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NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Monte Carlo Tree Search Approach

Numerous unmanned aircraft systems operating at low altitudes to deliver goods and services may one day become ubiquitous in our cities. In the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) framework, such a concept is envisioned, where aerial vehicles operate beyond visual line of sight (BVLOS) within specifically reserved and time stamped “corridors” in the airspace. For example, these corridors or operational intent volumes can connect an aerial vehicle’s origin site to its destination site for package delivery operations. There may also be more than one corridor available for an aerial vehicle to choose from and often different corridors may intersect with one another. Thus, it is imperative to ensure flight trajectories belonging to different aerial vehicles are not in conflict. Per the UTM CONOPs, we assume that a vehicle almost always stays inside its corridor or operational volume. This work provides a framework for strategic deconfliction of UTM or package delivery drones, where we schedule the departure time of all vehicles subject to various temporal constraints (including the corridor deconfliction at the intersections). We present the “multi-route weighted package delivery problem” which serves as an exemplifying model for strategic deconfliction in UTM. In the multi-route weighted package delivery problem, a graph network is given which consists of a set of depots (source) and drop-off (destination) nodes, with multiple routes (defined as a sequence of waypoints) connecting the depots to drop-off nodes. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is for a known set of aerial vehicles to depart from the depots, choose a route and take off time, while avoiding conflicts with other aerial vehicles, and minimizing both risk and distance traveled. We provide a mixed integer linear programming (MILP) formulation of the problem, as well as a heuristic solution based on Monte Carlo Tree Search (MCTS) – a method used in game theory and artificial intelligence – to overcome limitations inherent to optimal solvers. Computational results show the advantages of using MCTS over the MILP formulation; the former can provide a sub-optimal solution quickly, and may sometimes even reach an optimal solution, whereas the latter may not even produce a solution in reasonable time. Furthermore, results from both the MILP formulation and MCTS methods were validated using a preliminary agent-based simulator implementing the UTM concept of operations. Thus, the MCTS method can be seen as a scalable solution to the complex multi-route weighted package delivery problem and may possibly be extended to similar complex optimization problems.

Kenny Chour↗

NASA NTRS2014

Advancing Autonomous Operations for Deep Space Vehicles

Starting in Jan 2012, the Advanced Exploration Systems (AES) Autonomous Mission Operations (AMO) Project began to investigate the ability to create and execute "single button" crew initiated autonomous activities [1]. NASA Marshall Space Flight Center (MSFC) designed and built a fluid transfer hardware test-bed to use as a sub-system target for the investigations of intelligent procedures that would command and control a fluid transfer test-bed, would perform self-monitoring during fluid transfers, detect anomalies and faults, isolate the fault and recover the procedures function that was being executed, all without operator intervention. In addition to the development of intelligent procedures, the team is also exploring various methods for autonomous activity execution where a planned timeline of activities are executed autonomously and also the initial analysis of crew procedure development. This paper will detail the development of intelligent procedures for the NASA MSFC Autonomous Fluid Transfer System (AFTS) as well as the autonomous plan execution capabilities being investigated. Manned deep space missions, with extreme communication delays with Earth based assets, presents significant challenges for what the on-board procedure content will encompass as well as the planned execution of the procedures.

Haddock, Angie T.↗

DOE OSTI2025

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗

NASA NTRS1991

AI in manufacturing

AI techniques are shown to have been useful in such aerospace industry tasks as vehicle configuration layouts, process planning, tool design, numerically-controlled programming of tools, production scheduling, and equipment testing and diagnosis. Accounts are given of illustrative experiences at the production facilities of three major aerospace defense contractors. Also discussed is NASA's autonomous Intelligent Computer-Aided Training System, for such ambitious manned programs as Space Station Freedom, which employs five different modules to constitute its job-independent training architecture.

Gross, John E.↗

DOE OSTI2021

FY2020 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2001

Non-Deterministic, Non-Traditional Methods (NDNTM)

The review effort identified research opportunities related to the use of nondeterministic, nontraditional methods to support aerospace design. The scope of the study was restricted to structural design rather than other areas such as control system design. Thus, the observations and conclusions are limited by that scope. The review identified a number of key results. The results include the potential for NASA/AF collaboration in the area of a design environment for advanced space access vehicles. The following key points set the context and delineate the key results. The Principal Investigator's (PI's) context for this study derived from participation as a Panel Member in the Air Force Scientific Advisory Board (AF/SAB) Summer Study Panel on 'Whither Hypersonics?' A key message from the Summer Study effort was a perceived need for a national program for a space access vehicle whose operating characteristics of cost, availability, deployability, and reliability most closely match the NASA 3rd Generation Reusable Launch Vehicle (RLV). The Panel urged the AF to make a significant joint commitment to such a program just as soon as the AF defined specific requirements for space access consistent with the AF Aerospace Vision 2020. The review brought home a concurrent need for a national vehicle design environment. Engineering design system technology is at a time point from which a revolution as significant as that brought about by the finite element method is possible, this one focusing on information integration on a scale that far surpasses current design environments. The study therefore fully supported the concept, if not some of the details of the Intelligent Synthesis Environment (ISE). It became abundantly clear during this study that the government (AF, NASA) and industry are not moving in the same direction in this regard, in fact each is moving in its own direction. NASA/ISE is not yet in an effective leadership position in this regard. However, NASA does have complementary software interoperability efforts that should be a part of any major ISE program. Software standards that assure interoperability of data systems and modeling representations are enabling for the proposed research advocated herein and should be a major element in the ISE initiative. The international standard for data interchange is known by the acronym 'STEP.' The NASA participation and lead for that effort is at the Goddard Space Flight Center. NASA/GRC is leading an effort to define CAD geometry standards through the Object Management Group (OMG). To enable the design environment so necessary to the above national vision for a unique space vehicle will require an integrating software environment with interoperability standards that allow the development and widespread deployment of tools and toolsets, rather than traditional "shrink-wrapped" software used by engineers today.

Cruse, Thomas A.↗

DOE OSTI2025

Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.

AI↗

NASA NTRS2015

AMO EXPRESS: A Command and Control Experiment for Crew Autonomy Onboard the International Space Station

Stetson, Howard K.↗

NASA NTRS2015

AMO EXPRESS: A Command and Control Experiment for Crew Autonomy Onboard the International Space Station

NASA is investigating a range of future human spaceflight missions, including both Mars-distance and Near Earth Object (NEO) targets. Of significant importance for these missions is the balance between crew autonomy and vehicle automation. As distance from Earth results in increasing communication delays, future crews need both the capability and authority to independently make decisions. However, small crews cannot take on all functions performed by ground today, and so vehicles must be more automated to reduce the crew workload for such missions. NASA's Advanced Exploration Systems Program funded Autonomous Mission Operations (AMO) project conducted an autonomous command and control experiment on-board the International Space Station that demonstrated single action intelligent procedures for crew command and control. The target problem was to enable crew initialization of a facility class rack with power and thermal interfaces, and involving core and payload command and telemetry processing, without support from ground controllers. This autonomous operations capability is enabling in scenarios such as initialization of a medical facility to respond to a crew medical emergency, and representative of other spacecraft autonomy challenges. The experiment was conducted using the Expedite the Processing of Experiments for Space Station (EXPRESS) rack 7, which was located in the Port 2 location within the U.S Laboratory onboard the International Space Station (ISS). Activation and deactivation of this facility is time consuming and operationally intensive, requiring coordination of three flight control positions, 47 nominal steps, 57 commands, 276 telemetry checks, and coordination of multiple ISS systems (both core and payload). Utilization of Draper Laboratory's Timeliner software, deployed on-board the ISS within the Command and Control (C&C) computers and the Payload computers, allowed development of the automated procedures specific to ISS without having to certify and employ novel software for procedure development and execution. The procedures contained the ground procedure logic and actions as possible to include fault detection and recovery capabilities. The autonomous operations concept includes a reduction of the amount of data a crew operator is required to verify during activation or de-activation, as well as integration of procedure execution status and relevant data in a single integrated display. During execution, the auto-procedures (via Timerliner) provide a step-by-step messaging paradigm and a high-level status upon termination. This messaging and high-level status is the only data generated for operator display. To enhance situational awareness of the operator, the Web-based Procedure Display (WebPD) provides a novel approach to the issues of procedure display and execution tracking. WebPD is a web based application that serves as the user interface for electronic procedure execution. It incorporates several aspects of the HTML5 standard. Procedures are written in a dialect of XML called Procedure Representation Language (PRL). WebPD tracks execution status in the procedure or procedures being displayed. WebPD aggregates and simplifies the auto-sequence execution status information, and formatted to be easily followed and understood by an operator who is not dedicated to actively monitoring the task. WebPD also provides an integrated data and control interface to pause or halt the execution in order to provide a check point of operation and to examine progress before starting the next sequence of activities. For this demonstration, the procedure was initiated and monitored from the ground. As the Timeliner sequences executed, their high-level execution status was written to PLMDM memory. This memory is read and downlinked via Ku-Band at a 1 Hz rate. The data containing the high-level execution status is de-commutated on the ground, and rebroadcast for WebPD consumption. A future demonstration will be performed onboard, with ISS astronauts initiating the operations instead of ground controllers. The AMO EXPRESS experiment demonstrated activation and de-activation of EXPRESS rack 7, providing the capability of future single button activations and deactivations of facility class racks. The experiment achieved numerous technical and operations 'firsts' for the ISS

Stetson, Howard K.↗

DOE OSTI2026

Communications Reliability for Vehicle Grid Integration

Electric Vehicles (EVs) adoption rate has been steadily increasing in the US leading to a growing number of charging stations including faster DC (Direct Current) chargers and slower Level 1 and Level 2 AC (Alternating Current) chargers. This increase in demand for electricity is further exacerbated by recent developments in Artificial Intelligence (AI) technology, advanced manufacturing, and digitization. These factors will require electric utilities to upgrade their infrastructure to keep up with the increasing electrical demand (especially during peak hours). An easy way to counteract the need for these upgrades is to shift a major chunk of active charge sessions (durations where there is energy transfer from charger to EV's propulsion battery) to off-peak hours thereby flattening the load curve and making the infrastructure more resilient. This concept is known as Smart Charge Management (SCM). EV owners also benefit from SCM since it lowers their charging costs and consequently their transportation costs by prioritizing charging during off-peak hours. SCM takes advantage of EV's capability to act as a controllable load or DER (Distributed Energy Resource). This report summarizes the reliability analysis performed on the communication required for two of these SCM use-cases. This analysis only focuses on SCM strategies for unidirectional charging (energy transfer from EVSE to EV or V1G) and not bidirectional charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Operation, Manufacturing, and Supply Chain of Lithium-ion Batteries for Electric Vehicles

The US Department of Energy (DOE) has a rich history of funding key innovations in batteries through multiple programs, including the Energy Efficiency and Renewable Energy programs sponsored by the Vehicle Technologies Office and Advanced Manufacturing Office. These DOE-sponsored innovations have been critical in lowering the cost of lithium-ion batteries to less than $160/kWh and increasing their energy density to more than 250 Wh/kg, enhancing the nation’s electrification system and achieving higher degrees of decarbonization. These investments continue to be crucial for positioning the United States as the hub for the most cumulative battery R&D programs worldwide. However, major breakthroughs in battery manufacturing are still needed to enable an electrified future evolving to predominantly incorporate rechargeable lithium-ion batteries. This will require a strong battery manufacturing base that will not only enable cutting-edge advancements in materials and manufacturing sciences but will also allow the United States to become a world leader in this fast-growing area. This review aims at supporting DOE’s Advanced Technology Vehicle Manufacturing Loan Program to give technology background and information that can be used to develop the basis for loan topics and evaluate battery applications. Such a program can represent a significant stride toward reinforcing the US position in advanced manufacturing by introducing artificial intelligence, machine learning, and sensing and smart controls to facilitate, expedite, and lead the battery industry by 2030.

25 ENERGY STORAGE↗

NASA NTRS2012

Reconfigurable Antenna and Cognitive Radio for Space Applications

This presentation briefly discusses a research effort on mitigation techniques of radio frequency interference (RFI) on communication systems for possible space applications. This problem is of considerable interest in the context of providing reliable communications to the space vehicle which might suffer severe performance degradation due to RFI sources such as visiting spacecrafts and various ground radar systems. This study proposes a communication system with Reconfigurable Antenna (RA) and Cognitive Radio (CR) to mitigate the RFI impact. A cognitive radio is an intelligent radio that is able to learn from the environment and adapt to the variations in its surrounding by adjusting the transmit power, carrier frequency, modulation strategy or transmission data rate. Therefore, the main objective of a cognitive radio system is to ensure highly reliable communication whenever and wherever needed. To match the intelligent adaptability of the cognitive radio, a reconfigurable antenna system will be required to ensure the system performance. The technical challenges in design such a system will be discussed in this presentation.

Hwu, Shian U.↗

DOE OSTI2024

Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments

The project titled “Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments” addresses energy-related challenges associated with adaptive traffic control systems by integrating connected vehicles (CV) and connected infrastructure (CI). The system developed in this project, a CV-based adaptive traffic control system, aims to improve fuel consumption in mixed traffic environments by capitalizing on emerging CV and CI communication technologies, as well as leveraging recent advances in Artificial Intelligence (AI), optimization, and edge computing. The system was tested at the MLK Smart Corridor, an urban testbed managed by the University of Tennessee at Chattanooga (UTC) and the City of Chattanooga. The system was validated through extensive simulations, both Software-in-the-Loop (SILS) and Hardware-in-the-Loop (HILS), and was further implemented and tested in real-world conditions at several intersections along the corridor. The Fuel Consumption Performance Index (FC-PI) and the Ecological Performance Index (Eco-PI) were developed as the key components for evaluating the system’s impact on fuel consumption and emissions. These metrics provided a comprehensive means of understanding the impact of traffic signal control optimization in mixed traffic environments. The report presents an in-depth analysis of the Eco-PI, FC-PI, adaptive traffic control system integration, and the testing and field implementation of the system. The results demonstrate significant reductions in fuel consumption and emissions, showcasing the system’s capability to contribute to more sustainable urban traffic management. The report also documents the challenges encountered and recommendations for scaling and further improving the system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2009

Smart and Intelligent Sensors

John C. Stennis Space Center (SSC) provides rocket engine propulsion testing for NASA's space programs. Since the development of the Space Shuttle, every Space Shuttle Main Engine (SSME) has undergone acceptance testing at SSC before going to Kennedy Space Center (KSC) for integration into the Space Shuttle. The SSME is a large cryogenic rocket engine that uses Liquid Hydrogen (LH2) as the fuel. As NASA moves to the new ARES V launch system, the main engines on the new vehicle, as well as the upper stage engine, are currently base lined to be cryogenic rocket engines that will also use LH2. The main rocket engines for the ARES V will be larger than the SSME, while the upper stage engine will be approximately half that size. As a result, significant quantities of hydrogen will be required during the development, testing, and operation of these rocket engines.Better approaches are needed to simplify sensor integration and help reduce life-cycle costs. 1.Smarter sensors. Sensor integration should be a matter of "plug-and-play" making sensors easier to add to a system. Sensors that implement new standards can help address this problem; for example, IEEE STD 1451.4 defines transducer electronic data sheet (TEDS) templates for commonly used sensors such as bridge elements and thermocouples. When a 1451.4 compliant smart sensor is connected to a system that can read the TEDS memory, all information needed to configure the data acquisition system can be uploaded. This reduces the amount of labor required and helps minimize configuration errors. 2.Intelligent sensors. Data received from a sensor be scaled, linearized; and converted to engineering units. Methods to reduce sensor processing overhead at the application node are needed. Smart sensors using low-cost microprocessors with integral data acquisition and communication support offer the means to add these capabilities. Once a processor is embedded, other features can be added; for example, intelligent sensors can make a health assessment to inform the data acquisition client when sensor performance is suspect. 3.Distributed sample synchronization. Networks of sensors require new ways for synchronizing samples. Standards that address the distributed timing problem (for example, IEEE STD 1588) provide the means to aggregate samples from many distributed smart sensors with sub-microsecond accuracy. 4. Reduction in interconnect. Alternative means are needed to reduce the frequent problems associated with cabling and connectors. Wireless technologies offer the promise of reducing interconnects and simultaneously making it easy to quickly add a sensor to a system.

Lansaw, John↗

DOE OSTI2020

Energy storage emerging: A perspective from the Joint Center for Energy Storage Research

Energy storage is an integral part of modern society. A contemporary example is the lithium (Li)-ion battery, which enabled the launch of the personal electronics revolution in 1991 and the first commercial electric vehicles in 2010. Most recently, Li-ion batteries have expanded into the electricity grid to firm variable renewable generation, increasing the efficiency and effectiveness of transmission and distribution. Important applications continue to emerge including decarbonization of heavy-duty vehicles, rail, maritime shipping, and aviation and the growth of renewable electricity and storage on the grid. This perspective compares energy storage needs and priorities in 2010 with those now and those emerging over the next few decades. The diversity of demands for energy storage requires a diversity of purpose-built batteries designed to meet disparate applications. Advances in the frontier of battery research to achieve transformative performance spanning energy and power density, capacity, charge/discharge times, cost, lifetime, and safety are highlighted, along with strategic research refinements made by the Joint Center for Energy Storage Research (JCESR) and the broader community to accommodate the changing storage needs and priorities. Innovative experimental tools with higher spatial and temporal resolution, in situ and operando characterization, first-principles simulation, high throughput computation, machine learning, and artificial intelligence work collectively to reveal the origins of the electrochemical phenomena that enable new means of energy storage. This knowledge allows a constructionist approach to materials, chemistries, and architectures, where each atom or molecule plays a prescribed role in realizing batteries with unique performance profiles suitable for emergent demands.

Trahey, Lynn↗

DOE OSTI2023

How is statistical thinking useful for evaluating foundation models?

Good’s statement appears to be rather prescient – while humanity has marched on through to the 21st century, modern artificial intelligence (AI), driven by a menagerie of immense deep-neural-network architectures aided with parallel computation, seems positioned to change life as we know it. It would be an understatement to say that the world has been captivated by AI. Recent engineering feats, such as autonomous vehicles and generative AI, have gripped the public sphere, and AI is poised to disrupt a host of industries – including software, pharmaceuticals, healthcare, manufacturing, entertainment, and a slew of others; indeed, one is hard pressed to find any industry that does not claim to bear an impact from the so-called AI revolution.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2011

NASA Technology Area 07: Human Exploration Destination Systems Roadmap

This paper gives an overview of the National Aeronautics and Space Administration (NASA) Office of Chief Technologist (OCT) led Space Technology Roadmap definition efforts. This paper will given an executive summary of the technology area 07 (TA07) Human Exploration Destination Systems (HEDS). These are draft roadmaps being reviewed and updated by the National Research Council. Deep-space human exploration missions will require many game changing technologies to enable safe missions, become more independent, and enable intelligent autonomous operations and take advantage of the local resources to become self-sufficient thereby meeting the goal of sustained human presence in space. Taking advantage of in-situ resources enhances and enables revolutionary robotic and human missions beyond the traditional mission architectures and launch vehicle capabilities. Mobility systems will include in-space flying, surface roving, and Extra-vehicular Activity/Extravehicular Robotics (EVA/EVR) mobility. These push missions will take advantage of sustainability and supportability technologies that will allow mission independence to conduct human mission operations either on or near the Earth, in deep space, in the vicinity of Mars, or on the Martian surface while opening up commercialization opportunities in low Earth orbit (LEO) for research, industrial development, academia, and entertainment space industries. The Human Exploration Destination Systems (HEDS) Technology Area (TA) 7 Team has been chartered by the Office of the Chief Technologist (OCT) to strategically roadmap technology investments that will enable sustained human exploration and support NASA s missions and goals for at least the next 25 years. HEDS technologies will enable a sustained human presence for exploring destinations such as remote sites on Earth and beyond including, but not limited to, LaGrange points, low Earth orbit (LEO), high Earth orbit (HEO), geosynchronous orbit (GEO), the Moon, near-Earth objects (NEOs), which > 95% are asteroidal bodies, Phobos, Deimos, Mars, and beyond. The HEDS technology roadmap will strategically guide NASA and other U.S. Government agency technology investments that will result in capabilities enabling human exploration missions to diverse destinations generating high returns on investments.

Kennedy, Kriss J.↗

NASA NTRS2001

Smarter Software For Enhanced Vehicle Health Monitoring and Inter-Planetary Exploration

The existing philosophy for space mission control was born in the early days of the space program when technology did not exist to put significant control responsibility onboard the spacecraft. NASA relied on a team of ground control experts to troubleshoot systems when problems occurred. As computing capability improved, more responsibility was handed over to the systems software. However, there is still a large contingent of both launch and flight controllers supporting each mission. New technology can update this philosophy to increase mission assurance and reduce the cost of inter-planetary exploration. The advent of model-based diagnosis and intelligent planning software enables spacecraft to handle most routine problems automatically and allocate resources in a flexible way to realize mission objectives. The manifests for recent missions include multiple subsystems and complex experiments. Spacecraft must operate at longer distances from earth where communications delays make earthbound command and control impractical. NASA's Ames Research Center (ARC) has demonstrated the utility of onboard diagnosis and planning with the Remote Agent experiment in 1999. KSC has pioneered model-based diagnosis and demonstrated its utility for ground support operations. KSC and ARC are cooperating in research to improve the state of the art of this technology. This paper highlights model-based reasoning applications for Moon and Mars missions including in-situ resource utilization and enhanced vehicle health monitoring.

Larson, William E.↗