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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Evolution of Airport Terminal Wayfinding Technologies

This work was presented during the Transportation Research Board (TRB) and Airport Cooperative Research Program (ACRP) webinar "Enhancing the Airport Experience with Wayfinding" on May 23, 2024. The process of navigating within airports has evolved over the years, with various technologies emerging to complement static signage. Historically, wayfinding began with static signage and airport staff members providing personal guidance, before progressing into digital signs, interactive kiosks, and displays. In recent years, wayfinding has continued along its digital path using mobile applications, indoor positioning technology, and even robot guides. With ever- larger airport facilities and a growing number of travelers, the future of wayfinding will likely be heavily linked to further digital developments such as facial recognition, augmented-reality technology, and autonomous vehicle navigation. This presentation delves into the history of interior wayfinding in airports, its present state, and the anticipated future. Key wayfinding technologies are discussed with an emphasis on emerging smartphone applications. Other considerations such as legal issues, language barriers, and human-technology interactions are included. A wayfinding framework is proposed, with static wayfinding technologies serving as the base upon which dynamic and personalized digital technologies are built. In this framework, electronic wayfinding technologies do not replace - but rather augment - traditional methods. These technologies can be integrated into existing wayfinding systems for a seamless traveler experience.

airport↗
DOE OSTI2024

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗
DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗
DOE OSTI2020

EPRI Demonstration of an Autonomous Site Characterization Vehicle - 20523

A substantial effort is applied in performing radiological characterization of structures and land areas during decommissioning of a nuclear facility. This includes multiple surveys of land areas and large structures to support eventual site release under the Multi Agency Survey and Site Investigation Manual (MARSSIM, Reference 1). These surveys are typically performed using manually delivered systems, which is time consuming and labor intensive. Radiological surveys intentionally follow a prescribed protocol with respect to areas covered, scan speeds, responses to elevated readings, required detector offsets, the need for repeat measurements, etc. Based on these attributes, combined with the repetitive effort of performing surveys over large areas, automation of radiological characterization techniques has a high potential to be successful and promises substantial benefit. EPRI has performed a project that resulted in the development and demonstration of an autonomous site characterization system. This EPRI project focused on building surfaces and the surfaces of large land areas as these configurations lend themselves more readily to automated survey. The project included: - Identification of the requirements for an autonomous site characterization system; - A survey of existing control systems that use automation to conduct characterization of structures and land areas. - As no fully adequate systems were identified, a complete system was developed that utilized commercially available detection and analysis systems and a self driving vehicle owned by EPRI. The system was demonstrated at the Kewaunee nuclear power plant site in the fall of 2019. The demonstration included: - Autonomous surveys of floor areas inside the Auxiliary Building at Kewaunee after manually controlled mapping of the survey areas. - For land areas, the vehicle autonomously surveyed areas using travel paths had been preprogrammed onto an internet available map of area. In this case, GPS coordinates from the preprogramming were used for navigation by the vehicles guidance system. This paper summarizes the characterization system development and system demonstration which will also be published in an EPRI report in the first half of 2020. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1973

Stochastic estimates of gradient from laser measurements for an autonomous Martian roving vehicle

The general problem of estimating the state vector x from the state equation h = Ax where h, A, and x are all stochastic, is presented. Specifically, the problem is for an autonomous Martian roving vehicle to utilize laser measurements in estimating the gradient of the terrain. Error exists due to two factors - surface roughness and instrumental measurements. The errors in slope depend on the standard deviations of these noise factors. Numerically, the error in gradient is expressed as a function of instrumental inaccuracies. Certain guidelines for the accuracy of permissable gradient must be set. It is found that present technology can meet these guidelines.

Burger, P. A.↗
NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗
NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗
NASA NTRS1973

Stochastic estimates of gradient from laser measurements for an autonomous Martian Roving Vehicle

The general problem presented in this paper is one of estimating the state vector x from the state equation h = Ax, where h, A, and x are all stochastic. Specifically, the problem is for an autonomous Martian Roving Vehicle to utilize laser measurements in estimating the gradient of the terrain. Error exists due to two factors - surface roughness and instrumental measurements. The errors in slope depend on the standard deviations of these noise factors. Numerically, the error in gradient is expressed as a function of instrumental inaccuracies. Certain guidelines for the accuracy of permissable gradient must be set. It is found that present technology can meet these guidelines.-

Shen, C. N.↗
DOE OSTI2022

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

Autonomous vehicle-based traffic smoothing con- trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I-24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human followers. Using policy-gradient methods with an asymmetric critic to learn the controller, we show that we are able to improve average MPG by 11% in simulation on congested trajectories. We deploy this controller to a mixed platoon of 4 autonomous Toyota RAV-4’s and 7 human drivers in a validation experiment and demonstrate that the expected time-gap of the controller is maintained in the real world test. Finally, we release the driving dataset [1], the simulator, and the trained controller at https://github.com/nathanlct/trajectory-training-icra.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

Design of an autonomous lunar construction utility vehicle

In order to prepare a site for a lunar base, an autonomously operated construction vehicle is necessary. Discussed here is a Lunar Construction Utility Vehicle (LCUV), which uses interchangeable construction implements. Design of an elastic loop track system has advanced to the testing stage. A standard coupling device has been designed to insure a proper connection between the different construction tools and the LCUV. Autonomous control of the track drive motors was simulated successfully through the use of a joystick and a computer interface. A study of hydrogen-oxygen fuel cells produced estimates of reactant and product requirements and identified multilayer insulation needs. Research on the 100-kW heat rejection system determined that it is necessary to transport the radiator panel on a utility trailer. Extensive logistical support for the 720 hour use cycle requires further study.

Source record↗
NASA NTRS1972

A simplified satellite navigation system for an autonomous Mars roving vehicle.

The use of a retroflecting satellite and a laser rangefinder to navigate a Martian roving vehicle is considered in this paper. It is shown that a simple system can be employed to perform this task. An error analysis is performed on the navigation equations and it is shown that the error inherent in the scheme proposed can be minimized by the proper choice of measurement geometry. A nonlinear programming approach is used to minimize the navigation error subject to constraints that are due to geometric and laser requirements. The problem is solved for a particular set of laser parameters and the optimal solution is presented.

Janosko, R. E.↗
NASA NTRS1991

Navigation of military and space unmanned ground vehicles in unstructured terrains

Development of unmanned vehicles for local navigation in terrains unstructured by humans is reviewed. Modes of navigation include teleoperation or remote control, computer assisted remote driving (CARD), and semiautonomous navigation (SAN). A first implementation of a CARD system was successfully tested using the Robotic Technology Test Vehicle developed by Jet Propulsion Laboratory. Stereo pictures were transmitted to a remotely located human operator, who performed the sensing, perception, and planning functions of navigation. A computer provided range and angle measurements and the path plan was transmitted to the vehicle which autonomously executed the path. This implementation is to be enhanced by providing passive stereo vision and a reflex control system for autonomously stopping the vehicle if blocked by an obstacle. SAN achievements include implementation of a navigation testbed on a six wheel, three-body articulated rover vehicle, development of SAN algorithms and code, integration of SAN software onto the vehicle, and a successful feasibility demonstration that represents a step forward towards the technology required for long-range exploration of the lunar or Martian surface. The vehicle includes a passive stereo vision system with real-time area-based stereo image correlation, a terrain matcher, a path planner, and a path execution planner.

Lescoe, Paul↗