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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Eyes on the Earth 3D

Eyes on the Earth 3D software gives scientists, and the general public, a realtime, 3D interactive means of accurately viewing the real-time locations, speed, and values of recently collected data from several of NASA's Earth Observing Satellites using a standard Web browser (climate.nasa.gov/eyes). Anyone with Web access can use this software to see where the NASA fleet of these satellites is now, or where they will be up to a year in the future. The software also displays several Earth Science Data sets that have been collected on a daily basis. This application uses a third-party, 3D, realtime, interactive game engine called Unity 3D to visualize the satellites and is accessible from a Web browser.

Kulikov, anton I.↗
NASA NTRS2015

Software Development: 3D Animations and Creating User Interfaces for Realistic Simulations

My fall 2015 semester was spent at the Lyndon B. Johnson Space Center working in the Integrated Graphics, Operations, and Analysis Laboratory (IGOAL). My first project was to create a video animation that could tell the story of OMICS. OMICS is a term being used in the field of biomedical science to describe the collective technologies that study biological systems, such as what makes up a cell and how it functions with other systems. In the IGOAL I used a large 23 inch Wacom monitor to draw storyboards, graphics, and line art animations. I used Blender as the 3D environment to sculpt, shape, cut or modify the several scenes and models for the video. A challenge creating this video was to take a term used in biomedical science and describe it in such a way that an 8th grade student can understand. I used a line art style because it would visually set the tone for what we thought was an educational style. In order to get a handle on the perspective and overall feel for the animation without overloading my workspace, I split up the 2 minute animation into several scenes. I used Blender's python scripting capabilities which allowed for the addition of plugins to add or modify tools. The scripts can also directly interact with the objects to create naturalistic patterns or movements. After collecting the rendered scenes, I used Blender's built-in video editing workspace to output the animation. My second project was to write software that emulates a physical system's interface. The interface was to simulate a boat, ROV, and winch system. Simulations are a time and cost effective way to test complicated data and provide training for operators without having to use expensive hardware. We created the virtual controls with 3-D Blender models and 2-D graphics, and then add functionality in C# using the Unity game engine. The Unity engine provides several essential behaviors of a simulator, such as the start and update functions. A framework for Unity, which was developed in the lab, provided a way to place the different widgets on the virtual console dock and have them resize correctly based on the window dimensions.. My task in this project was to create the controls and visualizations for the data coming in from the simulator for the boat portion of the project. I wrote a class for each control window to handle the functionality of that widget. I implemented 11 widgets that make up the ship portion of the simulator. The members of the lab were each masters of their craft and I'm glad I had the opportunity to learn from them. I learned to plan strategically so I could finish this project on time. I allotted time for storyboarding, development, and refinement. In regards to animating I learned to use modifiers like lattice, boolean and build deformers. I also learned how to animate with drivers, how to use the dope sheet, and how to use the graph editor. In coding I learned to limit the chances for bugs by privatizing functions that should be exclusive to their class. I learned how to use the GIT repository to commit, stash and pull the latest build. I learned a bit of everything because I had the chance to see the entire application development process from the artwork, to the implementation.

Gordillo, Orlando Enrique↗
NASA NTRS2017

Holographic Rovers: Augmented Reality and the Microsoft HoloLens

Augmented Reality is an emerging field in technology, and encompasses Head Mounted Displays, smartphone apps, and even projected images. HMDs include the Meta 2, Magic Leap, Avegant Light Field, and the Microsoft HoloLens, which is evaluated specifically. The Microsoft HoloLens is designed to be used as an AR personal computer, and is being optimized with that goal in mind. Microsoft allied with the Unity3D game engine to create an SDK for interested application developers that can be used in the Unity environment.

augmented reality↗
NASA NTRS2017

NASA's Hybrid Reality Lab: One Giant Leap for Full Dive

This presentation demonstrates how NASA is using consumer VR headsets, game engine technology and NVIDIA's GPUs to create highly immersive future training systems augmented with extremely realistic haptic feedback, sound, additional sensory information, and how these can be used to improve the engineering workflow. Include in this presentation is an environment simulation of the ISS, where users can interact with virtual objects, handrails, and tracked physical objects while inside VR, integration of consumer VR headsets with the Active Response Gravity Offload System, and a space habitat architectural evaluation tool. Attendees will learn how the best elements of real and virtual worlds can be combined into a hybrid reality environment with tangible engineering and scientific applications.

Delgado, Francisco J.↗
NASA NTRS2017

Incorporating Speech Recognition into a Natural User Interface

The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.

Chapa, Nicholas↗
NASA NTRSDate not supplied

A Framework for Sense and Follow Convoys for Collective Autonomous Mobility

Convoys are collections of vehicles that share similar velocity profiles. In the sense-and follow convoy construct, a vehicle can follow a member of an existing convoy resulting in convoy chaining. We prove conditions under which this can occur such that the resulting convoy chain is bounded and the velocities of all members converge to the leader velocity through locally distributed control algorithms. To assess the approach, we utilize a six degree-of-freedom rigid body vehicle dynamics model for each agent. A dynamic inversion based control law is used to track the desired trajectory generated by the convoy algorithms. To simulate the system, we utilize Unity, a cross-platform game engine, where 3-dimensional terrain, building, and vehicle data can be visualized. Numerous simulations are presented to illustrate the concept.

convoys↗
NASA NTRSDate not supplied

A Framework for Sense and Follow Convoys for Collective Autonomous Mobility

Convoys are collections of vehicles that share similar velocity profiles. In the sense-and follow convoy construct, a vehicle can follow a member of an existing convoy resulting in convoy chaining. We prove conditions under which this can occur such that the resulting convoy chain is bounded and the velocities of all members converge to the leader velocity through locally distributed control algorithms. To assess the approach, we utilize a six degree-of-freedom rigid body vehicle dynamics model for each agent. A dynamic inversion based control law is used to track the desired trajectory generated by the convoy algorithms. To simulate the system, we utilize Unity, a cross-platform game engine, where 3-dimensional terrain, building, and vehicle data can be visualized. Numerous simulations are presented to illustrate the concept. Link to video file below:

convoys↗
NASA NTRSDate not supplied

ARRISTOTLE Simulation Demo

NASA’s Katherine Johnson Independent Verification and Validation (NASA IV&V) Jon McBride Software Testing and Research (JSTAR) laboratory has developed the Advanced Risk Reduction Integrated Software Test and Operations Tri-program Lightweight Environment (ARRISTOTLE). ARRISTOTLE is a digital twin of NASA’s Artemis mission. ARRISTOLE executes the true SLS and Orion flight binaries integrated on a single laptop computer. This demo will showcase the execution of the full Artemis liftoff. Once airborne, an inflight anomaly will be injected, resulting in Orion detaching from the booster. The Launch Abort System (LAS) will safely bring Orion back to earth. The Unity game engine is used to visualize the flyout as flight software executes.

emulation↗
NASA NTRS2010

A Chess-Like Game for Teaching Engineering Students to Solve Large System of Simultaneous Linear Equations

Solving large (and sparse) system of simultaneous linear equations has been (and continues to be) a major challenging problem for many real-world engineering/science applications [1-2]. For many practical/large-scale problems, the sparse, Symmetrical and Positive Definite (SPD) system of linear equations can be conveniently represented in matrix notation as [A] {x} = {b} , where the square coefficient matrix [A] and the Right-Hand-Side (RHS) vector {b} are known. The unknown solution vector {x} can be efficiently solved by the following step-by-step procedures [1-2]: Reordering phase, Matrix Factorization phase, Forward solution phase, and Backward solution phase. In this research work, a Game-Based Learning (GBL) approach has been developed to help engineering students to understand crucial details about matrix reordering and factorization phases. A "chess-like" game has been developed and can be played by either a single player, or two players. Through this "chess-like" open-ended game, the players/learners will not only understand the key concepts involved in reordering algorithms (based on existing algorithms), but also have the opportunities to "discover new algorithms" which are better than existing algorithms. Implementing the proposed "chess-like" game for matrix reordering and factorization phases can be enhanced by FLASH [3] computer environments, where computer simulation with animated human voice, sound effects, visual/graphical/colorful displays of matrix tables, score (or monetary) awards for the best game players, etc. can all be exploited. Preliminary demonstrations of the developed GBL approach can be viewed by anyone who has access to the internet web-site [4]!

Nguyen, Duc T.↗
NASA NTRS2017

Space Technology: Game Changing Development Deep Space Engine (DSE) 100 lbf and 5 lbf Thruster Development and Qualification

Science mission studies require spacecraft propulsion systems that are high-performance, lightweight, and compact. Highly matured technology and low-cost, short development time of the propulsion system are also very desirable. The Deep Space Engine (DSE) 100-lbf thruster is being developed to meet these needs. The overall goal of this game changing technology project is to qualify the DSE thrusters along with 5-lbf attitude control thrusters for space flight and for inclusion in science and exploration missions. The aim is to perform qualification tests representative of mission duty cycles. Most exploration missions are constrained by mass, power and cost. As major propulsion components, thrusters are identified as high-risk, long-lead development items. NASA spacecraft primarily rely on 1960s' heritage in-space thruster designs and opportunities exist for reducing size, weight, power, and cost through the utilization of modern materials and advanced manufacturing techniques. Advancements in MON-25/MMH hypergolic bipropellant thrusters represent a promising avenue for addressing these deficiencies with tremendous mission enhancing benefits. DSE is much lighter and costs less than currently available thrusters in comparable thrust classes. Because MON-25 propellants operate at lower temperatures, less power is needed for propellant conditioning for in-space propulsion applications, especially long duration and/or deep-space missions. Reduced power results in reduced mass for batteries and solar panels. DSE is capable of operating at a wide propellant temperature range (between -22 F and 122 F) while a similar existing thruster operates between 45 F and 70 F. Such a capability offers robust propulsion operation as well as flexibility in design. NASA's Marshall Space Flight Center evaluated available operational Missile Defense Agency heritage thrusters suitable for the science and lunar lander propulsion systems.

Barnett, Gregory↗
NASA NTRS2012

Multi-Mission Simulation and Visualization for Real-Time Telemetry Display, Playback and EDL Event Reconstruction

he Jet Propulsion Laboratory's Entry, Descent and Landing (EDL) Reconstruction Task has developed a software system that provides mission operations personnel and analysts with a real time telemetry-based live display, playback and post-EDL reconstruction capability that leverages the existing high-fidelity, physics-based simulation framework and modern game engine-derived 3D visualization system developed in the JPL Dynamics and Real Time Simulation (DARTS) Lab. Developed as a multi-mission solution, the EDL Telemetry Visualization (ETV) system has been used for a variety of projects including NASA's Mars Science Laboratory (MSL), NASA'S Low Density Supersonic Decelerator (LDSD) and JPL's MoonRise Lunar sample return proposal.

Entry, Descent and Landing (EDL) Reconstruction↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. 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 created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRS2008

Distributed Observer Network

NASA s advanced visual simulations are essential for analyses associated with life cycle planning, design, training, testing, operations, and evaluation. Kennedy Space Center, in particular, uses simulations for ground services and space exploration planning in an effort to reduce risk and costs while improving safety and performance. However, it has been difficult to circulate and share the results of simulation tools among the field centers, and distance and travel expenses have made timely collaboration even harder. In response, NASA joined with Valador Inc. to develop the Distributed Observer Network (DON), a collaborative environment that leverages game technology to bring 3-D simulations to conventional desktop and laptop computers. DON enables teams of engineers working on design and operations to view and collaborate on 3-D representations of data generated by authoritative tools. DON takes models and telemetry from these sources and, using commercial game engine technology, displays the simulation results in a 3-D visual environment. Multiple widely dispersed users, working individually or in groups, can view and analyze simulation results on desktop and laptop computers in real time.

Source record↗
NASA NTRS2010

Distributed Observer Network

The Distributed Observer network (DON) is a NASA-collaborative environment that leverages game technology to bring three-dimensional simulations to conventional desktop and laptop computers in order to allow teams of engineers working on design and operations, either individually or in groups, to view and collaborate on 3D representations of data generated by authoritative tools such as Delmia Envision, Pro/Engineer, or Maya. The DON takes models and telemetry from these sources and, using commercial game engine technology, displays the simulation results in a 3D visual environment. DON has been designed to enhance accessibility and user ability to observe and analyze visual simulations in real time. A variety of NASA mission segment simulations [Synergistic Engineering Environment (SEE) data, NASA Enterprise Visualization Analysis (NEVA) ground processing simulations, the DSS simulation for lunar operations, and the Johnson Space Center (JSC) TRICK tool for guidance, navigation, and control analysis] were experimented with. Desired functionalities, [i.e. Tivo-like functions, the capability to communicate textually or via Voice-over-Internet Protocol (VoIP) among team members, and the ability to write and save notes to be accessed later] were targeted. The resulting DON application was slated for early 2008 release to support simulation use for the Constellation Program and its teams. Those using the DON connect through a client that runs on their PC or Mac. This enables them to observe and analyze the simulation data as their schedule allows, and to review it as frequently as desired. DON team members can move freely within the virtual world. Preset camera points can be established, enabling team members to jump to specific views. This improves opportunities for shared analysis of options, design reviews, tests, operations, training, and evaluations, and improves prospects for verification of requirements, issues, and approaches among dispersed teams.

Conroy, Michael↗