS). The experimental results demonstrated the feasibility of satellite-to-ground cognitive anti-jamming communications along with excellent anti-jamming capability of machine-learning aided cognitive protocols against several different types of jammers.

Jayaweera, Sudharman K.↗
NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU)processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRS2014

First Results from the Mojave Volatiles Prospector (MVP) Field Campaign, a Lunar Polar Rover Mission Analog

The Mojave Volatiles Prospector (MVP) project is a science-driven field program with the goal to produce critical knowledge for conducting robotic exploration of the Moon. MVP will feed science, payload, and operational lessons learned to the development of a real-time, short-duration lunar polar volatiles prospecting mission. MVP achieves these goals through a simulated lunar rover mission to investigate the composition and distribution of surface and subsurface volatiles in a natural and a priori unknown environment within the Mojave Desert, improving our understanding of how to find, characterize, and access volatiles on the Moon. The MVP field site is the Mojave Desert, selected for its low, naturally occurring water abundance. The Mojave typically has on the order of 2-6% water, making it a suitable lunar analog for this field test. MVP uses the Near Infrared and Visible Spectrometer Subsystem (NIRVSS), Neutron Spectrometer Subsystem (NSS), and a downward facing GroundCam camera on the KREX-2 rover to investigate the relationship between the distribution of volatiles and soil crust variation. Through this investigation, we mature robotic in situ instruments and concepts of instrument operations, improve ground software tools for real time science, and carry out publishable research on the water cycle and its connection to geomorphology and mineralogy in desert environments. A lunar polar rover mission is unlike prior space missions and requires a new concept of operations. The rover must navigate 3-5 km of terrain and examine multiple sites in in just ~6 days. Operational decisions must be made in real time, requiring constant situational awareness, data analysis and rapid turnaround decision support tools. This presentation will focus on the first science results and operational architecture findings from the MVP field deployment relevant to a lunar polar rover mission.

Heldmann, Jennifer L.↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RAS-SOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar sur-face, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensing. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRS2018

Initial Operations Experience and Results from the Juno Gravity Experiment

Radio communications between the Juno spacecraft, in orbit around Jupiter, and the Earth-based observing stations of NASA’s Deep Space Network enable measurements of the Doppler shift induced on the radio signals by Juno’s motion in the Jovian environment. This measurement of the Doppler shift improves the knowledge of Jupiter’s gravitational field. As a radio science instrument, Juno’s gravity science instrument utilizes a ground component at the Deep Space Network’s DSS-25 antenna, equipped with simultaneous dual X- and Ka-band transmitters and receivers, and a spacecraft component, which includes X- and Ka-band transponders to relay the transmitted signal back to Earth. Originally planned to be in 14-day orbits around Jupiter, a risk identified in the propulsion system led to the decision to stay in the 53-day orbit period. Rapid turnaround of observation planning led to successful near-term perijove passes. Although maintaining a 53-day orbit period provides a scientific benefit to the gravity science investigation, the longer orbit period further increases the large dynamic range in Doppler shift and pointing angles induced by the geometry of each perijove. Between entering orbit at Jupiter on July 5, 2016 and September 2017, the Juno spacecraft has executed eight closest approach periods every 53 days where science data was collected. The first five perijove passes were conducted in different telecom configurations, each presenting unique challenges in data collection and processing. Perijoves PJ-01, PJ-02, PJ-03, and PJ-06 utilized the high-gain antenna and various configurations of the X- and Ka-bands. Perijoves PJ-04 and PJ-05 utilized the medium-gain antenna at X-band only while the spacecraft was off-Earth point. Additional perijoves are planned every 53-days, with an additional five by March 2018. Lessons learned from operating and collecting data at each perijove are documented and will be utilized in future perijoves. Analysis of the first two gravity science perijoves has improved the precision of Jupiter’s gravity field by a factor of five, providing crucial constraints on the interior structure of Jupiter.

Buccino, Dustin↗
NASA NTRS1998

Lessons Learned During Implementation and Early Operations of the DS1 Beacon Monitor Experiment

A new approach to mission operations will be flight validated on NASA's New Millennium Program Deep Space One (DS1) mission which launched in October 1998. The Beacon Monitor Operations Technology is aimed at decreasing the total volume of downlinked engineering telemetry by reducing the frequency of downlink and the volume of data received per pass. Cost savings are achieved by reducing the amount of routine telemetry processing and analysis performed by ground staff. The technology is required for upcoming NASA missions to Pluto, Europa, and possibly some other missions. With beacon monitoring, the spacecraft will assess its own health and will transmit one of four beacon messages each representing a unique frequency tone to inform the ground how urgent it is to track the spacecraft for telemetry. If all conditions are nominal, the tone provides periodic assurance to ground personnel that the mission is proceeding as planned without having to receive and analyze downlinked telemetry. If there is a problem, the tone will indicate that tracking is required and the resulting telemetry will contain a concise summary of what has occurred since the last telemetry pass. The primary components of the technology are a tone monitoring technology, AI-based software for onboard engineering data summarization, and a ground response system. In addition, there is a ground visualization system for telemetry summaries. This paper includes a description of the Beacon monitor concept, the trade-offs with adapting that concept as a technology experiment, the current state of the resulting implementation on DS1, and our lessons learned during the initial checkout phase of the mission. Applicability to future missions is also included.

Sherwood, Rob↗
NASA NTRS2015

Lessons Learned from Optical Payload for Lasercomm Science (OPALS) Mission Operations

This paper provides an overview of Optical Payload for Lasercomm Science (OPALS) activities and lessons learned during mission operations. Activities described cover the periods of commissioning, prime, and extended mission operations, during which primary and secondary mission objectives were achieved for demonstrating space-to-ground optical communications. Lessons learned cover Mission Operations System topics in areas of: architecture verification and validation, staffing, mission support area, workstations, workstation tools, interfaces with support services, supporting ground stations, team training, procedures, flight software upgrades, post-processing tools, and public outreach.

Sindiy, Oleg V.↗
NASA NTRS1988

The efficacy of using human myoelectric signals to control the limbs of robots in space

This project was designed to investigate the usefulness of the myoelectric signal as a control in robotics applications. More specifically, the neural patterns associated with human arm and hand actions were studied to determine the efficacy of using these myoelectric signals to control the manipulator arm of a robot. The advantage of this approach to robotic control was the use of well-defined and well-practiced neural patterns already available to the system, as opposed to requiring the human operator to learn new tasks and establish new neural patterns in learning to control a joystick or mechanical coupling device.

Clark, Jane E.↗
NASA NTRS1991

Adjoint-operators and non-adiabatic learning algorithms in neural networks

Adjoint sensitivity equations are presented, which can be solved simultaneously (i.e., forward in time) with the dynamics of a nonlinear neural network. These equations provide the foundations for a new methodology which enables the implementation of temporal learning algorithms in a highly efficient manner.

Toomarian, N.↗
NASA NTRS1994

Planning and scheduling the Hubble Space Telescope: Practical application of advanced techniques

NASA's Hubble Space Telescope (HST) is a major astronomical facility that was launched in April, 1990. In late 1993, the first of several planned servicing missions refurbished the telescope, including corrections for a manufacturing flaw in the primary mirror. Orbiting above the distorting effects of the Earth's atmosphere, the HST provides an unrivaled combination of sensitivity, spectral coverage and angular resolution. The HST is arguably the most complex scientific observatory ever constructed and effective use of this valuable resource required novel approaches to astronomical observation and the development of advanced software systems including techniques to represent scheduling preferences and constraints, a constraint satisfaction problem (CSP) based scheduler and a rule based planning system. This paper presents a discussion of these systems and the lessons learned from operational experience.

Miller, Glenn E.↗
NASA NTRS1994

Two Nights on a Flying Observatory: A KAO Journal

We are the first participants in the "Science in the Stratosphere" program, sponsored by NASA and the University of Texas at Austin. The purpose of the program is to bring the techniques and excitement of modem astronomy into the classroom. Our job is to become familiar with the normal KAO operations, and learn from the staff and scientists during an actual observing flight. We are going to fly on missions to observe galactic and extragalactic star-forming regions in the far infrared region of the spectrum. All of our expectations spring from this simple working description. Little do we know, however, what really lies ahead for us!

Stryker, Pam↗
NASA NTRS2004

Heavy particle irradiation, neurochemistry and behavior: thresholds, dose-response curves and recovery of function

Exposure to heavy particles can affect the functioning of the central nervous system (CNS), particularly the dopaminergic system. In turn, the radiation-induced disruption of dopaminergic function affects a variety of behaviors that are dependent upon the integrity of this system, including motor behavior (upper body strength), amphetamine (dopamine)-mediated taste aversion learning, and operant conditioning (fixed-ratio bar pressing). Although the relationships between heavy particle irradiation and the effects of exposure depend, to some extent, upon the specific behavioral or neurochemical endpoint under consideration, a review of the available research leads to the hypothesis that the endpoints mediated by the CNS have certain characteristics in common. These include: (1) a threshold, below which there is no apparent effect; (2) the lack of a dose-response relationship, or an extremely steep dose-response curve, depending on the particular endpoint; and (3) the absence of recovery of function, such that the heavy particle-induced behavioral and neural changes are present when tested up to one year following exposure. The current report reviews the data relevant to the degree to which these characteristics are common to neurochemical and behavioral endpoints that are mediated by the effects of exposure to heavy particles on CNS activity. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

Review↗
NASA NTRS2005

STS-114 Flight Day 13 and 14 Highlights

On Flight Day 13, the crew of Space Shuttle Discovery on the STS-114 Return to Flight mission (Commander Eileen Collins, Pilot James Kelly, Mission Specialists Soichi Noguchi, Stephen Robinson, Andrew Thomas, Wendy Lawrence, and Charles Camarda) hear a weather report from Mission Control on conditions at the shuttle's possible landing sites. The video includes a view of a storm at sea. Noguchi appears in front of a banner for the Japanese Space Agency JAXA, displaying a baseball signed by Japanese MLB players, demonstrating origami, displaying other crafts, and playing the keyboard. The primary event on the video is an interview of the whole crew, in which they discuss the importance of their mission, lessons learned, shuttle operations, shuttle safety and repair, extravehicular activities (EVAs), astronaut training, and shuttle landing. Mission Control dedicates the song "A Piece of Sky" to the Shuttle crew, while the Earth is visible below the orbiter. The video ends with a view of the Earth limb lit against a dark background.

Source record↗
NASA NTRS2006

Aircraft Control Using Engine Thrust: A History of Learning TOC Real-Time

A history of learning the operation of Throttles Only Control (TOC) to control an aircraft in real time using engine thrust is shown. The topics include: 1) Past TOC Accidents/Incidents; 2) 1972: DC-10 American Airlines; 3) May 1974: USAF B-52H; 4) April 1975: USAF C-5A; 5) April 1975: USAF C-5A; 6) 1981: USAF B-52G; 7) August 1985: JAL 123 B-747; 8) JAL 123 Survivor Story; 9) JAL 123 Investigation Findings; 10) July 1989: UAL 232 DC-10; 11) UAL 232 DC-10; 12) Eastwind 517 B-737; 13) November 2003: DHL A-300; 14) Historically, TOC has saved lives; 15) Automated Throttles-Only Control; 16) PCA Project; 17) Propulsion-Controlled Aircraft; 18) MD-11 PCA System and Flight Test Envelope; 19) MD-11 Simulation, PCA ILS-Soupled Landing Dispersion; 20) Throttles-Only Pitch and Roll Control Power; 21) PCA in Commercial Fleet; 22) Fall 2005: PCAR Project; 23) PCAR Background - TOC; and 24) PCAR Background - TOC.

Cole, Jennifer H.↗
NASA NTRS2011

Lessons Learned for Planning and Estimating Operations Support Requirements

Operations (phase E) costs are typically small compared to the spacecraft development and test costs. This, combined with the long lead time for realizing operations costs, can lead projects to focus on hardware development schedules and costs, de-emphasizing estimation of operations support requirements during proposal, early design, and replan cost exercises. The Discovery and New Frontiers (D&NF) programs comprise small, cost-capped missions supporting scientific exploration of the solar system. Even moderate yearly underestimates of the operations costs can present significant LCC impacts for deep space missions with long operational durations, and any LCC growth can directly impact the programs ability to fund new missions. The D&NF Program Office at Marshall Space Flight Center recently studied cost overruns for 7 D&NF missions related to phase C/D development of operational capabilities and phase E mission operations. The goal was to identify the underlying causes for the overruns and develop practical mitigations to assist the D&NF projects in identifying potential operations risks and controlling the associated impacts to operations development and execution costs. The study found that the drivers behind these overruns include overly optimistic assumptions regarding the savings resulting from the use of heritage technology, late development of operations requirements, inadequate planning for sustaining engineering and the special requirements of long duration missions (e.g., knowledge retention and hardware/software refresh), and delayed completion of ground system development work. This presentation summarizes the study and the results, providing a set of lessons NASA can use to improve early estimation and validation of operations costs.

Newhouse, Marilyn↗