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NASA NTRSDate not supplied

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

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

machine learning↗

NASA NTRS2021

Automated Uas Measurements of Reflectance and Solar Induced Florescence (SIF) for Assessment Of the Dinamics in Photosynthetic Function, Application for Maze (Zea Mays L.) in Greenbelt, Maryland, Us

For accurate assessment of vegetation function there is a strong need for advancement in the remote sensing methods and instruments, to enable monitoring of the parameters governing photosynthesis at the temporal frequency relevant to their dynamics and at a spatial scale that allows practical assessment and management. Currently, no single sensor can provide data at the desired high temporal, spectral and spatial resolutions. Our field measurements obtained using the integrated UAS Piccolo system during the summers of2017 and 2018 demonstrate that science quality reflectance and solar induced fluorescence (SIF) data can be retrieved with high temporal frequency using small Unmanned Aerial Systems (UAS). The implemented approach facilitates data comparisons through space and time, and the integration with other spectral satellite and airborne data. This investigation contributes for bridging the gap in Earth observation between field and airborne measurements by implementing autonomous methods for obtaining high temporal frequency spectroscopy measurements from an Unmanned Aerial Systems (UAS) at various illumination conditions. An advancement in the Intelligent Payload Module (IPM) facilitated the implementation of an optimization workflow to collect spectral data for characterizing vegetation reflectance and solar induced fluorescence (SIF).

Petya Campbell↗

NASA NTRSDate not supplied

NASA Earth System Digital Twins (ESDT) For a Sustainable Future

An Earth System Digital Twin (or ESDT) is an Information System for Understanding, Forecasting, and Conjecturing the complex interconnections among Earth systems, including anthropomorphic forcings and impacts to humanity. More specifically, an Earth System Digital Twin is a dynamic and interactive information system that first provides a digital replica of the past and current states of the Earth or Earth system, as accurately and timely as possible (i.e., the “What Now”); second allows for computing forecasts of future states under nominal assumptions and based on the current replica (i.e., the “What Next”); and third offers the capability to investigate many hypothetical scenarios under varying impact assumptions (i.e., the “What If”). The full power of digital twins is that, through an integrated representation and standardized tools and software technologies, the same digital replica can address the needs of multiple users at various resolutions (spatial and temporal) and for various applications (science, economic, policy, etc.) – “from farmer to scientist”. Since 2020, NASA’s Earth Science Technology’s Intelligent Systems Programs have started defining and investigating ESDTs. After several workshops, more than 15 projects have been funded to develop technologies and components as well as a few prototype ESDT systems. An ESDT architecture framework has been defined, and it is now being used to develop a Coastal Zone Digital Twin (CZ-DT), in coordination with NOAA and CNES. Additional projects have also just been selected to build additional ESDT technologies and prototypes. The details of these Programs will be detailed at the conference.

Earth Science Remote Sensing; Information Systems↗

NASA NTRSDate not supplied

A Framework for Assessment of Autonomy Challenges in Air Traffic Management

Traditionally, air traffic management services have been provided by air traffic controllers and managers stationed in ground facilities, employed or contracted by the public sector, and supported by automation. These centralized, human-centric air traffic management services do not scale to accommodate increasing demands from conventional and new entrant operations for access to the national airspace system. One transformation that provides much needed scalability is increasing the level of autonomy of air traffic management by enabling edge agents of the system, including vehicles, operators, and third-party service suppliers, to collectively self-manage independently from the centralized service providers and enabling the automation to also take on more independent traffic management responsibility from the human agents. This paper identifies challenges to increasing the level of autonomy of air traffic management services. It describes a framework to enable a systematic identification of these challenges. The framework consists of a functional breakdown of air traffic management services and several dimensions characterizing different autonomy scales. The autonomy dimensions include the automation level between human and machine agents, the locus of control between centralized and distributed edge agents, cognitive activities for autonomous situation awareness and decision making, intelligence levels ranging from skill-based to expertise-based autonomous behavior, and uncertainty levels of the dynamics and environment in which autonomous agents operate. Several challenges are identified and categorized using the different dimensions of the autonomy framework.

automation, autonomy framework, collective autonom↗

NASA NTRS2006

Radiation Tolerant Intelligent Memory Stack (RTIMS)

The Radiation Tolerant Intelligent Memory Stack (RTIMS), suitable for both geostationary and low earth orbit missions, has been developed. The memory module is fully functional and undergoing environmental and radiation characterization. A self-contained flight-like module is expected to be completed in 2006. RTIMS provides reconfigurable circuitry and 2 gigabits of error corrected or 1 gigabit of triple redundant digital memory in a small package. RTIMS utilizes circuit stacking of heterogeneous components and radiation shielding technologies. A reprogrammable field programmable gate array (FPGA), six synchronous dynamic random access memories, linear regulator, and the radiation mitigation circuitries are stacked into a module of 42.7mm x 42.7mm x 13.00mm. Triple module redundancy, current limiting, configuration scrubbing, and single event function interrupt detection are employed to mitigate radiation effects. The mitigation techniques significantly simplify system design. RTIMS is well suited for deployment in real-time data processing, reconfigurable computing, and memory intensive applications.

Ng, Tak-kwong↗

NASA NTRS2006

Use of Virtual Mission Operations Center Technology to Achieve JPDO's Virtual Tower Vision

The Joint Program Development Office has proposed that the Next Generation Air Transportation System (NGATS) consolidate control centers. NGATS would be managed from a few strategically located facilities with virtual towers and TRACONS. This consolidation is about combining the delivery locations for these services not about decreasing service. By consolidating these locations, cost savings in the order of $500 million have been projected. Evolving to spaced-based communication, navigation, and surveillance offers the opportunity to reduce or eliminate much of the ground-based infrastructure cost. Dynamically adjusted airspace offers the opportunity to reduce the number of sectors and boundary inconsistencies; eliminate or reduce "handoffs;" and eliminate the distinction between Towers, TRACONS, and Enroute Centers. To realize a consolidation vision for air traffic management there must be investment in networking. One technology that holds great potential is the use of Virtual Mission Operations Centers to provide secure, automated, intelligent management of the NGATS. This paper provides a conceptual framework for incorporating VMOC into the NGATS.

Ivancic, William D.↗

NASA NTRS2001

Adjustable Autonomy Testbed

The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.

Malin, Jane T.↗

NASA NTRS2009

Convective Weather Avoidance with Uncertain Weather Forecasts

Convective weather events have a disruptive impact on air traffic both in terminal area and in en-route airspaces. In order to make sure that the national air transportation system is safe and efficient, it is essential to respond to convective weather events effectively. Traffic flow control initiatives in response to convective weather include ground delay, airborne delay, miles-in-trail restrictions as well as tactical and strategic rerouting. The rerouting initiatives can potentially increase traffic density and complexity in regions neighboring the convective weather activity. There is a need to perform rerouting in an intelligent and efficient way such that the disruptive effects of rerouting are minimized. An important area of research is to study the interaction of in-flight rerouting with traffic congestion or complexity and developing methods that quantitatively measure this interaction. Furthermore, it is necessary to find rerouting solutions that account for uncertainties in weather forecasts. These are important steps toward managing complexity during rerouting operations, and the paper is motivated by these research questions. An automated system is developed for rerouting air traffic in order to avoid convective weather regions during the 20- minute - 2-hour time horizon. Such a system is envisioned to work in concert with separation assurance (0 - 20-minute time horizon), and longer term air traffic management (2-hours and beyond) to provide a more comprehensive solution to complexity and safety management. In this study, weather is dynamic and uncertain; it is represented as regions of airspace that pilots are likely to avoid. Algorithms are implemented in an air traffic simulation environment to support the research study. The algorithms used are deterministic but periodically revise reroutes to account for weather forecast updates. In contrast to previous studies, in this study convective weather is represented as regions of airspace that pilots are likely to avoid. The automated system periodically updates forecasts and reassesses rerouting decisions in order to account for changing weather predictions. The main objectives are to reroute flights to avoid convective weather regions and determine the resulting complexity due to rerouting. The eventual goal is to control and reduce complexity while rerouting flights during the 20 minute - 2 hour planning period. A three-hour simulation is conducted using 4800 flights in the national airspace. The study compares several metrics against a baseline scenario using the same traffic and weather but with rerouting disabled. The results show that rerouting can have a negative impact on congestion in some sectors, as expected. The rerouting system provides accurate measurements of the resulting complexity in the congested sectors. Furthermore, although rerouting is performed only in the 20-minute - 2-hour range, it results in a 30% reduction in encounters with nowcast weather polygons (100% being the ideal for perfectly predictable and accurate weather). In the simulations, rerouting was performed for the 20-minute - 2-hour flight time horizon, and for the en-route segment of air traffic. The implementation uses CWAM, a set of polygons that represent probabilities of pilot deviation around weather. The algorithms were implemented in a software-based air traffic simulation system. Initial results of the system's performance and effectiveness were encouraging. Simulation results showed that when flights were rerouted in the 20-minute - 2-hour flight time horizon of air traffic, there were fewer weather encounters in the first 20 minutes than for flights that were not rerouted. Some preliminary results were also obtained that showed that rerouting will also increase complexity. More simulations will be conducted in order to report conclusive results on the effects of rerouting on complexity. Thus, the use of the 20-minute - 2-hour flight time horizon weather avoidance teniques performed in the simulation is expected to provide benefits for short-term weather avoidance.

Karahan, Sinan↗

NASA NTRSDate not supplied

Parametric Mechanism Design Through Numerical Optimization and Physics Simulation

Design-Build-Test approaches for developing spaceflight hardware are prohibitively time and cost intensive and often lead to suboptimal mechanism designs. Approaches that couple machine learning and high-fidelity physics simulation could eliminate the need for hardware prototyping and dramatically accelerate the engineering design cycle, ultimately reducing cost. This work presents a modular NASA-developed toolchain to optimize hardware mechanisms in a virtual environment using numerical optimization and multi-body physics simulation. The toolchain enables multi-objective optimization, generates parametric CAD files that can be further post-processed by an end user, and can be expanded to optimize full systems and non-mechanical parameters such as feedback control variables. We demonstrate the toolchain through an independently verifiable design problem that optimizes wheel radius to achieve a desired linear velocity in a rigid-body physics environment when the wheel rotates at a constant angular speed, and then post-process the parametric CAD file of the optimal design generated by the tool before ultimately manufacturing it via 3D printing. We end with a discussion of how the toolchain can incorporate other analysis tools, including finite element analysis, computational fluid dynamics, and granular media simulations.

Optimization↗

NASA NTRSDate not supplied

An Optimization-Based Toolchain for Parametric Mechanism Design

Optimization↗

NASA NTRS1991

Dynamic edge warping - An experimental system for recovering disparity maps in weakly constrained systems

Dynamic edge warping (DEW), a technique for recovering reasonably accurate disparity maps from uncalibrated stereo image pairs, is presented. No precise knowledge of the epipolar camera geometry is assumed. The technique is embedded in a system including structural stereopsis on the front end and robust estimation in digital photogrammetry on the other for the purpose of self-calibrating stereo image pairs. Once the relative camera orientation is known, the epipolar geometry is computed and the system can use this information to refine its representation of the object space. Such a system will find application in the autonomous extraction of terrain maps from stereo aerial photographs, for which camera position and orientation are unknown a priori, and for online autonomous calibration maintenance for robotic vision applications, in which the cameras are subject to vibration and other physical disturbances after calibration. This work thus forms a component of an intelligent system that begins with a pair of images and, having only vague knowledge of the conditions under which they were acquired, produces an accurate, dense, relative depth map. The resulting disparity map can also be used directly in some high-level applications involving qualitative scene analysis, spatial reasoning, and perceptual organization of the object space. The system as a whole substitutes high-level information and constraints for precise geometric knowledge in driving and constraining the early correspondence process.

Boyer, K. L.↗

NASA NTRS1992

Using adaptive grid in modeling rocket nozzle flow

The mechanical behavior of a rocket motor internal flow field results in a system of nonlinear partial differential equations which cannot be solved analytically. However, this system of equations called the Navier-Stokes equations can be solved numerically. The accuracy and the convergence of the solution of the system of equations will depend largely on how precisely the sharp gradients in the domain of interest can be resolved. With the advances in computer technology, more sophisticated algorithms are available to improve the accuracy and convergence of the solutions. An adaptive grid generation is one of the schemes which can be incorporated into the algorithm to enhance the capability of numerical modeling. It is equivalent to putting intelligence into the algorithm to optimize the use of computer memory. With this scheme, the finite difference domain of the flow field called the grid does neither have to be very fine nor strategically placed at the location of sharp gradients. The grid is self adapting as the solution evolves. This scheme significantly improves the methodology of solving flow problems in rocket nozzles by taking the refinement part of grid generation out of the hands of computational fluid dynamics (CFD) specialists and place it into the computer algorithm itself.

Chow, Alan S.↗

NASA NTRS2004

Planning for rover opportunistic science

The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.

artificial intelligence↗

NASA NTRS2014

Aircraft Turbine Engine Control Research at NASA Glenn Research Center

This lecture will provide an overview of the aircraft turbine engine control research at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC). A brief introduction to the engine control problem is first provided with a description of the current state-of-the-art control law structure. A historical aspect of engine control development since the 1940s is then provided with a special emphasis on the contributions of GRC. The traditional engine control problem has been to provide a means to safely transition the engine from one steady-state operating point to another based on the pilot throttle inputs. With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Branch (CDB) at GRC is leading and participating in various projects in partnership with other organizations within GRC and across NASA, other government agencies, the U.S. aerospace industry, and academia to develop advanced propulsion controls and diagnostics technologies that will help meet the challenging goals of NASA programs under the Aeronautics Research Mission. The second part of the lecture provides an overview of the various CDB technology development activities in aircraft engine control and diagnostics, both current and some accomplished in the recent past. The motivation for each of the research efforts, the research approach, technical challenges and the key progress to date are summarized. The technologies to be discussed include system level engine control concepts, gas path diagnostics, active component control, and distributed engine control architecture. The lecture will end with a futuristic perspective of how the various current technology developments will lead to an Intelligent and Autonomous Propulsion System requiring none to very minimum pilot interface, interfacing directly with the flight management system to determine its mode of operation, and providing personalized engine control to optimize its performance given the current condition and mission objectives.

Propulsion↗

NASA NTRS2014

Air Breathing Propulsion Controls and Diagnostics Research at NASA Glenn Under NASA Aeronautics Research Mission Programs

Engine Control↗

NASA NTRSDate not supplied

Merging Analytic Collaborative Frameworks with New Observing Strategies Toward a Digital Twin: Earth – Episodic Pulse Event Impacts on Ocean Carbon Cycle as an Example

Virtual representations of the Earth will allow us to address some of the most critical environmental issues of our time. Here, we show the first steps toward representation of riverine, estuarine, and coastal carbon processes to enable scenario driven “what-if” analyses of the carbon system and human footprint. Excess sediment and nutrient runoff from land-based human activities impact water quality and can pose serious threats to coastal and marine ecosystems. Episodic pulse events, such as extreme precipitation events, can increase the amount of nutrients entering estuaries and coastal regions, potentially leading to large phytoplankton blooms followed by anoxic conditions. Consequences of coastal runoff are predicted to increase with the higher intensity and frequency of extreme events. Beyond the threat to coastal ecosystems, recent findings suggest these episodic pulses might play a significant role for biological production influencing regional and global carbon fluxes and budgets. An improved understanding of these events through optimal, dynamic observing strategies will increase our knowledge of the land-ocean continuum and how regional events and nutrient fluxes affect the carbon cycle and ocean ecosystem. This conceptual framework enables focused science investigations by pairing data analytics and artificial intelligence tools (otherwise termed an Analytic Center Framework, ACF) with targeted measurement acquisition through distributed sensing and intelligent asset tasking (or New Observing Strategies, NOS). This NOS and ACF iterative approach acquires and integrates complementary and coincident satellite, in-situ and model data to build a more complete and in-depth picture of science phenomena. Specifically, Apache Science Data Analytic Platform (SDAP) is extended to incorporate relevant datasets for data access, harmonized analysis, and anomaly detection. When conditions are met for a likely pulse event, NASA’s D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) tool is triggered to optimize asset overpass frequency and schedule observations for persistent monitoring. Targeted data is ingested by SDAP for enhanced investigation via iterative analysis until the trigger criteria is no longer met - steps toward a digital twin.

Laura Rogers↗

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 NTRS2021

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗