Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “autonomous execution”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Onboard Autonomy on the Earth Observing One Mission

The Earth Observing One Spacecraft is currently flying The Autonomous Sciencecraft Experiment (ASE) - onboard autonomy software to improve science return. The ASE software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. ASE includes software systems that perform science data analysis, mission planning, and run-time robust execution. In this article we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations.

autonomy↗
DOE OSTI2025

Secure API-Driven Research Automation to Accelerate Scientific Discovery

The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M enables secure and flexible programmatic access to high performance computing (HPC) resources. This framework allows intelligent agents and experimental facilities to dynamically provision resources and execute complex workflows, accelerating experimental lifecycles, and enabling AI-augmented autonomous science. S3M establishes a modern foundation for scientific computing infrastructure that significantly reduces traditional barriers between researchers, computational resources, and experimental facilities.

Skluzacek, Tyler [ORNL] (ORCID:0000000322424931)↗
NASA NTRS1983

A split delta-V technique for drift control of geosynchronous spacecraft

A split-delta-V technique is presented for the east-west stationkeeping of geosynchronous spacecraft using the linearized optimal transfer strategies for near-circular coplanar orbits. The maneuvers are carried out by taking advantage of the dynamics of the spacecraft in order to cancel out the effect of the drift accumulated between successive impulsive delta-V's. A stability analysis is then carried out in order to determine the effect of implementing a series of precanned maneuvers at the end of each drift cycle in an autonomous mode by taking into account initial orbit determination uncertainties and execution errors emanating from each delta-V. It is shown that small tolerance deadbands are violated after only a few cycles and the system is unstable.

Kechichian, F. A.↗
NASA NTRS2000

On-Board Real-Time State and Fault Identification for Rovers

For extended autonomous operation, rovers must identify potential faults to determine whether its execution needs to be halted or not. At the same time, rovers present particular challenges for state estimation techniques: they are subject to environmental influences that affect senior readings during normal and anomalous operation, and the sensors fluctuate rapidly both because of noise and because of the dynamics of the rover's interaction with its environment. This paper presents MAKSI, an on-board method for state estimation and fault diagnosis that is particularly appropriate for rovers. The method is based on a combination of continuous state estimation, wing Kalman filters, and discrete state estimation, wing a Markov-model representation.

Washington, Richard↗
NASA NTRS2011

Integration of the Reconfigurable Self-Healing eDNA Architecture in an Embedded System

In this work we describe the first real world case study for the self-healing eDNA (electronic DNA) architecture by implementing the control and data processing of a Fourier Transform Spectrometer (FTS) on an eDNA prototype. For this purpose the eDNA prototype has been ported from a Xilinx Virtex 5 FPGA to an embedded system consisting of a PowerPC and a Xilinx Virtex 5 FPGA. The FTS instrument features a novel liquid crystal waveguide, which consequently eliminates all moving parts from the instrument. The addition of the eDNA architecture to do the control and data processing has resulted in a highly fault-tolerant FTS instrument. The case study has shown that the early stage prototype of the autonomous self-healing eDNA architecture is expensive in terms of execution time.

Computer architecture↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission in 2003. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in multilayer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗
NASA NTRS2003

Wavelet-Based Real-Time Diagnosis of Complex Systems

A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning.

Gulati, Sandeep↗
DOE OSTI2024

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗
NASA NTRS2017

Utilization of the International Space Station for Crew Autonomous Scheduling Test (CAST)

The United States space policy is evolving toward missions beyond low Earth orbit. In an effort to meet that policy, NASA has recognized Autonomous Mission Operations (AMO) as a valuable capability. Identified within AMO capabilities is the potential for autonomous planning and replanning during human spaceflight operations. That is allowing crew members to collectively or individually participate in the development of their own schedules. Currently, dedicated mission operations planners collaborate with international partners to create daily plans for astronauts aboard the International Space Station (ISS), taking into account mission requirements, ground rules, and various vehicle and payload constraints. In future deep space operations the crew will require more independence from ground support due to communication transmission delays. Furthermore, crew members who are provided with the capability to schedule their own activities are able to leverage direct experience operating in the space environment, and possibly maximize their efficiency. CAST (Crew Autonomous Scheduling Test) is an ISS investigation designed to analyze three important hypotheses about crew autonomous scheduling. First, given appropriate inputs, the crew is able to create and execute a plan in a reasonable period of time without impacts to mission success. Second, the proximity of the planner, in this case the crew, to the planned operations increases their operational efficiency. Third, crew members are more satisfied when given a role in plan development. This paper presents the results from a single astronaut test subject who participated in five CAST sessions. The details on the operational philosophy of CAST are discussed, including the approach to crew training, selection criteria for test days, and data collection methods. CAST is a technology demonstration payload sponsored by the ISS Research Science and Technology Office, and performed by experts in Mission Operations Planning from the Flight Operations Directorate at NASA Johnson Space Center, and researchers across multiple NASA centers. It is hoped the results of this investigation will guide NASA's implementation of autonomous mission operations for long duration human space missions to Mars and beyond.

Healy, Matthew↗
NASA NTRS2014

Ground Operations Autonomous Control and Integrated Health Management

An intelligent autonomous control capability has been developed and is currently being validated in ground cryogenic fluid management operations. The capability embodies a physical architecture consistent with typical launch infrastructure and control systems, augmented by a higher level autonomous control (AC) system enabled to make knowledge-based decisions. The AC system is supported by an integrated system health management (ISHM) capability that detects anomalies, diagnoses causes, determines effects, and could predict future anomalies. AC is implemented using the concept of programmed sequences that could be considered to be building blocks of more generic mission plans. A sequence is a series of steps, and each executes actions once conditions for the step are met (e.g. desired temperatures or fluid state are achieved). For autonomous capability, conditions must consider also health management outcomes, as they will determine whether or not an action is executed, or how an action may be executed, or if an alternative action is executed instead. Aside from health, higher level objectives can also drive how a mission is carried out. The capability was developed using the G2 software environment (www.gensym.com) augmented by a NASA Toolkit that significantly shortens time to deployment. G2 is a commercial product to develop intelligent applications. It is fully object oriented. The core of the capability is a Domain Model of the system where all elements of the system are represented as objects (sensors, instruments, components, pipes, etc.). Reasoning and decision making can be done with all elements in the domain model. The toolkit also enables implementation of failure modes and effects analysis (FMEA), which are represented as root cause trees. FMEA's are programmed graphically, they are reusable, as they address generic FMEA referring to classes of subsystems or objects and their functional relationships. User interfaces for integrated awareness by operators have been created.

Figueroa, Fernando↗
NASA NTRS1989

Design and implementation of a robot control system with traded and shared control capability

Preliminary results are reported from efforts to design and develop a robotic system that will accept and execute commands from either a six-axis teleoperator device or an autonomous planner, or combine the two. Such a system should have both traded as well as shared control capability. A sharing strategy is presented whereby the overall system, while retaining positive features of teleoperated and autonomous operation, loses its individual negative features. A two-tiered shared control architecture is considered here, consisting of a task level and a servo level. Also presented is a computer architecture for the implementation of this system, including a description of the hardware and software.

Hayati, S.↗
NASA NTRS1997

STIS target acquisition

We describe the STIS autonomous target acquisition capabilities. We also present the results of dedicated tests executed as part of Cycle 7 calibration, following post-launch improvements to the Space Telescope Imaging Spectrograph (STIS) flight software. The residual pointing error from the acquisitions are < 0.5 CCD pixels, which is better than preflight estimates. Execution of peakups show clear improvement of target centering for slits of width 0.1 sec or smaller. These results may be used by Guest Observers in planning target acquisitions for their STIS programs.

Kraemer, Steve↗
NASA NTRS2007

Spatial Coverage Planning for Exploration Robots

A report discusses an algorithm for an onboard planning and execution technology to support the exploration and characterization of geological features by autonomous rovers. A rover that is capable of deciding which observations are more important relieves the engineering team from much of the burden of attempting to make accurate predictions of what the available rover resources will be in the future. Instead, the science and engineering teams can uplink a set of observation requests that may potentially oversubscribe resources and let the rover use observation priorities and its current assessment of available resources to make decisions about which observations to perform and when to perform them. The algorithm gives the rover the ability to model spatial coverage quality based on data from different scientific instruments, to assess the impact of terrain on coverage quality, to incorporate user-defined priorities among subregions of the terrain to be covered, and to update coverage quality rankings of observations when terrain knowledge changes. When the rover is exploring large geographical features such as craters, channels, or boundaries between two different regions, an important factor in assessing the quality of a mission plan is how the set of chosen observations spatially cover the area of interest. The algorithm allows the rover to evaluate which observation to perform and to what extent the candidate observation will increase the spatial coverage of the plan.

Gaines, Daniel↗
NASA NTRS2018

Autonomous Guidance Algorithms for NASA Learn-to-Fly Technology Development

Learn-to-Fly (L2F) is an advanced technology development effort under the NASA Transformative Aeronautics Concepts Program (TACP) that is aimed at assessing the feasibility of self-learning flight vehicles. Specifically, research has been conducted to demonstrate the potential to merge two enabling technologies; real-time aerodynamic modeling and adaptive controls, to substantially reduce the typical ground and flight testing requirements for air vehicle design. The approach to this effort involved development of unique airframes and on-board algorithms to demonstrate key L2F technologies on a fully autonomous flight test vehicle. This research, that included an aggressive flight test program, was intended to rapidly advance these technologies and demonstrate capabilities of the L2F approach. Key components of the L2F architecture include real-time aerodynamic modeling, adaptive controls and control allocation, and guidance. This paper provides an overview of the guidance algorithm which primarily served as an executive function to coordinate control commands for range navigation and the desired test conditions, provide autonomous envelope limiting/expansion and enable automatic landing to touchdown with no intervention from a human operator. A discussion of the L2F concept-of-operations and unique flight testing considerations, which influenced the guidance functional requirements, is included and results of recent flight testing are presented.

Foster, John V.↗
NASA NTRS1978

Correlation tracking for a Planetary Pointing and Tracking System

The Planetary Pointing and Tracking System (PPTS) is being developed to provide precision pointing for science platforms on future autonomous planetary spacecraft. The PPTS design approach using a CCD optical sensor for closed-loop control with respect to the target body, a gyro for inertial stabilization, and brushless dc torque motors for smooth and continuous platform articulation is essential for high resolution planetary imaging and automated science execution. An integral part of PPTS is the correlation tracker which has the potential to revolutionize autonomous guidance.

Assefi, T.↗
NASA NTRSDate not supplied

Distributed Spacecraft Autonomy - Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC) measurements. DSA will be demonstrated onboard the NASA Ames Starling mission – a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets. The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasmasphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input dataset for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software, aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements, which define the range of observability for the satellites, as well as exploitative measurements, which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth’s magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions. As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown. Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middleware provides reliable delivery, routing, and topic subscription features over User Datagram Protocol (UDP). Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarm scalability from 2 satellites to 100 satellites is shown.

Distributed Autonomy↗
NASA NTRS2020

Distributed Spacecraft Autonomy (DSA): Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC)measurements. DSA will be demonstrated on board the NASA Ames Starling mission a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets.The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasma sphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input data set for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software,aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements,which define the range of observability for the satellites, as well as exploitative measurements,which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth's magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions.As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown.Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middle ware provides reliable delivery, routing, and topic subscription features over User Data gram Protocol (UDP).Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarms calability from 2 satellites to 100 satellites is shown.

Fugate, Jason↗