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

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Autonomous Acquisition of Science Quality Spectra from Unmanned Aerial Systems

This is a presentation on a technique for using Unmanned Aerial Systems with intelligent sensing to create science quality data. In this particular case, the team used two spectrometers with high spectral resolution and developed a technique to have the spectrometers self calibrate so that it is possible to take the measurements within the allocated 10 minute flight times. Because there is both upwelling and downwelling foreoptics on each of the two spectrometers, it becomes slow to set the calibration up for all four possibilities which relates to the integration time and then take the measurements. If light conditions change too rapidly then the measurements won't be scaled so that the spectra max is at least 85% of the instrument max but not saturated. This process was more difficult then it appeared due to the complexity of determining the integration time before light conditions changed and before the battery runs our of charge to fly the Unmanned Aerial System. This presentation goes over the results for the first few flight days and the planned future flight experiments.

Mandl, Daniel↗
NASA NTRSDate not supplied

Monitoring ICAROUS: From Requirements to Autonomous Flight

The Independent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) is a software architecture incorporating a set of algorithms to enable autonomous operations of unmanned aircraft applications. This paper provides an overview of Monitoring ICAROUS, a project whose objective is to provide a formal approach to generating runtime monitors for autonomous systems from requirements written in a structured natural language. This approach integrates FRET, a formal requirement elicitation and authoring tool, and Copilot, a runtime verification framework. FRET is used to specify formal requirements in structured natural language. These requirements are translated into temporal logic formulae. Copilot is then used to generate executable runtime monitors from these temporal logic specifications. The generated monitors are directly integrated into ICAROUS to perform runtime verification during flight.

Formal Methods↗
NASA NTRSDate not supplied

Adaptive Stress Testing: Using Reinforcement Learning to Find Failures in Safety-Critical Systems

Emerging applications in artificial intelligence, such as driverless cars and autonomous aircraft promise to be more efficient, cheaper to operate, and always available. However, ensuring the safety of these systems remains a major challenge to their certification and adoption. These autonomous systems are expected to routinely make safety-critical decisions where failures can have serious consequences including loss of life and property. Testing and validation techniques aim to identify and diagnose potential failures before the system is deployed. However, finding failure scenarios in autonomous systems can be very challenging due to high-dimensional and continuous state spaces, interaction with large environments over many time steps, and the rarity of failures. This talk presents Adaptive Stress Testing (AST), a simulation-based testing framework for finding the most likely path to a failure event of a safety-critical system. The key idea of AST is that stress testing can be formulated as a Partially Observable Markov Decision Process (POMDP), which enables reinforcement learning techniques to be used for finding failure events. Reinforcement learning algorithms can efficiently explore the search space and have been shown to scale to very large systems. We present applications of AST to find failures in various safety-critical systems including the aircraft collision avoidance systems, autonomous cars, and small unmanned aerial vehicles.

autonomous vehicles↗
NASA NTRSDate not supplied

Evaluating Faulty State Occurrence in Wildfire UAS Missions Using Markov Chains

As autonomous technology advances, unmanned aircraft systems are increasingly integrated into emergency response missions, such as wildfire response. These systems must be be safe with less risk than non-autonomous counter parts, yet quantifying the risk associated with present-day and future systems conventionally relies solely on expert opinion and little data. Instead, combining narrative mishap reports with probabilistic analysis can provide a method for evolutionary and timely risk analysis. In this paper, we present a framework for a data-driven probabilistic risk assessment style analysis, where hazard events and rates originate from documented UAS mishaps. The framework is applied to a UAS mapping mission in wildfire response, including a fault tree analysis, event tree analysis, and probabilistic analysis using Markov Chains. The analysis provides an enumeration of hazards in the system, hazard events that can lead to faults, the probability of a mission experiencing any fault, the probability of experiencing a specific fault, and the expected time spent until faulty states occur in present-day operations.

risk analysis↗
NASA NTRSDate not supplied

Evaluating Faulty State Occurrence in Wildfire UAS Missions Using Markov Chains

As autonomous technology advances, unmanned aircraft systems are increasingly integrated into emergency response missions, such as wildfire response. These systems must be be safe with less risk than non-autonomous counter parts, yet quantifying the risk associated with present-day and future systems conventionally relies solely on expert opinion and little data. Instead, combining narrative mishap reports with probabilistic analysis can provide a method for evolutionary and timely risk analysis. In this paper, we present a framework for a data-driven probabilistic risk assessment style analysis, where hazard events and rates originate from documented UAS mishaps. The framework is applied to a UAS mapping mission in wildfire response, including a fault tree analysis, event tree analysis, and probabilistic analysis using Markov Chains. The analysis provides an enumeration of hazards in the system, hazard events that can lead to faults, the probability of a mission experiencing any fault, the probability of experiencing a specific fault, and the expected time spent until faulty states occur in present-day operations.

risk analysis↗
NASA NTRS2003

Holarchical Systems and Emotional Holons : Biologically-Inspired System Designs for Control of Autonomous Aerial Vehicles

The BEES (Bio-inspired Engineering for Exploration Systems) for Mars project at NASA Ames Research Center has the goal of developing bio-inspired flight control strategies to enable aerial explorers for Mars scientific investigations. This paper presents a summary of our ongoing research into biologically inspired system designs for control of unmanned autonomous aerial vehicle communities for Mars exploration. First, we present cooperative design considerations for robotic explorers based on the holarchical nature of biological systems and communities. Second, an outline of an architecture for cognitive decision making and control of individual robotic explorers is presented, modeled after the emotional nervous system of cognitive biological systems. Keywords: Holarchy, Biologically Inspired, Emotional UAV Flight Control

Ippolito, Corey↗
NASA NTRS2004

Approach for Autonomous Control of Unmanned Aerial Vehicle Using Intelligent Agents for Knowledge Creation

This paper describes the development of a planned approach for Autonomous operation of an Unmanned Aerial Vehicle (UAV). A Hybrid approach will seek to provide Knowledge Generation through the application of Artificial Intelligence (AI) and Intelligent Agents (IA) for UAV control. The applications of several different types of AI techniques for flight are explored during this research effort. The research concentration is directed to the application of different AI methods within the UAV arena. By evaluating AI and biological system approaches. which include Expert Systems, Neural Networks. Intelligent Agents, Fuzzy Logic, and Complex Adaptive Systems, a new insight may be gained into the benefits of AI and CAS techniques applied to achieving true autonomous operation of these systems. Although flight systems were explored, the benefits should apply to many Unmanned Vehicles such as: Rovers. Ocean Explorers, Robots, and autonomous operation systems. A portion of the flight system is broken down into control agents that represent the intelligent agent approach used in AI. After the completion of a successful approach, a framework for applying an intelligent agent is presented. The initial results from simulation of a security agent for communication are presented.

Dufrene, Warren R., Jr.↗
NASA NTRS2009

A Robust Mechanical Sensing System for Unmanned Sea Surface Vehicles

The need for autonomous navigation and intelligent control of unmanned sea surface vehicles requires a mechanically robust sensing architecture that is watertight, durable, and insensitive to vibration and shock loading. The sensing system developed here comprises four black and white cameras and a single color camera. The cameras are rigidly mounted to a camera bar that can be reconfigured to mount multiple vehicles, and act as both navigational cameras and application cameras. The cameras are housed in watertight casings to protect them and their electronics from moisture and wave splashes. Two of the black and white cameras are positioned to provide lateral vision. They are angled away from the front of the vehicle at horizontal angles to provide ideal fields of view for mapping and autonomous navigation. The other two black and white cameras are positioned at an angle into the color camera's field of view to support vehicle applications. These two cameras provide an overlap, as well as a backup to the front camera. The color camera is positioned directly in the middle of the bar, aimed straight ahead. This system is applicable to any sea-going vehicle, both on Earth and in space.

Kulczycki, Eric A.↗
NASA NTRS2010

Autonomous Planning and Replanning for Mine-Sweeping Unmanned Underwater Vehicles

This software generates high-quality plans for carrying out mine-sweeping activities under resource constraints. The autonomous planning and replanning system for unmanned underwater vehicles (UUVs) takes as input a set of prioritized mine-sweep regions, and a specification of available UUV resources including available battery energy, data storage, and time available for accomplishing the mission. Mine-sweep areas vary in location, size of area to be swept, and importance of the region. The planner also works with a model of the UUV, as well as a model of the power consumption of the vehicle when idle and when moving.

Gaines, Daniel M.↗
NASA NTRS2015

Mars OASIS (Mars Operational Agricultural System for In-Situ Specialization) System Reference Manual

In order to enable long term habitation on planetary surfaces, a means of sustainable food production must be developed. Addressing this need for surface habitats on Mars, the MarsOASIS team has developed a concept for a Martian surface greenhouse for unmanned crop production research as a proof of concept for larger scale food production facilities for manned surface missions. Utilizing in-situ resources such as the Martian atmosphere, sunlight, and UV-C radiation, the greenhouse aims to provide a sustainable method of long-term food production requiring minimal consumable resources. The MarsOASIS system is capable of growing a full life cycle of Outredgeous lettuce with its autonomous control system designed for a unmanned environment, only requiring teleoperation in extreme circumstances. A reduced-scope prototype of MarsOASIS is being developed to test technologies such as a natural/artificial hybrid lighting system, a closed water recycling system, remote teleoperation, and fully autonomous monitoring and control of the greenhouse. The prototype is currently in the final stages of design, with a full demonstration of plant life cycle testing set to occur in summer 2015. Results from this prototype demonstration will help quantify the feasibility of the innovative approaches seen in the MarsOASIS design.

Asa Darnell↗
NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗
NASA NTRSDate not supplied

Ground to Air Testing of a Fused Optical-Radar Aircraft Detection and Tracking System

Onboard detection and tracking capability are integral to the sensing component in collision avoidance systems needed to safely operate autonomous urban air taxis and small Unmanned Aircraft Systems. Ground-based validation of detection and tracking systems is an important milestone towards the end goal of real-time collision avoidance using onboard sensors and algorithms. In this work, we evaluate three Extended Kalman Filter(EKF)based fusion trackers with radar and vision detection inputs, and compare them with baseline trackers for each sensor type. Performance is assessed using field collected data of ground to air test flights with the sensors co-located on a stable platform with an instrumented multirotor acting as the intruder performing a waypoint pattern at a distance of1.1km to 0.3km to simulate a head-on collision geometry. Fusing an image-based morphological detector with a radar detector using an EKF covered 74% of the ground truth position updates logged by the flight controller on the multirotor within 50 meters after accounting for alignment offsets while covering 15% more ground truth updates relative to radar only. Removing timestamps when the intruder aircraft is occluded by trees and only considering timestamps where the radar has an update, the EKF image-based morphological detector combined with the radar detector covered 90% of the of the ground truth position updates within 50metersand 97% within 100 meters.

Chester V Dolph↗
NASA NTRS2020

Overview and Initial Results of SAND-E: Semi-Autonomous Navigation for Detrital Environments

Unmanned aerial systems (UAS) and automated terrain analysis for science and navigation are new technologies for planetary exploration. The Mars Helicopter will fly with the Mars2020 rover, the Dragonfly quadcopter will explore Titan, and Soil Properties and Object Classification (SPOC) software will be used for path planning and navigation on the Mars2020 rover. Using an Argo J5 rover instrumented with stereo cameras and Autonomous Soil Assessment System (ASAS) software, and an off the shelf quadcopter, SAND-E tested the use of automated terrain analysis and UAS data for science operations in a Mars-analog environment in Iceland during July of 2019. Scientifically, we sought to determine changes in the physical and chemical properties of sediments along a glacial-fluvial-aeolian transport pathway. Operationally, we tested rover mission-like scenarios that included UAS images and classified terrain images. Here, we present the initial results for both the operations and science elements of the study. Site Selection: A goal of SAND-E is examine sorting and alteration of sediments in fluvial and aeolian environments in both mineral-dominated and glass-dominated basaltic settings. During the first year of the project we focused on a mineral-dominated environment. Selection of the location was based on prior publications that indicated our selected region had a greater abundance of crystalline sediments than other areas fluvial-aeolian settings in Iceland. Other criteria included the presence of both fluvial and aeolian landforms along a transport pathway such that the sediments in transport could be linked to their source rocks. We chose the Skjaldbreidauhraun glacial outwash plain, which sits at the base of Thórisjökull glacier. The site is 30 km north of Thingvellir National Park and ~2 hours from Reykjavik. The outwash plain is fed by two small catchments that drain from the base of the glacier and cut through hyaloclastite and shield volcano bedrock. The drainage progresses from steep alluvial fans near the glacier into a low-sloping fluvial braidplain that becomes confined by the Skjaldbreidur shield volcano and creates a shallow canyon cut into lava bedrock. The fluvial system was a typical braided alluvial environment composed pebble- and cobble-bedded longitudinal bars and sandy channel beds. The river remained active and fluctuated in response to diurnal runoff cycles near the glacier before disappearing into the sandy substrate downstream. The high concentration of suspended sediment in the river was evident by the cloudy water and the silt and clay-sized sediments that draped the channel beds after abandonment and created playas in the lowest sloping areas of the catchment. The entire fluvial system was affected by the winds generated by frontal systems and katabatic flows descending the glacier. This resulted in the formation of aeolian lag deposits and a wind-deflation plain where the fluvial system was not active. Wind ripples and drifts formed in abandoned fluvial channels from aeolian reworking of the sand-sized fluvial sediments. The silt- and clay-sized sediments found in fluvial channels, bar tops, and playas generated dust plumes during high wind events. Our operation sought to capture the variability in this system by sampling from the range of fluvial and aeolian features 6.3 km (proximal), 11.3 km (medial), and 14.4 km (distal) along the river from its origin at the base of glacier.

Ewing, R. C.↗
NASA NTRSDate not supplied

Intelligent Change Detection System: Autonomous Intelligent Machine Agent Model Development

NASA’s significant role in facilitating the harmonious integration of unmanned aircraft systems (UAS), with other aerial vehicles operating in the National Airspace System (NAS), has revealed a need for more advanced technological tools than are being utilized currently. This technology would lend itself to significantly assisting Direct-Action Aviation Personnel (DAAP) with the ingress and egress of UAS operations within the NAS. Providing research findings that would reduce technical barriers, associated with UAS-NAS integration, has been a persistent effort by both NASA and the FAA. One such research effort, pursued by NASA’s Transformative Tools and Technologies – Revolutionary Aviation Mobility (T3-RAM) project, is the development of an autonomous intelligent machine (AIM) agent that would aid DAAP functioning as implemented in remote ground control stations (RGCS). This evolution of the “human-machine” symbiosis, within the aviation environment, is necessary for many reasons. For example, there are projections of large increases to the 864,000 registered UAS and 45,000 aviation operations taking place in the NAS each day. With a data output range from 1 to 20 terabytes per flight or each aerial vehicle, which is projected to have proportional rate increase to that of registered UASs. It is evident, that due to the projected increase of UASs and their generated data, the human-agent’s data managing capabilities will be quickly overwhelmed by the enormous amounts of data emanating in the NAS. The research efforts presented in this paper puts forward results from the development, assessment, and verification of a previously conceptualized AIM-Agent that combats actionable-data (information) errors resulting from the visual perception phenomenon known as “change blindness” (CB). CB has been identified as one of the main culprits of information erroring encountered within the ground control station operator (GCSO) community.

Change Blindness↗
NASA NTRS2004

Approach for Autonomous Control of Unmanned Aerial Vehicle Using Intelligent Agents for Knowledge Creation

This paper describes the development of a planned approach for Autonomous operation of an Unmanned Aerial Vehicle (UAV). A Hybrid approach will seek to provide Knowledge Generation thru the application of Artificial Intelligence (AI) and Intelligent Agents (IA) for UAV control. The application of many different types of AI techniques for flight will be explored during this research effort. The research concentration will be directed to the application of different AI methods within the UAV arena. By evaluating AI approaches, which will include Expert Systems, Neural Networks, Intelligent Agents, Fuzzy Logic, and Complex Adaptive Systems, a new insight may be gained into the benefits of AI techniques applied to achieving true autonomous operation of these systems thus providing new intellectual merit to this research field. The major area of discussion will be limited to the UAV. The systems of interest include small aircraft, insects, and miniature aircraft. Although flight systems will be explored, the benefits should apply to many Unmanned Vehicles such as: Rovers, Ocean Explorers, Robots, and autonomous operation systems. The flight system will be broken down into control agents that will represent the intelligent agent approach used in AI. After the completion of a successful approach, a framework of applying a Security Overseer will be added in an attempt to address errors, emergencies, failures, damage, or over dynamic environment. The chosen control problem was the landing phase of UAV operation. The initial results from simulation in FlightGear are presented.

Dufrene, Warren R., Jr.↗