Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Autonomous Systems”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38

Task Description Language

Task Description Language (TDL) is an extension of the C++ programming language that enables programmers to quickly and easily write complex, concurrent computer programs for controlling real-time autonomous systems, including robots and spacecraft. TDL is based on earlier work (circa 1984 through 1989) on the Task Control Architecture (TCA). TDL provides syntactic support for hierarchical task-level control functions, including task decomposition, synchronization, execution monitoring, and exception handling. A Java-language-based compiler transforms TDL programs into pure C++ code that includes calls to a platform-independent task-control-management (TCM) library. TDL has been used to control and coordinate multiple heterogeneous robots in projects sponsored by NASA and the Defense Advanced Research Projects Agency (DARPA). It has also been used in Brazil to control an autonomous airship and in Canada to control a robotic manipulator.

Simmons, Reid↗

A Ground-Based Profiling Differential Absorption LIDAR System for Measuring CO2 in the Planetary Boundary Layer

Ground-based LIDAR observations can potentially provide continuous profiles of CO2 through the planetary boundary layer and into the free troposphere. We will present initial atmospheric measurements from a prototype system that is based on components developed by the telecommunications industry. Preliminary measurements and instrument performance calculations indicate that an optimized differential absorption LIDAR (DIAL) system will be capable of providing continuous hourly averaged profiles with 250m vertical resolution and better than 1 ppm precision at 1 km. Precision increases (decreases) at lower (higher) altitudes and is directly proportional to altitude resolution and acquisition time. Thus, precision can be improved if temporal or vertical resolution is sacrificed. Our approach measures absorption by CO2 of pulsed laser light at 1.6 microns backscattered from atmospheric aerosols. Aerosol concentrations in the planetary boundary layer are relatively high and are expected to provide adequate signal returns for the desired resolution. The long-term goal of the project is to develop a rugged, autonomous system using only commercially available components that can be replicated inexpensively for deployment in a monitoring network.

Andrews, Arlyn E.↗

IDEA: Planning at the Core of Autonomous Reactive Agents

Several successful autonomous systems are separated into technologically diverse functional layers operating at different levels of abstraction. This diversity makes them difficult to implement and validate. In this paper, we present IDEA (Intelligent Distributed Execution Architecture), a unified planning and execution framework. In IDEA a layered system can be implemented as separate agents, one per layer, each representing its interactions with the world in a model. At all levels, the model representation primitives and their semantics is the same. Moreover, each agent relies on a single model, plan database, plan runner and on a variety of planners, both reactive and deliberative. The framework allows the specification of agents that operate, within a guaranteed reaction time and supports flexible specification of reactive vs. deliberative agent behavior. Within the IDEA framework we are working to fully duplicate the functionalities of the DS1 Remote Agent and extend it to domains of higher complexity than autonomous spacecraft control.

Muscettola, Nicola↗

Uncrewed Aerial Systems for Emergency Medical First Response: A Market Research Report

This report presents the findings from market research conducted for NASA’s Aerial Aid Convergent Aeronautics Solutions (CAS) exploration project, which aims to assess the current state of the market and technological readiness for Uncrewed Aerial Systems (UAS) for medical emergency first response. The research reveals a robust and rapidly growing market for UAS, with a notable emerging sector for Drones as First Responders (DFR). Despite this growth, DFR applications are currently limited by regulatory, technical, and other challenges, which restrict their use primarily to manned remote video surveillance, and therefore are primarily employed by police units. To our knowledge, there is no evidence of UAS being utilized by medical first responders for scene assessment. Limited evidence exists for closely related applications; however, these are mostly confined to pilot programs for the delivery of medical supplies or equipment. Although there has been discussion around fully autonomous DFR applications for medical purposes such as UAS ambulances or patient transport drones, these applications are generally not yet operational in practice. The technology for full autonomy, especially in guidance and control, has seen significant advancements, and recent Federal Aviation Administration (FAA)regulations are likely to accelerate adoption. Computer vision algorithms for fully autonomous medical emergency response scene surveillance are primed for advancement and deployment. A notable gap likely exists between advancements in computer vision research and what is being integrated in the commercial DFR sector. This gap is primarily due to challenges such as quality assurance for autonomous systems, the availability of application-specific training datasets for computer vision algorithms, regulatory constraints, and public perception and privacy concerns.

Joshua M Fody↗

Dynamic Adjustment of Simulation Parameters for Efficient Vehicle Prognostics

Model-based diagnostics and prognostics rely on state estimation and uncertainty management algorithms to produce useful information for system operators and maintainers. This information enables more informed operational decisions, condition-based maintenance, and overall mission safety assurance. Typically, uncertainty is associated with vehicle state-of-health estimation and prediction results because of modeling errors, internal or external sources of noise, and sensor inaccuracy. Probabilistic uncertainty management methods including Sequential Monte Carlo simulation are commonly used to reason about state-of-health estimates and predictions in the presence of these sources of uncertainty. However, such algorithms can be computationally expensive as they require a very large number of samples to obtain a sufficiently accurate quantification of the end of life probability distribution. As a result, highly mobile autonomous systems that leverage the prognostic results for mission-level replanning are often constrained in their processing capability because of these computationally expensive simulation approaches. Therefore, in this paper, we investigate algorithmic methods for dynamically adjusting simulation time step as well as number of samples to achieve highly efficient prognostic results while maintaining results accuracy. Results obtained from simulated flight experiments of an electric unmanned aerial vehicle are presented to verify the efficacy of such algorithms.

Prognostics↗

Digital Twins and Living Models at NASA

The idea of a “digital twin” was born at NASA in the 1960s as a “living model” of the Apollo mission. In response to Apollo 13’s oxygen tank explosion and subsequent damage to the main engine, NASA employed multiple simulators to evaluate the failure and extended a physical model of the vehicle to include digital components. This “digital twin” was the first of its kind, allowing for a continuous ingestion of data to model the events leading to up to the accident for forensic analysis and exploration of next steps. Fast forward half a century and NASA, along with others in the aerospace community, continues to develop and utilize high-fidelity digital models of physical systems and components as well as the extreme environments in which they operate. NASA aims to travel further and stay longer in space as we realize the Artemis program, taking us from the moon to Mars by establishing a sustainable presence on the Moon to prepare for missions to Mars. We will no longer be able to rely on constant connectivity with an asset nor be in-the-loop for on-demand human intervention in the event of an anomaly. Further, the importance of digital twins is increasing as we seek alternatives for certification of structures so large that they cannot be fully evaluated in existing test facilities and autonomous systems that are not deterministic. The idea of a digital twin is not a new one. What is new is the scale, ordinality, and non-deterministic nature of the models that are critical to achieving NASA’s goals. Their number and autonomy from each other as well as the reference system is suggestive of a changing ecosystem, returning us the idea of a living model.

Digital Twin↗

Formal Methods in Air Traffic Management: The Case of Unmanned Aircraft Systems

As the technological and operational capabilities of unmanned aircraft systems (UAS) continue to grow, so too does the need to introduce these systems into civil airspace. Unmanned Aircraft Systems Integration in the National Airspace System is a NASA research project that addresses the integration of civil UAS into non-segregated airspace operations. One of the major challenges of this integration is the lack of an onboard pilot to comply with the legal requirement that pilots see and avoid other aircraft. The need to provide an equivalent to this requirement for UAS has motivated the development of a detect and avoid (DAA) capability to provide the appropriate situational awareness and maneuver guidance in avoiding and remaining well clear of traffic aircraft. Formal methods has played a fundamental role in the development of this capability. This talk reports on the formal methods work conducted under NASA's Safe Autonomous System Operations project in support of the development of DAA for UAS. This work includes specification of low-level and high-level functional requirements, formal verification of algorithms, and rigorous validation of software implementations. The talk also discusses technical challenges in formal methods research in the context of the development and safety analysis of advanced air traffic management concepts.

Munoz, Cesar A.↗

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗

Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. In this study, we analyze both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.

42 ENGINEERING↗

Research and technology

As the NASA center responsible for assembly, checkout, servicing, launch, recovery, and operational support of Space Transportation System elements and payloads, Kennedy Space Center (KSC) is placing increasing emphasis on KSC's research and technology program. In addition to strengthening those areas of engineering and operations technology that contribute to safer, more efficient, and more economical execution of the current mission, the technological tools needed to execute KSC's mission relative to future programs are being developed. The Engineering Development Directorate encompasses most of the laboratories and other KSC resources that are key elements of research and technology program implementation and is responsible for implementation of the majority of the projects in this KSC 1990 annual report. Projects under the following topics are covered: (1) materials science; (2) hazardous emissions and contamination monitoring; (3) biosciences; (4) autonomous systems; (5) communications and control; (6) meteorology; (7) technology utilization; and (8) mechanics, structures, and cryogenics.

Source record↗

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet [1], outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Cognitive Networking↗

Development of Plug-and-Play Interchangeable Components for Unmanned Aerial System with Mobile Manipulation Capability

The safe cleanup of the environmental legacy brought about from decades of nuclear weapons development and government-sponsored nuclear energy research is of vital importance. The sites of U.S. Department of Energy hold 6.5 trillion liters of contaminated groundwater and 40 million cubic meters of soil and debris contaminated with radionuclides, metals, and organics. The deactivation and decommissioning process (D and D) involves conversion of active, excess, and/or abandoned facilities to a final disposition. Because of residual radioactivity and dangerous chemicals, and the physical condition of these facilities, D and D presents unique hazards that must be addressed from safety, environmental, programmatic, and technological standpoints. This problem calls for development of informed D and D strategies including in-situ decommissioning, efficacy of D and D operations, productivity and personnel safety of D and D operation, facilitation of acceptable facility end-states, and independent verification. To identify and address D and D risks and challenges, innovative applications and timely insertion of existing commercially available technologies, processes and robotics or remote hardware are necessary. Aerial robotic unmanned autonomous systems (UAS) enable remote sensing operations. To address the needs of UAS-based sensing and sampling applications, we are developing plug-and-play interchangeable components for integration into the UAS: a neutron/gamma radiation sensor, a gas sensor for detection of vapors in air, and a miniature manipulator. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

The critical role of velocity storage in production of motion sickness

We propose that motion sickness is mediated through the orientation properties of velocity storage in the vestibular system that tend to align eye velocity produced by the angular vestibulo-ocular reflex (aVOR) with gravito-inertial acceleration (GIA). (GIA is the sum of the linear accelerations acting on the head. In the absence of translational accelerations, gravity is the GIA.) We further postulate that motion sickness produced by cross-coupled vestibular stimulation can be characterized by a metric composed of the disparity between the axis of eye rotation and the GIA, the strength of the response to angular motion, and the response duration, as determined by the central vestibular time constant, that is, by the time constant of velocity storage. The nodulus and uvula of the vestibulocerebellum are likely to be the central sites where the disparity is sensed, where the vestibular time constants are habituated, and where links are made to the autonomic system to produce the symptoms and signs.

Non-NASA Center↗

NASA System-Wide Safety Wildland Firefighting Operations Workshop Report

On March 9-11, 2022, NASA’s System-Wide Safety Wildland Firefighting Operations Workshop engaged the broader wildland firefighting management ecosystem in a safety-oriented discussion via a virtual platform. This enabled a better understanding of how NASA and community expertise can be leveraged in the safe development of current and future firefighting systems and operations. The goals of the workshop were to: (1) identify and prioritize the top safety-oriented risks, gaps in capabilities, and emerging technologies to enhance wildland firefighting for both near-term and far-term concepts, with a specific focus on aviation operations and (2) engage the stakeholder community in defining emergent safety-oriented scope, roles, responsibilities, and procedures for agents undergoing increasingly complex wildland firefighting operations in information-rich, but uncertain environments. Workshop participants were solicited from wildland firefighting stakeholders across government, industry, and academia. All levels of government were engaged, as NASA sought attendees from federal, state, local, and tribal government agencies. Industry participants from traditional wildland firefighting domains such as data visualization and equipment manufacturers were invited, and corporate attendees from novel application domains such as aerial robotics and autonomous systems were present as well. The top three findings were as follows: (1) Enhancing situation awareness is a safety priority, especially in the use of aerial assets; (2) Timely access to information along with data fusion and integrated displays will enhance safety-critical decision-making both inside and outside aviation contexts; and (3) Tailorable standards and common operating pictures in the field will enhance inter-agency cooperation in the wildland firefighting lifecycle and enable the optimal use of limited resources such as aerial assets. The workshop helped inform NASA of the relevant safety-related wildland firefighting concerns and aided the broader ecosystem in understanding the potential safety-oriented role NASA might play in this community. Increased engagement with crucial governmental stakeholders (e.g., U.S. Forest Service, CAL FIRE, etc.) along with industry partners in cutting- edge information -centric domains is a fundamental next step. Additionally, the workshop findings will help define the first of a series of operationally challenging demonstrations, held in concert with strategic ecosystem partners, known as the Safety Demonstrator Series for NASA’s System-Wide Safety project. The first demonstration is set in the wildland firefighting application domain and will: (1) examine high risk operational scenarios to reduce their overall risk via services, functions or capabilities that act as risk mitigators (or transfer that risk to automated systems better able to tolerate it) and (2) explore novel tools and technologies that will enhance safety margins by enabling non-traditional or neoteric operational paradigms.

wildland firefighting↗

NASA's Orbital Debris Optical Program: ES-MCAT Updated and Upgraded

The 1.3m ES-MCAT telescope (or MCAT for short) now has a proven capability for observing objects from Low- Earth Orbit (LEO) out to Geosynchronous (GEO) orbit, and the ability to run all systems autonomously. A GEO survey, the initial focus for MCAT, will commence in late 2019 to map out the current state of the GEO population as input for the ORbital Debris Engineering Model (ORDEM 4.x). This survey will statistically sample the GEO belt (0 to ~15 deg orbital inclinations) to detect both known and unknown targets. If a break-up occurs, additional surveys of the break-up field can be followed for discovery and investigations of daughter debris fragments from the parent satellite. Discovery can be accomplished by tracking orbits near to and including the parent object’s orbit. Targeted observations of debris can be taken with a suite of broadband filters for characterizing individual objects by ratetracking their known or calculated orbital elements (Two-Line Element sets, TLEs). Several modifications and upgrades have been made to the instrumentation and systems originally installed in 2015 and are reported here. In 2018, MCAT’s primary mirror was recoated with a high-end protected, enhanced silver by the ZeCoat Corporation. The CCD chip was replaced in the Spectral Instruments camera with a broad-band antireflective coated chip. The automated weather systems have been modified from the original system, removing some weather sensors and installing replacements that are better suited to Ascension’s weather and environment. A new 2.5-m ObservaDome replaced the Astrohaven dome on the nearby tower platform that will house an auxiliary 0.4-meter telescope. Finally, in 2019, the Observatory Control System was upgraded to 2.0 which includes additional flexibility for automating data collection and reduction. With these updates completed, MCAT is now well on track to reach Full Operational Capability (FOC) in 2019 for its survey, rate-track, and TLE tracking capabilities.

Lederer, S. M.↗

Double Asteroid Redirection Test (DART) Mission

NASA’s Double Asteroid Redirection Test (DART) mission was humanity’s first attempt to move a celestial body, demonstrating the capability to perform a kinetic impact on a planetary defense–relevant sized asteroid. DART was part of the international collaboration known as the Asteroid Impact & Deflection Assessment (AIDA), involving NASA, the European Space Agency (ESA), the Agenzia Spaziale Italiana (ASI), and scientists around the world. DART was a key step to demonstrating preparedness to respond to planetary defense scenarios, and it provides a crucial data point for likely outcomes. Near-Earth objects (NEOs) greater than 140 m in size are of particular interest to planetary defense because they have the potential to cause significant damage if they were to impact Earth, and also because they are difficult to detect, with less than 50% of the predicted population discovered as of 2022 (National Academies Press, 2022). With an appropriately sized spacecraft and enough warning (typically many years to decades), a kinetic impact can slightly alter the orbit of an asteroid in a way that, over time, prevents the asteroid from colliding with Earth in the future. DART’s target was Dimorphos, the smaller (~150-m-diameter) member of the binary asteroid system (65803) Didymos, which is a near-Earth, potentially hazardous, and well-characterized asteroid system. By simply observing changes to the system after impact and comparing them with a pre-impact reference, it was possible to use ground-based telescopes to observe the deflection in the orbit of Dimorphos after impact. Developed and operated by the Johns Hopkins University Applied Physics Laboratory (APL), the mission entered formulation in 2015 after multiple years of concept development. The project was administered according to NPR 7120.5, with technical oversight and funding through the Planetary Missions Program Office (PMPO) at Marshall Space Flight Center (MSFC) and overall support as a directed mission from NASA’s Planetary Defense Coordination Office (PDCO). The DART spacecraft hosted a singular payload, the Didymos Reconnaissance and Asteroid Camera for Optical navigation (DRACO), and a deployable CubeSat contributed by ASI named the Light Italian CubeSat for Imaging of Asteroids (LICIACube). On 11 September 2022, DART deployed LICIACube, which subsequently followed the DART spacecraft at a safe distance and observed the immediate aftermath of the DART impact. DART was designed to autonomously detect, navigate to, and impact Dimorphos. This autonomous design was chosen to maximize the probability of impact, since commanding from the ground could result in course corrections arriving too late. On the day of impact, 26 September 2022, the spacecraft’s autonomous systems successfully detected and locked on to Dimorphos, impacting its surface within 2 m of the center of the illuminated figure (Jensenius et al., 2023). No human intervention was required for a successful impact, demonstrating that humanity possesses the technology to perform a kinetic impact. Within 2 weeks of impact, it was clear that the orbit of Dimorphos had been significantly altered. On 11 October 2022, NASA Administrator Bill Nelson announced that the new orbital period of Dimorphos was shortened by approximately 32 ± 2 min, from 11 h and 55 min before impact to 11 h and 23 min after impact. With additional observations over the following months, the accuracy of this measurement improved to a –33.24 min ± 1.4 s orbital period change (Naidu et al., 2023; Scheirich et al., 2023), and Beta (β), the momentum transfer enhancement parameter, was reported to be 3.6 (Cheng et al., 2023). Subsequent studies examined the details of DART’s impact site, modeled the impact event, investigated the ejecta produced, and analyzed the dynamics of the Didymos system. These combined results clearly demonstrate that the project met all Level 1 mission requirements.

asteroid↗