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NASA NTRS2016

Autonomous Operations System: Development and Application

Autonomous control systems provides the ability of self-governance beyond the conventional control system. As the complexity of mechanical and electrical systems increases, there develops a natural drive for developing robust control systems to manage complicated operations. By closing the bridge between conventional automated systems to knowledge based self-awareness systems, nominal control of operations can evolve into relying on safe critical mitigation processes to support any off-nominal behavior. Current research and development efforts lead by the Autonomous Propellant Loading (APL) group at NASA Kennedy Space Center aims to improve cryogenic propellant transfer operations by developing an automated control and health monitoring system. As an integrated systems, the center aims to produce an Autonomous Operations System (AOS) capable of integrating health management operations with automated control to produce a fully autonomous system.

Autonomous Control Systems↗

NASA NTRS2016

R2U2 in Space: System and Software Health Management for Small Satellites

In order for small but complex systems like rovers, SmallSats, or Unmanned Aircraft (UAS) to operate autonomously, they must have a real-time solution for assessing their own system health. System and Software Health Management (SHM) enables better detection of faulty sensors and software problems, and enables better fault management including mitigation of unpredicted fault scenarios in the absence of a human on-board. In recent work, we have developed a Responsive, Realizable, Unobtrusive Unit (R2U2) for on-board SHM of autonomous UAS and demonstrated its ability to detect faults during flight time. These faults, from sensor failures, to software problems, to malicious security attacks, can present as transient temporal faults that even humans are challenged to find. An R2U2 congfiuration is a modular combination of multiple types of temporal logic runtime observers with fault-specic Bayesian Nets and sensor filters. R2U2 reasons about both on-board hardware and software components; R2U2 itself can be instantiated as an independent FPGA (Field-Programmable Gate Array)-based conguration or as a software component running independently from other software on-board. Small satellites, such as CubeSats, also require on-board SHM and failure mitigation, as limited telemetry bandwidth does not allow the transmission of the entire system state for ground-based health management. However, the autonomous operation of satellites brings a set of challenges different from UAS, including the effects of radiation on non-rad-hard, low-cost components, and the harsher environment of space. We surmise that a new extension of R2U2 could be adapted to help better detect, for example, radiation errors in cheaper COTS (Commercial Off the Shelf) (not rad-hard) components often used in small space systems. Since small satellites often operate in coordination, we will also examine new ways of distributed monitoring of their communication and cooperation and real-time detection of off-nominal situations utilizing multiple satellites. This talk will discuss preliminary work and ideas for building on terrestrial success of system and software health management for the harsher, and differently challenging, environment of space.

Runtime Verification & Validation↗

NASA NTRS2013

Advancing Autonomous Operations Technologies for NASA Missions

This paper discusses the importance of implementing advanced autonomous technologies supporting operations of future NASA missions. The ability for crewed, uncrewed and even ground support systems to be capable of mission support without external interaction or control has become essential as space exploration moves further out into the solar system. The push to develop and utilize autonomous technologies for NASA mission operations stems in part from the need to reduce operations cost while improving and increasing capability and safety. This paper will provide examples of autonomous technologies currently in use at NASA and will identify opportunities to advance existing autonomous technologies that will enhance mission success by reducing operations cost, ameliorating inefficiencies, and mitigating catastrophic anomalies.

Cruzen, Craig↗

NASA NTRSDate not supplied

Advancing Dust Tolerant Mechanisms for a Sustained Exploration of the Moon

Introduction: “I think dust is probably one of our greatest inhibitors to a nominal operation on the Moon. I think we can overcome other physio-logical or physical or mechanical problems except dust.”– Gene Cernan, Apollo 17 Technical Debrief The Apollo missions revealed the impact of lu-nar dust on mechanisms. Lunar dust particles are jagged and electrostatically charged, giving them the ability to bind or damage mechanisms and alter thermal properties. Reports documented clogged equipment and jammed mechanisms in every mission, regardless of surface duration, as well as clogged mechanisms in the Extravehicular Mobility Suit (EMS), including zippers, wrist and hose locks, faceplates, and sunshades [1-2]. Several astronauts remarked they could not have sustained surface activity much longer because clogged joints would have frozen up completely [2]. Effective dust mitigation strategies are need-ed to support longer duration stays on the lunar surface [3-4]. State of the Art: Technology for mechanisms able to operate in dusty enviroments is advancing rapidly due to the needs of both Mars rovers and the Artemis program. Vacuum-tight connectors are essential for spacesuits and habitats, and their performance can be dependent on cleaning technologies, which have proven difficult on the lunar surface. Several TRL 3-5 technologies are undergoing tests with the expectation to reach TRL 6 within 1-2 years. Some mechanisms will be infused and tested on the VIPER (Volatiles Inves-tigating Polar Exploration Rover) mission planned for mid-2020s. NASA Funded Efforts: NASA has recognized the need for dust tolerant mechanisms, and has partnered with industry to advance the state-of-the-art. At NASA GRC, KSC, and JSC, the Dust Tolerant Mechanisms Project is working to devel-op advanced actuator seals for rotary joints and rotary bearing technologies for long-term sus-tained operation in lunar dust environments. An-other NASA project at NASA GRC, partnered with GSFC, JPL, and KSC is Motors for Dusty & Ex-treme Cold Environments (MDECE). MDECE is developing an unheated magnetically-geared mo-tor that can operate continuously for a long dura-tion at an ambient temperature of -243 ºC (33 K). NASA GRC has the capability to characterize the effects of dust on seals, mechanisms, and other mating surfaces and components under lunar conditions [5]. Through the SBIR/STTR program, NASA has funded several companies to advance dust toler-ant mechanisms via the Dust Tolerant Mecha-nisms sub-topic with applications in surface mobil-ity, spacesuits, connectors, joints, and more. LSIC and Community Efforts: The Lunar Surface Innovation Consortium (LSIC) Dust Miti-gation focus group has fostered collaborations across NASA, industry, and academia to develop solutions that minimizes the impact of lunar dust on robotic and human systems. Community ef-forts have included topical meetings on dust tol-erant mechanisms, featured technology presenta-tions, and feedback to NASA on potential gaps and needs. Testing: In 2021, NASA released NASA-STD-1008 [6]. This NASA Technical Standard estab-lishes minimum requirements and provides guid-ance for testing systems and hardware to be ex-posed to dust in planetary environments. The standard has specific sections dedicated to Mechanisms Testing (e.g. bearings, gears) as well as Seals and Mating Surfaces Testing (e.g. hatches, docking systems). Gaps and Needs: NASA is tracking dust tol-erant mechanisms as a gap in a cross-directorate analysis of capability areas needed to enable fu-ture human space-flight architectures. Two high-priority gap areas include additional facilities for testing mechanisms in lunar-surface conditions, and a better understanding of vulnerabilities to the smallest, nanometer-scale dust particles. Conclusion: Understanding and mitigating lu-nar dust is critical to successful, sustained opera-tions on the lunar surface – whether autonomous or otherwise. This presentation will discuss both the state-of-the-art and open needs in lunar dust tolerant mechanisms, technology impacts, mitiga-tion approaches, testing, LSIC community efforts, and more. References: [1] Gaier, J. R. (2020). The Im-pact of Dust on Lunar Surface Equipment During Apollo. Lunar Dust 2020. [2] GRC, & Gaier, J. R. (2005). The Effects of Lunar Dust on EVA Sys-tems During the Apollo Missions. [3] Johansen, M. R. (2020). An Update on NASA’s Lunar Dust Mitigation Strategy. Lunar Dust 2020. [4] ASI, CSA, ESA, JAXA, & NASA. (2016). Dust Mitiga-tion Gap Assessment Report. [5] Jimenez, N. et al (2022), LPSC Abstract 2572. [6] NASA-STD-1008, 2021.

J I Nunez↗

DOE OSTI2022

Security Architecture for Microgrid Integration

The purpose of this document is to present cyber security risk considerations and mitigations for community microgrids. The mitigations discussed herein specifically focus on cyber security technical requirements for the De-centralized Autonomous Community Controller (DAC) as part of the SECURE project’s technology development effort. These requirements also consider the associated security implementation challenges involved with the decentralized characteristics of microgrids where upstream communications to utility security management functions are lost. These security recommendations and technical requirements may be used by microgrid implementers and technology vendors to specify the security design of the microgrid systems and their components.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗

NASA NTRSDate not supplied

A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

The emergence and development of advanced technologies and vehicle types has created a growing demand for the introduction of new forms of flight operations. These new and increasingly complex operational paradigms such as Advanced and Urban Air Mobility (AAM/UAM) present regulatory authorities and the aviation community with several design and implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is finding methods to integrate these emerging operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. This paper focus on the development and testing of a prognostic service aimed at estimating the quality of Global Navigation Satellite System (GNSS) performance for an autonomous aircraft in complex environments. The intent of this function is to proactively reduce a flight operations risk of exposure to states that may induce poor or unacceptable navigation system performance by factoring in estimates of GNSS quality into pre-flight and/or in-flight route planning. Methodologies for producing quality estimates are specified and results are provided for selected simulation and flight test cases.

GNSS↗

NASA NTRSDate not supplied

Conjunction Assessment and Deconfliction Paradigm for Co-located Satellite Constellations with On-Spacecraft “Autonomous” Flight Dynamics Control

Constellations that employ highly-automated satellite flight dynamics can operate safely from a collision avoidance (CA) perspective using current CA screening technology and communication paradigms—except when in proximity to other highly-automated constellations: there is no existing arrangement and concept of operations to allow the low-latency CA screenings, exchange of information, and assignment/acceptance of mitigation responsibility that co-location of highly-automated constellations requires. However, the serendipitous co-location of the NASA Starling mission—an autonomously-controlled technology demonstration constellation—and the SpaceX Starlink constellation has presented an opportunity (indeed, a requirement) to develop and build out a solution for safe operations of two (or more) co-located, highly-automated satellite constellations. Such a solution has been designed and constructed, including the required ground node; and it will be exercised as a dedicated, in-flight safety experiment between the two constellations from January to September 2024. Lessons learned from this experiment are expected to feed the design of the US Department of Commerce’s Tracking Control System for Space (TraCSS) space traffic coordination solution.

conjunction assessment↗

NASA NTRS2016

Using Multimodal Input for Autonomous Decision Making for Unmanned Systems

Autonomous decision making in the presence of uncertainly is a deeply studied problem space particularly in the area of autonomous systems operations for land, air, sea, and space vehicles. Various techniques ranging from single algorithm solutions to complex ensemble classifier systems have been utilized in a research context in solving mission critical flight decisions. Realized systems on actual autonomous hardware, however, is a difficult systems integration problem, constituting a majority of applied robotics development timelines. The ability to reliably and repeatedly classify objects during a vehicles mission execution is vital for the vehicle to mitigate both static and dynamic environmental concerns such that the mission may be completed successfully and have the vehicle operate and return safely. In this paper, the Autonomy Incubator proposes and discusses an ensemble learning and recognition system planned for our autonomous framework, AEON, in selected domains, which fuse decision criteria, using prior experience on both the individual classifier layer and the ensemble layer to mitigate environmental uncertainty during operation.

Neilan, James H.↗

NASA NTRS1991

Demonstration of automated proximity and docking technologies

An autodock was demonstrated using straightforward techniques and real sensor hardware. A simulation testbed was established and validated. The sensor design was refined with improved optical performance and image processing noise mitigation techniques, and the sensor is ready for production from off-the-shelf components. The autonomous spacecraft architecture is defined. The areas of sensors, docking hardware, propulsion, and avionics are included in the design. The Guidance Navigation and Control architecture and requirements are developed. Modular structures suitable for automated control are used. The spacecraft system manager functions including configuration, resource, and redundancy management are defined. The requirements for autonomous spacecraft executive are defined. High level decisionmaking, mission planning, and mission contingency recovery are a part of this. The next step is to do flight demonstrations. After the presentation the following question was asked. How do you define validation? There are two components to validation definition: software simulation with formal and vigorous validation, and hardware and facility performance validated with respect to software already validated against analytical profile.

Anderson, Robert L.↗

DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

NASA’s Moon-to-Mars Planetary Autonomous Construction Technology Project: Overview and Status

NASA plans to land the first woman and next man on the Moon by 2025 through the initial Artemis missions. NASA and its international partners plan to establish a sustainable long-term presence on the lunar surface and build up infrastructure in the subsequent Artemis missions. The Lunar Surface Innovation Initiative (LSII), within NASA’s Space Technology Mission Directorate, aims to spur the creation of novel technologies needed for lunar surface exploration and accelerate the technology readiness of key systems and components. The primary thrust areas of LSII include sustainable power; dust mitigation; in-situ resource utilization; surface excavation, construction, and outfitting; and extreme access/extreme environments. The Moon-to-Mars Planetary Autonomous Construction Technology (MMPACT) projectwas initiated to address the lunar surface construction thrust area of LSII. The goal of the MMPACT project is to develop, deliver, and demonstrate on-demand capabilities to protect astronauts and create infrastructure on the lunar surface via construction of landing pads, habitats, shelters, roadways, berms and blast shields using lunar regolith-based materials. The MMPACT project is leveraging technology derived from NASA’s 3D Printed Mars Habitat Challenge along with contributions from other Government agencies, and multiple partners within industry and academia. The MMPACT project is comprised of three interrelated elements, construction hardware and process development; feedstock materials development; and microwave structure construction capabilities. These elements are working together to address the multiple challenges of infrastructure construction on the surface of the Moon including increased autonomy of operations, hardware operation and manufacturing under lunar environmental conditions, long-duration operation of mechanisms and parts, scale of construction activities, and material and construction requirements and standards. This presentation will summarize the status of development activities in each of the three elements, including testing of the various candidate materials, preliminary design concepts for future lunar infrastructure elements, and the vision for future technology demonstrations on the lunar surface. These demonstrations, targeting the mid-to-late 2020’s, are expected to enable landing pad construction and habitat construction resulting in commercial capabilities early in the next decade.

lunar infrastructure↗

NASA NTRSDate not supplied

NASA’s Moon-to-Mars Planetary Autonomous Construction Technology Project: Overview and Status

NASA plans to land the first woman and next man on the Moon by 2025 through the initial Artemis missions. NASA and its international partners plan to establish a sustainable long-term presence on the lunar surface and build up infrastructure in the subsequent Artemis missions. The Lunar Surface Innovation Initiative (LSII), within NASA’s Space Technology Mission Directorate, aims to spur the creation of novel technologies needed for lunar surface exploration and accelerate the technology readiness of key systems and components. The primary thrust areas of LSII include sustainable power; dust mitigation; in-situ resource utilization; surface excavation, construction, and outfitting; and extreme access/extreme environments. The Moon-to-Mars Planetary Autonomous Construction Technology (MMPACT) project was initiated to address the lunar surface construction thrust area of LSII. The goal of the MMPACT project is to develop, deliver, and demonstrate on-demand capabilities to protect astronauts and create infrastructure on the lunar surface via construction of landing pads, habitats, shelters, roadways, berms and blast shields using lunar regolith-based materials. The MMPACT project is leveraging technology derived from NASA’s 3D Printed Mars Habitat Challenge along with contributions from other Government agencies, and multiple partners within industry and academia. The MMPACT project is comprised of three interrelated elements, construction hardware and process development; feedstock materials development; and microwave structure construction capabilities. These elements are working together to address the multiple challenges of infrastructure construction on the surface of the Moon including increased autonomy of operations, hardware operation and manufacturing under lunar environmental conditions, long-duration operation of mechanisms and parts, scale of construction activities, and material and construction requirements and standards. This presentation will summarize the status of development activities in each of the three elements, including testing of the various candidate materials, preliminary design concepts for future lunar infrastructure elements, and the vision for future technology demonstrations on the lunar surface. These demonstrations, targeting the mid-to-late 2020’s, are expected to enable landing pad construction and habitat construction resulting in commercial capabilities early in the next decade.

Additive construction, regolith processing, lunar ↗

NASA NTRS2022

Reliability-Aware Requirements Development for Autonomy Software

Autonomy is the ability of a system to make decisions and take actions in the presence of uncertainty. Validating autonomy, therefore, is a matter of validating that the system makes intelligent decisions even when there might be discrepancies between the observed and the expected state of the world as well as when the possible outcome of each of the possible courses of action is uncertain. During the requirements engineering phase, the functions, including those that are required to be autonomous and their expected behavior are specified. However, in the case of autonomous software system, it is challenging to specify all possible scenarios that the software may encounter. Additionally, since autonomous behaviors cannot always be fully predetermined, it is difficult to reason about their completeness and correctness. While these challenges are also faced with non-autonomous system, they are more prevalent for autonomous system due to their complexity and emergent behavior. Our efforts have been three-fold. On one hand, we have developed a goal-based method for requirements decomposition. We have embedded this approach into a higher-level systems engineering framework and then developed methods for decision and reliability analysis within this framework. And finally, we use simulation analysis to validate the more theoretical methods and provide a feedback mechanism. The decision analysis approach (DA) helps in identifying and mitigating the run-time risks, by bringing to the forefront the uncertainties, decisions, interactions, and other factors that may cause autonomous software to make erroneous decisions. The DA framework is used to quantify, for each decision that the software can make, failure risk due to uncertainties. The information about the key decisions and circumstances that can cause autonomous system to make incorrect decisions are used to generate test scenarios to be run in a simulator to ensure that the system can handle error-prone circumstances. The results or outcomes from decisions, based on the simulated scenarios, can then be leveraged to further gain insight of the assured system. We combine a goal-based approach with simulation analysis to facilitate requirements development for autonomous systems and further provide a method for run-time systems level reliability considerations during the autonomous software development process. The DA and the reliability analysis methods provide a formalism for the consideration of uncertainty in the run time decision-making process for autonomous software. This formalism considers the uncertainty involved in the outcome of different courses of action, in terms of performance and cost, as well as a method to consider the system level reliability implications. System level reliability considerations for autonomous software decision making contribute to the satisfaction of the system level goals over the lifecycle of a given mission. We demonstrate our approach using a hypothetical rover path planning example. The requirements for our rover simulator are based on existing literature about Mars rovers and lessons learned from real world observations at JPL. The specifics of the autonomy design for the Mars rovers, that have not been previously cleared for external release, are not used for our demonstrations.

Lindvall, Mikael↗

DOE OSTI2022

Synergies between repositioning and charging strategies for shared autonomous electric vehicle fleets

The emergence of on-demand shared autonomous electric vehicle (SAEV) service requires careful charging station planning and a joint charging and repositioning strategy to mitigate empty travel. This study couples charging and repositioning events as a means of improving service quality (rider wait times), reducing empty travel due to repositioning or charging, and improving fleet utilization (average daily trips per vehicle and charging queues). This synergy is explored for the Austin, Texas region using POLARIS, an agent-based model. On average, wait times were 39% lower, and average daily trips served per SAEV increased up to 6.4 (or 28%) compared to SAEV repositioning with heuristic charging. Coupling repositioning with charging decreased the fleet's percent empty travel on average by 1.6%, relative to the scenario treating them as independent events (which varies by charging station design). Sparser charging stations reduce investment costs, and operators can leverage this framework to keep traveler wait times low.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2005

Integrated Systems Health Management for Space Exploration

Integrated Systems Health Management (ISHM) is a system engineering discipline that addresses the design, development, operation, and lifecycle management of components, subsystems, vehicles, and other operational systems with the purpose of maintaining nominal system behavior and function and assuring mission safety and effectiveness under off-nominal conditions. NASA missions are often conducted in extreme, unfamiliar environments of space, using unique experimental spacecraft. In these environments, off-nominal conditions can develop with the potential to rapidly escalate into mission- or life-threatening situations. Further, the high visibility of NASA missions means they are always characterized by extraordinary attention to safety. ISHM is a critical element of risk mitigation, mission safety, and mission assurance for exploration. ISHM enables: In-space maintenance and repair; a) Autonomous (and automated) launch abort and crew escape capability; b) Efficient testing and checkout of ground and flight systems; c) Monitoring and trending of ground and flight system operations and performance; d) Enhanced situational awareness and control for ground personnel and crew; e) Vehicle autonomy (self-sufficiency) in responding to off-nominal conditions during long-duration and distant exploration missions; f) In-space maintenance and repair; and g) Efficient ground processing of reusable systems. ISHM concepts and technologies may be applied to any complex engineered system such as transportation systems, orbital or planetary habitats, observatories, command and control systems, life support systems, safety-critical software, and even the health of flight crews. As an overarching design and operational principle implemented at the system-of-systems level, ISHM holds substantial promise in terms of affordability, safety, reliability, and effectiveness of space exploration missions.

Uckun, Serdar↗

NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗

NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

Risk detection↗