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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Tran, Loc↗
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.↗
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 NTRSDate not supplied

Uncertainty Quantification of Expected Time-of-Arrival in UAV Flight Trajectory

One of the foremost requirements for accurate in-flight safety monitoring of autonomous unmanned aerial vehicles (UAVs) is tracking of their flight trajectory. Existing UAVs leverage autonomous flight functionalities based on trajectory generation algorithms developed in robotic applications such as polynomial or spline curves in order to facilitate kinematic smoothness, minimum vibrations and fuel efficiency. However in practice, the actual path may be subjected to unexpected local weather conditions, unexpected obstacles along the path or abrupt traffic changes in the low-altitude airspace resulting in large errors of the predicted time-of-arrival at way-points. In this study, an approach to quantify and propagate uncertainty in 4D trajectories is proposed. The paper presents a simple error interval propagation method based on the expected cruise speed of the UAV and its associated uncertainty. The uncertainty is then propagated in time to estimate reasonable confidence intervals on the times-of-arrival of the vehicle at each way-point as well as along the entire flight-path. The uncertainty propagation is demonstrated on a state-of-the-art trajectory generation algorithm based on non-uniform rational B-spline (NURBS) curves. Further, the effect of a stationary wind field is incorporated in the uncertainty propagation approach. The proposed method is implemented on synthetic and real data obtained from flight experiments with a small UAV.

Uncertainty Quantification↗
NASA NTRS2002

Simulation and Flight Control of an Aeroelastic Fixed Wing Micro Aerial Vehicle

Micro aerial vehicles have been the subject of continued interest and development over the last several years. The majority of current vehicle concepts rely on rigid fixed wings or rotors. An alternate design based on an aeroelastic membrane wing has also been developed that exhibits desired characteristics in flight test demonstrations, competition, and in prior aerodynamics studies. This paper presents a simulation model and an assessment of flight control characteristics of the vehicle. Linear state space models of the vehicle associated with typical trimmed level flight conditions and which are suitable for control system design are presented as well. The simulation is used as the basis for the design of a measurement based nonlinear dynamic inversion control system and outer loop guidance system. The vehicle/controller system is the subject of ongoing investigations of autonomous and collaborative control schemes. The results indicate that the design represents a good basis for further development of the micro aerial vehicle for autonomous and collaborative controls research.

Waszak, Martin↗
NASA NTRSDate not supplied

Autonomous Spacecraft Inspection with Free-Flying Drones

This paper describes a proof-of-concept mission demonstrating a multi-agent system performing visual inspection of damage sustained by a spacecraft. Free-flying satellites, simulated by unmanned aerial vehicles (UAVs), autonomously fly around a mock space module maximizing the search space for damage detection. The free-flyers are responsible for independently coordinating their flights to avoid collision with the space module and each other, while executing mission tasks. Damage analysis on the surface of the mock space module is performed in real-time using video from each free-flyer. Three-dimensional modeling is deployed offline to supplement and improve damage detection. This approach demonstrates the feasibility of deploying real space systems for damage detection, where 2D analysis can quickly determine region of interest and 3D visualization can produce a human-navigable virtual environment with depth perspective for further investigation.

unmanned aerial vehicle (UAV)↗
DOE OSTI2024

Mission planning for photogrammetry-based autonomous 3D Mapping of Dams using a commercial UAV

The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.

42 ENGINEERING↗
DOE OSTI2026

Machine Learning Enabled Position Detection for 6.78 MHz UAV Wireless Power Transfer System

This paper presents a novel supervised machine learning (SML) approach for accurate position detection of the receiver coil in wireless power transfer (WPT) systems using only secondary-side electrical measurements, with applications in autonomous unmanned aerial vehicle (UAV) charging. The proposed method trains a supervised learning model to map measured secondary-side voltage and current features to the receiver’s spatial position with high precision. This enables an autonomous UAV to determine its location relative to the primary coil center, the optimal position for maximizing wireless charging efficiency. The sensing method is fully integrated into a standard WPT system, utilizing the same primary and secondary coils for both power transfer and position detection, thereby eliminating additional sensing hardware. The use of a 6.78 MHz operating frequency enhances positional sensitivity, as high-frequency near-field electromagnetic fields respond strongly to small spatial variations. Experimental validation is performed on a 30 W scaled prototype featuring a 210 mm × 140 mm primary coil, a 50 mm × 80 mm receiver coil, and a 15 mm air gap. Results demonstrate reliable position estimation and a strong correlation between predicted position and optimal coil alignment. This integrated framework unifying position detection and wireless charging offers a promising foundation for future autonomous electric vertical takeoff and landing (eVTOL) systems, enabling compact, hardware-efficient, and high-accuracy charging solutions.

Colak, Kerim [New York University]↗
NASA NTRSDate not supplied

3D Representation of UAV-obstacle Collision Risk Under Off-nominal Conditions

Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The risk is represented in terms of a matrix with rows corresponding to the likelihood of occurrence of collision and columns representing severity of collision to the vehicle and surrounding structures. Risk likelihood is generated using a Bayesian Belief Network (BBN) that compiles knowledge from related Failure Modes and Effects Analysis (FMEAs) and Subject Matter Experts (SMEs) to determine the probability of collision based on on-board sensor measurements indicative of vehicle health and controllability. Risk severity is computed utilizing a point-mass 3D kinematic model of the vehicle in presence of wind. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.

risk analysis↗
NASA NTRSDate not supplied

3D Representation of UAV-obstacle Collision Risk under off-nominal conditions

Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk likelihood of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The deviation in the planned trajectory caused due to wind is computed utilizing a point-mass 3D kinematic simulation model of the vehicle. Likelihood of risk for the flight plan is then analyzed based on generating the probability of collision for each point in the trajectory. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, distance from obstacles, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.

Portia Banerjee↗
DOE OSTI2021

Dynamic Interference Management for UAV-Assisted Wireless Networks

Here, we investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.

42 ENGINEERING↗
NASA NTRS2006

Semiautonomous Avionics-and-Sensors System for a UAV

Unmanned Aerial Vehicles (UAVs) autonomous or remotely controlled pilotless aircraft have been recently thrust into the spotlight for military applications, for homeland security, and as test beds for research. In addition to these functions, there are many space applications in which lightweight, inexpensive, small UAVS can be used e.g., to determine the chemical composition and other qualities of the atmospheres of remote planets. Moreover, on Earth, such UAVs can be used to obtain information about weather in various regions; in particular, they can be used to analyze wide-band acoustic signals to aid in determining the complex dynamics of movement of hurricanes. The Advanced Sensors and Electronics group at Langley Research Center has developed an inexpensive, small, integrated avionics-and-sensors system to be installed in a UAV that serves two purposes. The first purpose is to provide flight data to an AI (Artificial Intelligence) controller as part of an autonomous flight-control system. The second purpose is to store data from a subsystem of distributed MEMS (microelectromechanical systems) sensors. Examples of these MEMS sensors include humidity, temperature, and acoustic sensors, plus chemical sensors for detecting various vapors and other gases in the environment. The critical sensors used for flight control are a differential- pressure sensor that is part of an apparatus for determining airspeed, an absolute-pressure sensor for determining altitude, three orthogonal accelerometers for determining tilt and acceleration, and three orthogonal angular-rate detectors (gyroscopes). By using these eight sensors, it is possible to determine the orientation, height, speed, and rates of roll, pitch, and yaw of the UAV. This avionics-and-sensors system is shown in the figure. During the last few years, there has been rapid growth and advancement in the technological disciplines of MEMS, of onboard artificial-intelligence systems, and of smaller, faster, and smarter wireless telemetry systems. The major attraction of MEMS lies in orders-of-magnitude reductions of power requirements relative to traditional electronic components that perform equivalent functions. In addition, the compactness of MEMS, relative to functionally equivalent traditional electronics systems, makes MEMS attractive for UAV applications. Recent advances in MEMS have made it possible to produce pressure, acceleration, humidity, and temperature sensors having masses in subgram range and possessing sensitivities and accuracies comparable to those of larger devices.

Shams, Qamar↗
NASA NTRS2012

An Autonomous Autopilot Control System Design for Small-Scale UAVs

This paper describes the design and implementation of a fully autonomous and programmable autopilot system for small scale autonomous unmanned aerial vehicle (UAV) aircraft. This system was implemented in Reflection and has flown on the Exploration Aerial Vehicle (EAV) platform at NASA Ames Research Center, currently only as a safety backup for an experimental autopilot. The EAV and ground station are built on a component-based architecture called the Reflection Architecture. The Reflection Architecture is a prototype for a real-time embedded plug-and-play avionics system architecture which provides a transport layer for real-time communications between hardware and software components, allowing each component to focus solely on its implementation. The autopilot module described here, although developed in Reflection, contains no design elements dependent on this architecture.

Ippolito, Corey↗
NASA NTRS2016

Serious Gaming for Test & Evaluation of Clean-Slate (Ab Initio) National Airspace System (NAS) Designs

Incremental approaches to air transportation system development inherit current architectural constraints, which, in turn, place hard bounds on system capacity, efficiency of performance, and complexity. To enable airspace operations of the future, a clean-slate (ab initio) airspace design(s) must be considered. This ab initio National Airspace System (NAS) must be capable of accommodating increased traffic density, a broader diversity of aircraft, and on-demand mobility. System and subsystem designs should scale to accommodate the inevitable demand for airspace services that include large numbers of autonomous Unmanned Aerial Vehicles and a paradigm shift in general aviation (e.g., personal air vehicles) in addition to more traditional aerial vehicles such as commercial jetliners and weather balloons. The complex and adaptive nature of ab initio designs for the future NAS requires new approaches to validation, adding a significant physical experimentation component to analytical and simulation tools. In addition to software modeling and simulation, the ability to exercise system solutions in a flight environment will be an essential aspect of validation. The NASA Langley Research Center (LaRC) Autonomy Incubator seeks to develop a flight simulation infrastructure for ab initio modeling and simulation that assumes no specific NAS architecture and models vehicle-to-vehicle behavior to examine interactions and emergent behaviors among hundreds of intelligent aerial agents exhibiting collaborative, cooperative, coordinative, selfish, and malicious behaviors. The air transportation system of the future will be a complex adaptive system (CAS) characterized by complex and sometimes unpredictable (or unpredicted) behaviors that result from temporal and spatial interactions among large numbers of participants. A CAS not only evolves with a changing environment and adapts to it, it is closely coupled to all systems that constitute the environment. Thus, the ecosystem that contains the system and other systems evolves with the CAS as well. The effects of the emerging adaptation and co-evolution are difficult to capture with only combined mathematical and computational experimentation. Therefore, an ab initio flight simulation environment must accommodate individual vehicles, groups of self-organizing vehicles, and large-scale infrastructure behavior. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ab initio simulation environment is similar to online gaming environments in which player participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual player's active participation.

Allen, B. Danette↗
NASA NTRS2019

Real-Time Path Planning for Multi-copters flying in UTM -TCL4

NASA's UAS Traffic management (UTM) -TCL-4 focuses on safely enabling large scale small UAS oper- ations in low altitude airspace in dense urban regions. This paper presents an operational architecture of an autonomous unmanned aerial vehicle operating in TCL4. An on-line path planning scheme is proposed which can effectively plan for feasible paths in real time with TCL-4 constraints. An end to end system is designed and tested in high fidelity Reflection architecture which demonstrates the feasibility of the approach.

Chakrabarty, Anjan↗
NASA NTRS2012

Vision-Aided Autonomous Landing and Ingress of Micro Aerial Vehicles

Micro aerial vehicles have limited sensor suites and computational power. For reconnaissance tasks and to conserve energy, these systems need the ability to autonomously land at vantage points or enter buildings (ingress). But for autonomous navigation, information is needed to identify and guide the vehicle to the target. Vision algorithms can provide egomotion estimation and target detection using input from cameras that are easy to include in miniature systems.

Brockers, Roland↗
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↗