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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.

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At least 451 records · Page 25

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NASA NTRS2012

An Autonomous Autopilot Control System Design for Small-Scale UAVs

This paper describes the design and implementation of a fully autonomous and programmable autopilot system for small scale autonomous unmanned aerial vehicle (UAV) aircraft. This system was implemented in Reflection and has flown on the Exploration Aerial Vehicle (EAV) platform at NASA Ames Research Center, currently only as a safety backup for an experimental autopilot. The EAV and ground station are built on a component-based architecture called the Reflection Architecture. The Reflection Architecture is a prototype for a real-time embedded plug-and-play avionics system architecture which provides a transport layer for real-time communications between hardware and software components, allowing each component to focus solely on its implementation. The autopilot module described here, although developed in Reflection, contains no design elements dependent on this architecture.

Ippolito, Corey↗

NASA NTRS2012

NASA Space Launch System Operations Strategy

The National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is charged with delivering a new capability for human and scientific exploration beyond Earth orbit. The SLS also will provide backup crew and cargo services to the International Space Station, where astronauts have been training for long-duration voyages to destinations such as asteroids and Mars. For context, the SLS will be larger than the Saturn V, providing 10 percent more thrust at liftoff in its initial 70 metric ton (t) configuration and 20 percent more in its evolved 130 t configuration. The SLS Program knows that affordability is the key to sustainability. This paper will provide an overview of its operations strategy, which includes initiatives to reduce both development and fixed costs by using existing hardware and infrastructure assets to meet a first launch by 2017 within the projected budget. It also has a long-range plan to keep the budget flat using competitively selected advanced technologies that offer appropriate return on investment. To arrive at the launch vehicle concept, the SLS Program conducted internal engineering and business studies that have been externally validated by industry and reviewed by independent assessment panels. A series of design reference missions has informed the SLS operations concept, including launching the Orion Multi-Purpose Crew Vehicle on an autonomous demonstration mission in a lunar flyby scenario in 2017, and the first flight of a crew on Orion for a lunar flyby in 2021. Additional concepts address the processing of very large payloads, using a series of modular fairings and adapters to flexibly configure the rocket for the mission. This paper will describe how the SLS, Orion, and 21st Century Ground Systems programs are working together to create streamlined, affordable operations for sustainable exploration.

Singer, Joan A.↗

NASA NTRS2012

NASA's Space Launch System: A Flagship for Exploration Beyond Earth's Orbit

The National Aeronautics and Space Administration s (NASA) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is making progress toward delivering a new capability for exploration beyond Earth orbit in an austere economic climate. This fact drives the SLS team to find innovative solutions to the challenges of designing, developing, fielding, and operating the largest rocket in history. To arrive at the current SLS plan, government and industry experts carefully analyzed hundreds of architecture options and arrived at the one clear solution to stringent requirements for safety, affordability, and sustainability over the decades that the rocket will be in operation. This paper will explore ways to fit this major development within the funding guidelines by using existing engine assets and hardware now in testing to meet a first launch by 2017. It will explain the SLS Program s long-range plan to keep the budget within bounds, yet evolve the 70 metric ton (t) initial lift capability to 130-t lift capability after the first two flights. To achieve the evolved configuration, advanced technologies must offer appropriate return on investment to be selected through a competitive process. For context, the SLS will be larger than the Saturn V that took 12 men on 6 trips for a total of 11 days on the lunar surface over 4 decades ago. Astronauts train for long-duration voyages on the International Space Station, but have not had transportation to go beyond Earth orbit in modern times, until now. NASA is refining its mission manifest, guided by U.S. Space Policy and the Global Exploration Roadmap. Launching the Orion Multi-Purpose Cargo Vehicle s first autonomous certification flight in 2017, followed by a crewed flight in 2021, the SLS will offer a robust way to transport international crews and the air, water, food, and equipment they need for extended trips to asteroids, Lagrange Points, and Mars. In addition, the SLS will accommodate high-priority science experiments. SLS affordability initiatives include streamlining interfaces, applying risk-based insight into contracted work, centralizing systems engineering and integration, and nurturing a learning culture that continually benchmarks its performance against successful ventures. As this paper will explain, the SLS is making measurable progress toward becoming a global infrastructure asset for robotic and human scouts of all nations by harnessing business and technological innovations to deliver sustainable solutions for space exploration.

May, Todd↗

NASA NTRS2013

The Need for Intelligent Control of Space Power Systems

As manned spacecraft venture farther from Earth, the need for reliable, autonomous control of vehicle subsystems becomes critical. This is particularly true for the electrical power system which is critical to every other system. Autonomy can not be achieved by simple scripting techniques due to the communication latency times and the difficulty associated with failures (or combinations of failures) that need to be handled in as graceful a manner as possible to ensure system availability. Therefore an intelligent control system must be developed that can respond to disturbances and failures in a robust manner and ensure that critical system loads are served and all system constraints are respected.

May, Ryan David↗

NASA NTRS2012

Development of Field Excavator with Embedded Force Measurement

A semi-intelligent excavation mechanism was developed for use with the NASA-built Centaur 2 rover prototype. The excavator features a continuously rotatable large bucket supported between two parallel arms, both of which share a single pivot axis near the excavator base attached to the rover. The excavator is designed to simulate the collection of regolith, such as on the Moon, and to dump the collected soil into a hopper up to one meter tall for processing to extract oxygen. Because the vehicle can be autonomous and the terrain is generally unknown, there is risk of damaging equipment or using excessive power when attempting to extract soil from dense or rocky terrain. To minimize these risks, it is critical for the rover to sense the digging forces and adjust accordingly. It is also important to understand the digging capabilities and limitations of the excavator. This paper discusses the implementation of multiple strain gages as an embedded force measurement system in the excavator's arms. These strain gages can accurately measure and resolve multi-axial forces on the excavator. In order to validate these sensors and characterize the load capabilities, a series of controlled excavation tests were performed at Glenn Research Center with the excavator at various depths and cut angles while supported by a six axis load cell. The results of these tests are both compared to a force estimation model and used for calibration of the embedded strain gages. In addition, excavation forces generated using two different types of bucket edge (straight vs. with teeth) were compared.

Soils↗

NASA NTRS2016

Fast Kalman Filtering for Relative Spacecraft Position and Attitude Estimation for the Raven ISS Hosted Payload

The Raven ISS Hosted Payload will feature several pose measurement sensors on a pan/tilt gimbal which will be used to autonomously track resupply vehicles as they approach and depart the International Space Station. This paper discusses the derivation of a Relative Navigation Filter (RNF) to fuse measurements from the different pose measurement sensors to produce relative position and attitude estimates. The RNF relies on relative translation and orientation kinematics and careful pose sensor modeling to eliminate dependence on orbital position information and associated orbital dynamics models. The filter state is augmented with sensor biases to provide a mechanism for the filter to estimate and mitigate the offset between the measurements from different pose sensors.

estimation↗

NASA NTRS2016

Fast Kalman Filtering for Relative Spacecraft Position and Attitude Estimation for the Raven ISS Hosted Payload

estimation↗

NASA NTRS2016

Traveler: Trustworthy Autonomy

This presentation discusses the FAA belief that this research may hold the key to enabling safe autonomous operation of vehicles.

complex systems↗

NASA NTRS2015

Simulations of the Hazard Detection System for Approach Trajectories of the Morpheus Lunar Lander

The Hazard Detection System is part of a suite of sensors and algorithms designed to autonomously land a vehicle on unknown terrain while avoiding any hazards. This paper describes the simulations built to predict the performance of the Hazard Detection System to support flight testing onboard the Morpheus Lander testbed at the Kennedy Space Center. The paper describes a hardware-in-the-loop simulation that was used to predict system performance under nominal operating conditions, and also a Monte Carlo simulation to predict command timing performance bounds under a wide range of varying conditions.

Monte Carlo↗

NASA NTRS2016

Serious Gaming for Test & Evaluation of Clean-Slate (Ab Initio) National Airspace System (NAS) Designs

Incremental approaches to air transportation system development inherit current architectural constraints, which, in turn, place hard bounds on system capacity, efficiency of performance, and complexity. To enable airspace operations of the future, a clean-slate (ab initio) airspace design(s) must be considered. This ab initio National Airspace System (NAS) must be capable of accommodating increased traffic density, a broader diversity of aircraft, and on-demand mobility. System and subsystem designs should scale to accommodate the inevitable demand for airspace services that include large numbers of autonomous Unmanned Aerial Vehicles and a paradigm shift in general aviation (e.g., personal air vehicles) in addition to more traditional aerial vehicles such as commercial jetliners and weather balloons. The complex and adaptive nature of ab initio designs for the future NAS requires new approaches to validation, adding a significant physical experimentation component to analytical and simulation tools. In addition to software modeling and simulation, the ability to exercise system solutions in a flight environment will be an essential aspect of validation. The NASA Langley Research Center (LaRC) Autonomy Incubator seeks to develop a flight simulation infrastructure for ab initio modeling and simulation that assumes no specific NAS architecture and models vehicle-to-vehicle behavior to examine interactions and emergent behaviors among hundreds of intelligent aerial agents exhibiting collaborative, cooperative, coordinative, selfish, and malicious behaviors. The air transportation system of the future will be a complex adaptive system (CAS) characterized by complex and sometimes unpredictable (or unpredicted) behaviors that result from temporal and spatial interactions among large numbers of participants. A CAS not only evolves with a changing environment and adapts to it, it is closely coupled to all systems that constitute the environment. Thus, the ecosystem that contains the system and other systems evolves with the CAS as well. The effects of the emerging adaptation and co-evolution are difficult to capture with only combined mathematical and computational experimentation. Therefore, an ab initio flight simulation environment must accommodate individual vehicles, groups of self-organizing vehicles, and large-scale infrastructure behavior. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ab initio simulation environment is similar to online gaming environments in which player participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual player's active participation.

Allen, B. Danette↗

NASA NTRS2018

Orion Optical Navigation Progress Toward Exploration Mission 1

Optical navigation of human spacecraft was proposed on Gemini and implemented successfully on Apollo as a means of autonomously operating the vehicle in the event of lost communication with controllers on Earth. The Orion emergency return system utilizing optical navigation has matured in design over the last several years, and is currently undergoing the final implementation and test phase in preparation for Exploration Mission 1 (EM-1) in 2019. The software development is past its Critical Design Review, and is progressing through test and certification for human rating. The filter architecture uses a square-root-free UDU covariance factorization. Linear Covariance Analysis (LinCov) was used to analyze the measurement models and the measurement error models on a representative EM-1 trajectory. The Orion EM-1 flight camera was calibrated at the Johnson Space Center (JSC) electro-optics lab. To permanently stake the focal length of the camera a 500 mm focal length refractive collimator was used. Two Engineering Design Unit (EDU) cameras and an EDU star tracker were used for a live-sky test in Denver. In-space imagery with high-fidelity truth metadata is rare so these live-sky tests provide one of the closest real-world analogs to operational use. A hardware-in-the-loop test rig was developed in the Johnson Space Center Electro-Optics Lab to exercise the OpNav system prior to integrated testing on the Orion vehicle. The software is verified with synthetic images. Several hundred off-nominal images are also used to analyze robustness and fault detection in the software. These include effects such as stray light, excess radiation damage, and specular reflections, and are used to help verify the tuning parameters chosen for the algorithms such as earth atmosphere bias, minimum pixel intensity, and star detection thresholds.

Holt, Greg N.↗

NASA NTRS2019

Real-Time Path Planning for Multi-copters flying in UTM -TCL4

NASA's UAS Traffic management (UTM) -TCL-4 focuses on safely enabling large scale small UAS oper- ations in low altitude airspace in dense urban regions. This paper presents an operational architecture of an autonomous unmanned aerial vehicle operating in TCL4. An on-line path planning scheme is proposed which can effectively plan for feasible paths in real time with TCL-4 constraints. An end to end system is designed and tested in high fidelity Reflection architecture which demonstrates the feasibility of the approach.

Chakrabarty, Anjan↗

NASA NTRSDate not supplied

2018 Workshop on Autonomy for Future NASA Science Missions

Autonomy is changing our world; commercial enterprises and academic institutions are developing and deploying drones, robots, self-driving vehicles and other autonomous capabilities to great effect here on Earth. Autonomous technologies will also play a critical and enabling role in future NASA science missions, and the Agency requires a specific strategy to leverage these advances and infuse them into its missions. To address this need, NASA sponsored the 2018 Workshop on Autonomy for NASA Science Missions, held at Carnegie Mellon University, October 10-11, 2018.

Florence W Tan↗

NASA NTRS2020

Material Remodelling and Unconventional Gaits Facilitate Locomotion of a Robophysical Rover Over Granular Terrain

Autonomous robots and vehicles must occasionally recover from locomotion failure in loosely consolidated granular terrain. Recent mobility challenges led NASA Johnson Space Center to develop a prototype robotic lunar rover Resource Prospector 15 (RP15) capable of wheeled, legged, and crawling behavior. To systematically understand performance of such a device, we developed a scaled-down rover robot and studied its locomotion on slopes of dry and wet granular media. Addition of a cyclic legged gait to the robot’s wheel spinning action changes the robot dynamics from that of a wheeled vehicle to a locomotor paddling through frictional fluid. Granular drag force measurements and modified Resistive Force Theory facilitate modeling of such dynamics. A peculiar gait strategy that agitates and cyclically reflows grains under the robot allows it to ‘swim’ up loosely consolidated hills. Whereas substrate disturbance typically hinders locomotion in granular media, the multi-mode design of RP15 and a diversity of possible gaits facilitate formation of self-organized localized frictional fluids that enable effective robust transport.

Siddharth Shrivastava↗

NASA NTRS2022

IRIS: High-fidelity Perception Sensor Modeling for Closed-Loop Planetary Simulations

Perception plays a key role in autonomous and semi-autonomous planetary exploration vehicles. For instance, landers can use computer vision techniques for identifying safe landing locations, aerial vehicles use cameras as navigation sensors, and planetary rovers use them for localization and hazard detection. Engineering simulations of such systems requires the accurate modeling of perception and vision sensors for simulating autonomy scenarios. In addition, the modeling of sensors for landers, aerial and ground vehicles requires the ability to handle large and high-resolution terrains, the accurate modeling of illumination, hi-fidelity rendering via ray/path tracing and the inclusion of sensor characteristics. Vision sensor models strive to simulate sensor reality by using physics principles to model the interaction of light and objects. Furthermore, high frame rate performance is highly desirable for in-the-loop simulations involving vehicle dynamics and control software. In this paper we describe a new sensor modeling capability called Inter-planetary Rendering for Imaging and Sensors (IRIS) that meets these requirements for the real-time and high-fidelity simulation of vision sensors for planetary aerospace and robotics applications.

Elmquist, Asher↗

NASA NTRSDate not supplied

A Systems Approach to AI Model Integration and Performance Evaluation for the Generic UAM Simulation Framework

This paper introduces py-guam, an open-source experimentation framework developed for the NASA Generic Urban Air Mobility simulation (GUAM) environment, facilitating the integration and evaluation of advanced artificial intelligence (AI) algorithms. We present a systems approach which enables the seamless incorporation of data-driven models, including off-nominal and failure state detection, into the GUAM’s Cognitive Architecture (CA). The framework supports customizable experimentation parameters, derives Safety Performance Indicators (SPIs) from UL 4600 safety case analyses, and employs rapid UAM simulations to assess AI impacts on flight performance across diverse scenarios. Through comprehensive testing and validation experiments, we demonstrate GUAM’s capability to enhance safety and efficiency in urban air mobility operations. Additionally, the open-source nature of py-guam fosters community collaboration, ensuring continuous improvement and adaptability to evolving technological advancements. This work establishes a robust tool for developing and testing AI-driven urban air mobility (UAM) systems, advancing the safety and reliability of autonomous urban air vehicles.

Artificial Intelligence↗

NASA NTRS2018

Autonomous Power Controller for the NASA Gateway

Intelligent autonomous control of a spacecraft is an enabling technology that must be developed for deep space human exploration. NASA's current long term human space platform, the International Space Station which is in Low Earth Orbit, is in almost continuous communication with ground based mission control. This allows near real-time operation of all the vehicle core systems, including power, from the ground. As the focus shifts from Low Earth Orbit to deep space, challenges associated with communication time-lag and bandwidth limitations beyond geosynchronous orbit do not permit this type of ground based operation. These communication limitations motivate autonomous vehicle operations, including the vehicle subsystems such as power. This presentation will describe the ongoing development of an Autonomous Power Control (APC) system that can be used for a deep space exploration spacecraft. This work extends previous work on developing an autonomous power control which includes developing a control architecture for deep space vehicles, the use of software agents, and constructing a control simulation lab for demonstrating this capability. This presentation will begin with a discussion of the representative future power architectures that will be required for deep space exploration vehicles. The presentation then will describe how the power controller will integrate with the vehicle followed with a discussion of the autonomous power controller. Next it will describe the test setup used to evaluate the performance of the system and lastly show some of the test results. To develop the type of controller envisioned, it will be necessary to employ a detailed real-time simulation to evaluate its performance and ultimately verify its functionality.

Csank, Jeffrey↗

NASA NTRS2000

Computer-implemented method and apparatus for autonomous position determination using magnetic field data

A computer-implemented method and apparatus for determining position of a vehicle within 100 km autonomously from magnetic field measurements and attitude data without a priori knowledge of position. An inverted dipole solution of two possible position solutions for each measurement of magnetic field data are deterministically calculated by a program controlled processor solving the inverted first order spherical harmonic representation of the geomagnetic field for two unit position vectors 180 degrees apart and a vehicle distance from the center of the earth. Correction schemes such as a successive substitutions and a Newton-Raphson method are applied to each dipole. The two position solutions for each measurement are saved separately. Velocity vectors for the position solutions are calculated so that a total energy difference for each of the two resultant position paths is computed. The position path with the smaller absolute total energy difference is chosen as the true position path of the vehicle.

Ketchum, Eleanor A.↗