Search NASA⌕ Search

SEARCH · Search NASA

Results for “Operational Autonomy”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Advances in Orion's On-Orbit Guidance and Targeting System Architecture

NASA's manned spaceflight programs have a rich history of advancing onboard guidance and targeting technology. In order to support future missions, the guidance and targeting architecture for the Orion Multi-Purpose Crew Vehicle must be able to operate in complete autonomy, without any support from the ground. Orion's guidance and targeting system must be sufficiently flexible to easily adapt to a wide array of undecided future missions, yet also not cause an undue computational burden on the flight computer. This presents a unique design challenge from the perspective of both algorithm development and system architecture construction. The present work shows how Orion's guidance and targeting system addresses these challenges. On the algorithm side, the system advances the state-of-the-art by: (1) steering burns with a simple closed-loop guidance strategy based on Shuttle heritage, and (2) planning maneuvers with a cutting-edge two-level targeting routine. These algorithms are then placed into an architecture designed to leverage the advantages of each and ensure that they function in concert with one another. The resulting system is characterized by modularity and simplicity. As such, it is adaptable to the on-orbit phases of any future mission that Orion may attempt.

Scarritt, Sara K.↗
NASA NTRS2018

Model-Based Data Integration and Process Standardization Techniques for Fault Management: A Feasibility Study

This paper describes the theory and considerations in the application of model-based techniques to assimilate information from disjoint knowledge sources for performing NASA's Fault Management (FM)-related activities using the TEAMS® toolset. FM consists of the operational mitigation of existing and impending spacecraft failures. NASA's FM directives have both design-phase and operational-phase goals. This paper highlights recent studies by QSI and DST of the capabilities required in the TEAMS® toolset for conducting FM activities with the aim of reducing operating costs, increasing autonomy, and conforming to time schedules. These studies use and extend the analytic capabilities of QSI's TEAMS® toolset to conduct a range of FM activities within a centralized platform.

Haste, Deepak↗
NASA NTRSDate not supplied

AMO-EXPRESS-2.5: Crew Autonomy Onboard the International Space Station

NASA is committed to landing American astronauts, including the first woman and the next man, on the Moon by 2024. Currently, the crew cannot take on all functions performed by the ground today, so the future crews will need more automation to reduce the crew workload for future missions. Of significant importance for these missions is the balance between crew autonomy and vehicle automation. The Advanced Exploration Systems (AES) Autonomous Systems and Operations (ASO) Project has been investigating the ability to evaluate crew self-scheduling and activity monitoring for future space missions. The ASO project designed the Autonomous Mission and Operations- EXpedite the PRocessing of Experiments to Space Station Rack-2.5 (AMO-EXPRESS-2.5) payload to evaluate crew self-scheduling and activity monitoring. The AMO-EXPRESS-2.5 builds on the previous AMO-EXPRESS and AMO-EXPRESS-2.0 demonstrations on ISS. The AMO-EXPRESS-2.5 demonstration goals are to prove crew self-scheduling by planning through diagnosing systems expertise, failure detection, procedure recommendation and situational awareness. This paper will describe the development, test and execution results of the AMO-EXPRESS-2.5 demonstration, and will also outline the future planned development and operational efforts to enable autonomy for future deep space manned missions.

Brooke C. Allen↗
NASA NTRSDate not supplied

NASA Science Technology Development Programs for Ocean Worlds Exploration

The exploration of ocean worlds such as Europa and Enceladus supports NASA’s goal to search for life and potentially habitable regions elsewhere in the universe, and further promises to help us understand the origins, evolution, and limits of life on Earth. Over the past several years, NASA’s Planetary Science Division has funded several technology development programs to enable future surface and subsurface missions to ocean worlds. These programs include Instrument Concepts for Europa Exploration (ICEE), Concepts for Ocean Worlds Life Detection Technology (COLDTech), Scientific Exploration Subsurface Access Mechanism for Europa (SESAME), Applied Information Systems Research: Autonomous Robotics Research for Ocean Worlds (AISR:ARROW), and Astrodynamics in Support of Icy Worlds Missions. Tasks selected under these programs include the development of scientific instruments including seismometers, imagers, spectrometers, and organic analyzers, and platform technologies including drills, melt probes, through-ice communications, radiation-hard electronics, and autonomy for surface operations. This paper describes the objectives of each of these programs and provides a summary of the work that has been completed or is underway in each.

technology↗
NASA NTRSDate not supplied

NASA Science Technology Development Programs for Ocean Worlds Exploration

The exploration of icy satellites such as Europa and Enceladus supports NASA’s goal to search for life and potentially habitable regions elsewhere in the universe, and further promises to help us understand the origins, evolution, and limits of life on Earth. Over the past several years, NASA’s Planetary Science Division has funded several technology development programs to enable future surface and subsurface missions to ocean worlds. These programs include Instrument Concepts for Europa Exploration (ICEE), Concepts for Ocean Worlds Life Detection Technology (COLDTech), Scientific Exploration Subsurface Access Mechanism for Europa (SESAME), Applied Information Systems Research: Autonomous Robotics Research for Ocean Worlds (AISR:ARROW), and Astrodynamics in Support of Icy Worlds Missions. Tasks selected under these programs include the development of scientific instruments including seismometers, imagers, spectrometers, and organic analyzers, and also platform technologies including drills, melt probes, through-ice communications, radiation-hard electronics, and autonomy for surface operations. This paper describes the objectives of each of these programs and provides a summary of the work that has been completed or is underway in each.

science instruments↗
NASA NTRS2022

NASA Science Technology Development Programs for Ocean Worlds Exploration

The exploration of ocean worlds such as Europa and Enceladus supports NASA’s goal to search for life and potentially habitable regions elsewhere in the universe, and further promises to help us understand the origins, evolution, and limits of life on Earth. Over the past several years, NASA’s Planetary Science Division has funded several technology development programs to enable future surface and subsurface missions to ocean worlds. These programs include Instrument Concepts for Europa Exploration (ICEE), Concepts for Ocean Worlds Life Detection Technology (COLDTech), Scientific Exploration Subsurface Access Mechanism for Europa (SESAME), Applied Information Systems Research: Autonomous Robotics Research for Ocean Worlds (AISR:ARROW), and Astrodynamics in Support of Icy Worlds Missions. Tasks selected under these programs include the development of scientific instruments including seismometers, imagers, spectrometers, and organic analyzers, and platform technologies including drills, melt probes, through-ice communications, radiation-hard electronics, and autonomy for surface operations. This paper describes the objectives of each of these programs and provides a summary of the work that has been completed or is underway in each.

Nayar, Hari D↗
NASA NTRS2022

Psyche Project Implementation During the COVID Pandemic

In January 2017, “Psyche: Journey to a Metal World” was selected for implementation as part of NASA’s Discovery program. The Psyche mission will utilize electric propulsion with SPT-140 Hall thrusters to rendezvous and orbit the metal-rich asteroid (16) Psyche, in the main asteroid belt between Mars and Jupiter. The Psyche spacecraft requires no chemical propulsion and, when launched in 2022, will be the first mission to use Hall thrusters beyond lunar orbit. The Psyche spacecraft is a hybrid development with Jet Propulsion Laboratory (JPL)-provided deep-space avionics and communications equipment mounted on a high-heritage MAXAR (formerly Space Systems Loral) Solar-Electric Propulsion (SEP) Chassis, based on their 1300 series of GEO communications satellites. The spacecraft is equipped to support a suite of science instruments, as well as a demonstration of the Deep Space Optical Communications (DSOC) technology. The spacecraft has sufficient onboard resources, autonomy, redundancy, and operability to complete a 3.5-year cruise to 16 Psyche, followed by a 20-month campaign of science investigations while orbiting the asteroid.The mission’s early concept design and progress through Preliminary Design Review (PDR) has been described in previous work. The paper will cover the recent mission progress from the Critical Design Review (CDR) through the start of Spacecraft Environmental Testing, which took place during the COVID pandemic.The authors will highlight the successful remote collaboration between the major partners: ASU, JPL, MAXAR, and the Payload teams that led to the initiation of the Assembly, Test, Launch, Operations Phase (ATLO) in early March 2021. Emphasis will be placed on the effects that the COVID-19 pandemic had on the work environment over the last 16+ months, including challenges to delivery of flight hardware and test equipment. In addition to the COVID-19 challenges, other significant anomalies discovered during design and test will be described along with any impacts to the overall science capability of the mission.

Lord, Peter↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRS2019

The TASAR Project: Launching Aviation on an Optimized Route Toward Aircraft Autonomy

The Traffic Aware Strategic Aircrew Request (TASAR) concept applies onboard automation for the purpose of advising the pilot of route modifications that would be beneficial to the flight. Leveraging onboard computing platforms with connectivity to avionics and diverse data sources on and off the aircraft, TASAR introduces a new, powerful capability for in-flight trajectory management to the cockpit and its flight crew that is anticipated to induce a significant culture change in airspace operations. Flight crews empowered by TASAR and its derivative technologies could transform from today’s flight plan followers to proactive trajectory managers, taking an initial critical step towards increasing autonomy in the airspace system. TASAR was developed as a catalyst for operational autonomy, a future vision where the responsibilities and authorities of trajectory management reside with the aircraft operator and are distributed among participating aircraft, thus fulfilling a vision dating back decades and enabling a fully scalable airspace system. This NASA Technical Paper maps TASAR to its foundational vision and traces its research and development from initial concept generation to an operational evaluation by a U.S. airline in revenue service, the final stage before technology transfer and commercialization.

Wing, David J.↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Research in Support of m:N Operations

Since 2019, the NASA and industry partners have been involved in research focused on a novel paradigm for operations of remotely piloted aircraft. This paradigm involves multiple people sharing a fleet of multiple vehicles between them. Referred to as m:N (pronounced “em-to-en”), this configuration describes a ratio where m is the number of operators and N is the number of vehicles. Through force and asset multiplication, the m:N concept seeks to enable a scalable and resilient operation of remotely crewed vehicles. The primary means of obtaining such a robust operation is through allowing a flexible crew of variable size to dynamically attend to the needs of assets in while performing real-time operator workload management. It is in that sense that assets are shared between operators: as needed (such as in events of elevated workload) an operator in an m:N context can “handoff” the responsibility for some amount of assets, nh < N, to be absorbed by the m – 1 crew members on staff. At some time later, these nh assets could be returned to their original owner or they may be further distributed to other crew if called for by the mission. During this panel, I will elaborate on the research activity undertaken by the Human-Autonomy Teaming (HAT) Laboratory at NASA Ames Research Center over the previous three years. The studies conducted by the HAT Lab range from interviews with subject matter experts, a cognitive walkthrough, a task analysis, and two simulation experiments to-date. During experimentation, pilots made use of an advanced Ground Control Station developed by the HAT Lab and industry partners to simulate m:N operations in two large, metropolitan areas of Southern California: Los Angeles and San Diego. Further experimentation planned over the next few years. Results from our research to-date indicates that pilots of a moderately sized fleet of about a dozen remotely crewed aircraft adequately maintained safety performance and situation awareness of their aircraft, even when presented with unexpected situations of heightened workload.

multi-vehicle control↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations

An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes

Human-Autonomy Teaming↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations

An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes.

Human-Autonomy Teaming↗
DOE OSTI2020

Performance Evaluation of Augmented Teleoperation of Contact Manipulation Tasks

At Argonne National Laboratory, an enhanced telerobotic concept has been suggested by providing augmented virtual fixtures for precise and dexterous manipulation. The proposed technology incorporated a human-robot interface that can assist personnel on the processes of Deactivation and Decommissioning in sites of high radiation. It has been implemented on a dual-arm robot system, known as Baxter, by integrating the Robotic Operating System (ROS) with 3D sensors and visual-haptic operator interface. However, what remained before its practical utilization is the capability for precisely placing the virtual fixtures. To this end, the objectives involved camera calibration, which required hardware fabrication and setup, as well as modification and implementation of ROS. The goal of this Research and Development is to develop an augmented-reality- based operator interface for human-inclusive, intelligent, and autonomous robotic system for the Deactivation and Decommissioning (D and D) of radioactive sites. The objectives are to develop: Collaborative Robot; Multi-modal Mixed-Reality Human-Robot Interface; Intelligent and Autonomous Behaviors. In order to achieve this, Baxter's Head-Kinect camera and Left-Hand camera need to be calibrated to overlap the real environment with the simulated environment. Camera calibration, which is crucial for precisely placing the virtual fixtures, was successful after running the simulations. The real and simulated environments overlapped, permitting the utilization of augmented teleoperation and the dexterous manipulation of Baxter for D and D applications in nuclear sites. Future steps: Performance evaluation of augmented teleoperation of contact manipulation tasks: Multiple-Camera 3D Reconstruction and Automation. Oculus Interface: Augmented reality and machine learning for enhanced telerobotic operation (Augmented Tele-autonomy): Machine-learning techniques for complex and flexible autonomous robotic behaviors. Interactive remote operation of robotic systems. Mesh cutting with simulation and real robot.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2008

Lunar and Mars Exploration: The Autonomy Factor

Long duration space flight crews have relied heavily on almost constant communication with ground control mission support. Ground control teams provide vehicle status and system monitoring, while offering near real time support for specific tasks, emergencies, and ensuring crew health and well being. With extended exploration goals to lunar and Mars outposts, real time communication with ground control teams and the ground s ability to conduct mission monitoring will be very limited compared to the resources provided to current International Space Station (ISS) crews. An operational shift toward more autonomy and a heavier reliance on the crew to monitor their vehicle and operations will be required for these future missions. NASA s future exploration endeavors and the subsequent increased autonomy will require a shift in crew skill composition, i.e. engineer, doctor, mission specialist etc. and lead to new training challenges and mission scenarios. Specifically, operational and design changes will be necessary in many areas including: Habitat Infrastructure and Support Systems, Crew Composition, Training, Procedures and Mission Planning. This paper will specifically address how to apply ISS lessons learned to further use ISS as a test bed to address decreased amounts of ground support to achieve full autonomous operations for lunar and Mars missions. Understanding these lessons learned and applying them to current operations will help to address the future impacts of increased crew autonomy for the lunar and Mars outposts and pave the way for success in increasingly longer mission durations.

Rando, Cynthia M.↗
NASA NTRS2008

Autonomy and Sensor Webs: The Evolution of Mission Operations

Demonstration of these sensor web capabilities will enable fast responding science campaigns that combine spaceborne, airborne, and ground assets. Sensor webs will also require new operations paradigms. These sensor webs will be operated directly by scientists using science goals to control their instruments. We will explore these new operations architectures through a study of existing sensor web prototypes.

Sherwood, Rob↗
NASA NTRS2019

Prognostics As-A-Service: A Scalable Cloud Architecture for Prognostics

Comprehensive aircraft system health-state awareness is critical for maintaining safe, efficient growth in global operations, enabling higher levels of autonomy, and facilitating new forms of aviation. Maintainers, vehicle operators, air traffic controllers, dispatchers, pilots, autonomous systems, and other decision-makers must have reliable real-time knowledge of the vehicle health, the health of its critical composite systems, predictions of how health changes with time, and forecasts of how its capabilities change with health degradation to preserve safety and efficiency. Providing this information in a reliable manner in computationally constrained environments and across a wide range of vehicles and systems continues to be a challenge. This challenge can be partially resolved through cloud computing, where the execution of prognostic and diagnostic algorithms is performed on a network of remote servers hosted on the internet. NASA is developing a cloud computing service, Prognostics As-A-Service (PaaS), that explores the feasibility and challenges of cloud-enhanced prognostics. Though such a system has broad applicability, this research effort is focused on aviation applications.

Prognostics↗