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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Abstract Physics‐driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics‐based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure–property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk‐ and edge plasmon discovery in MnPS 3 , a lesser‐known beam‐sensitive layered 2D material. This approach is universal, can be directly used as‐is with any specimen, and is expected to be applicable to any probe‐based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

42 ENGINEERING↗
DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Analysis of the Impact of Radiation on Cameras and Robotic Perception - 19242

Robotic systems are increasingly used for inspection, maintenance, and disaster assistance for nuclear power plants. These robots utilize increasingly high levels of autonomy to operate in environments that are unsafe for humans to operate the robot in or require more precise control over the robot than a human can offer. In these cases, the robot must include perception and computation components to autonomously sense and navigate through the environment to fulfill its mission. This difficult task is complicated by radiation interfering with sensors' operation. The impact of this interference is largely unexplored, as existing works on radiation interference on sensors focus on estimating the incident radiation, rather than how it impacts a robotic system using the sensor. In this work, we seek to quantify and describe the impact of radiation on cameras, and how the radiation affects robotic performance. In this paper we study cameras that are suitable for small, lightweight unmanned aerial vehicles (UAVs) that are increasingly deployed to fly in confined areas in emergencies, where the surrounding environment is unsafe for workers to operate. These small UAVs lack the payload capacity to carry significant radiation shielding, so any onboard sensors will be affected by radiation. In the case of cameras, this radiation appears as noise in the camera imagery. This noise will interfere with the UAV's operation, by obscuring obstacles and interfering with localization and optical flow algorithms that detect detailed features in the environment. To date, there is a lack of information on how the radiation noise in the camera image impacts the overall robot system performance Existing work focuses on estimating the amount of incident radiation by measuring the corruption in the images. However, these works suffer several shortcomings for robotic systems. First, a robot must be able to successfully navigate its environment during a successful mission, and merely quantifying the amount of radiation noise in an image does not adequately explain how it may interfere with higher level navigation and perception tasks. Second, radiation estimation procedures rely on focusing the camera on a known and static background. This is unsuitable for robotic exploration tasks, where the robot frequently moves around an unknown environment that may be dynamically changing. Our work contains two contributions that address these shortcomings. First, we conduct a detailed analysis of the noise radiation causes in cameras by exposing cameras that are commonly used on UAVs [e.g., small form factor complementary metal-oxide-semiconductor (CMOS) cameras] to varying levels of radiation that may be present in emergency scenarios. From this exposure, we quantify not only how much noise is present, but quantify its appearance. By quantifying the appearance and amount of noise in a camera image, researchers can design mitigation strategies for the corruption, so that their robotic systems can operate in these challenging environments. Second, we demonstrate how to leverage this experimental data in robotic simulations. Using data from the first set of experiments allows us to test and verify the robots in simulation. Using simulators allows us to verify the robots will perform as expected during critical times without needing expensive test equipment or damaging valuable robot prototypes. Other researchers using robots for exploration of irradiated environments will benefit from our research by incorporating similar noise models in their research. 1 Southwest Research Institute{sup R} is a registered trademark of Southwest Research Institute in the United States and/or other countries. This will increase the reliability of robotic systems prior to deployment in emergency situations, which will in turn increase their effectiveness and speed the development of robots that would otherwise need expensive and time-consuming test procedures to validate. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Advanced Laboratory and Field Arrays: Technical Report for Autonomous Monitoring & Intervention (Task 2)

The goal for this task was to develop software for an autonomous underwater vehicle (AUV) capable of performing inspection, monitoring, and intervention operations in marine renewable energy arrays, employing motion planning and probabilistic navigation methods. Inspection and manipulation tasks were chosen to be applicable to multiple device types without requiring expensive components. Minimal human intervention reduces costs and also improves efficiency. Using AUVs for inspection and monitoring with limited human intervention and low-cost infrastructure is viewed as critical to the strategy for bringing down O&M costs. The final deliverable for this task was to enable a decrease in the time for maintenance and intervention in marine renewable energy arrays by up to 30% versus using tele-operated ROVs.

13 HYDRO ENERGY↗
DOE OSTI2015

Security vulnerabilities of connected vehicle streams and their impact on cooperative driving

Autonomous vehicles capable of navigating unpredictable real-world environments with little human feedback are a reality today. Such systems rely heavily on onboard sensors such as cameras, radar/LIDAR, and GPS as well as capabilities such as 3G/4G connectivity and V2V/V2I communication to make real-time maneuvering decisions. Autonomous vehicle control imposes very strict requirements on the security of the communication channels used by the vehicle to exchange information as well as the control logic that performs complex driving tasks such as adapting vehicle velocity or changing lanes. This research presents a first look at the effects of security attacks on the communication channel as well as sensor tampering of a connected vehicle stream equipped to achieve CACC. Our simulation results show that an insider attack can cause significant instability in the CACC vehicle stream. We also outline how different countermeasures, such as downgrading to ACC mode, could potentially be used to improve the security and safety of the connected vehicle streams.

42 ENGINEERING↗
DOE OSTI2018

Robot Navigation in a Rich Information Map for Inspection in Nuclear Environments - 18596

This paper discusses our approach of using an unmanned ground vehicle (UGV) to perform inspection tasks in nuclear environments. To reduce people's exposure to the environment with elevated radiation levels, our goal is enabling UGV to navigate and perform inspections fully autonomous with no human getting involved. To achieve the full automation, the UGV can take use of a rich information map. In traditional robot navigation, the 2D global cost map used for planning path consists only obstacle locations, while a rich information map also includes location information of interested objects to robot. The UGV can then use a rich information map to perform routine inspection tasks with known object locations. In this paper, we will present our method of generating a rich information map and the usage of this map for International Atomic Energy Agency (IAEA) Robotics Challenge. (authors)

42 ENGINEERING↗
DOE OSTI2019

Autonomy for Hypersonics Mission Campaign (A4H)

A4H is developing autonomous hypersonic flight vehicles that can intelligently navigate, guide, and control themselves and home-in on targets. Autonomous systems are characterized by the use of closed-loop SENSE-THINK-ACT operations to achieve their desired goals. Closing the SENSE-THINK-ACT loop onboard these systems will provide an improved ability to engage diverse targets in contested environments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Real World Use Case Evaluation of Radar Retro-reflectors for Autonomous Vehicle Lane Detection Applications

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical R2 score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.

Brown, Nicolas E.↗
DOE OSTI2023

Increased range and contrast in fog with circularly polarized imaging

Fogs, low lying clouds, and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on optical sensors for navigation whose performance is degraded by highly scattering environments. In our previous simulation work, we have shown that polarized light can penetrate through a scattering environment such as fog. We have demonstrated that circularly polarized light maintains its initial polarization state better than linearly polarized light, even through large numbers of scattering events and thus ranges. This has recently been experimentally verified by other researchers. In this work, we present the design, construction, and testing of active polarization imagers at short-wave infrared and visible wavelengths. We explore multiple polarimetric configurations for the imagers, focusing on linear and circular polarization states. The polarized imagers were tested at the Sandia National Laboratories Fog Chamber under realistic fog conditions. Here, we show that active circular polarization imagers can increase range and contrast in fog better than linear polarization imagers. We show that when imaging typical road sign and safety retro-reflective films, circularly polarized imaging has enhanced contrast throughout most fog densities/ranges compared to linearly polarized imaging and can penetrate over 15 to 25 m into the fog beyond the range limit of linearly polarized imaging, with a strong dependence on the interaction of the polarization state with the target materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Autonomous Hypersonics

A new race is emerging among nuclear powers: the hypersonic weapon. Hypersonics are flight vehicles that travel at Mach 5 (five times the speed of sound) or faster. They can cruise in the atmosphere, unlike traditional exo-atmospheric ballistic missiles, allowing stealth and maneuverability during midflight. Faster, lower, and stealthier means the missiles can better evade adversary defense systems. The U.S. has experimented with hypersonics for years, but current investments by Russia and China into their own offensive hypersonic systems may render U.S. missile defense systems ineffective. For the U.S. to avoid obsolescence in this strategically significant technology arena, hypersonics—combined with autonomy—needs to be a force multiplier. Achieving an autonomous hypersonic missile, however, that can intelligently navigate, guide, and control itself and home-in on targets ranging from traditional stationary systems to targets that are themselves hypersonic vehicles—with all the maneuverability that this entails—may sound far-fetched. But to Sandia's Autonomy for Hypersonics (A4H) team, this dream is one step closer to reality.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

Autonomous Radiation Survey Inside Contaminated Buildings at Fukushima Daiichi with the Riser UAV - 18299

Nuclear decommissioning is constrained and defined by radiation and radioactive contamination. Where radiation is high and its distribution is unknown simply gathering the data needed for project planning can become a challenge. Fukushima Daiichi epitomises this challenge. The complex sequence of events at the plant following the March 2011 earthquake and tsunami has resulted in extensive physical damage and an essentially unpredictable distribution of contamination. TEPCO have applied a range of manual and robotics techniques to map the status of the plant, but many significant areas have remained no-go zones due to a combination of high radiation, physical hazards and obstacles. Further, even in areas where manual survey is feasible, there is strong desire to minimise human exposure to radiation by deploying robots where possible. Riser is a fully autonomous UAV equipped with an infrastructure-free 3D navigation and mapping system, inspection cameras and a CZT spectrometric measurement capability. The system has been custom designed specifically to meet the needs of the nuclear inspection. Riser is capable of accessing areas with dose rates of hundreds of milli-sievert per hour and producing 3D models and radiation surveys with accurately located data points. The data from Riser can subsequently be fused using Createc's N-Visage engine to produce three-dimensional contamination maps which can be used for predictive modelling of future D and D activity. Riser has been deployed at Fukushima Daiichi since March 2017. We present examples of data and analysis outputs, as well as general learning about deploying autonomous robots at a nuclear site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner

We report in this paper describes how the performance of motion primitive-based planning algorithms can be improved using reinforcement learning. Specifically, we describe and evaluate a framework that autonomously improves the performance of a primitive-based motion planner. The improvement process consists of three phases: exploration, extraction, and reward updates. This process can be iterated continuously to provide successive improvement. The exploration step generates new trajectories, and the extraction step identifies new primitives from these trajectories. These primitives are then used to update rewards for continued exploration. This framework required novel shaping rewards, development of a primitive extraction algorithm, and modification of the Hybrid A* algorithm. The framework is tested on a navigation task using a nonlinear F-16 model. The framework autonomously added 91 motion primitives to the primitive library and reduced average path cost by 21.6 seconds, or 35.75% of the original cost. The learned primitives are applied to an obstacle field navigation task, which was not used in training, and reduced path cost by 16.3 seconds, or 24.1%. Additionally, two heuristics for the modified Hybrid A* algorithm are designed to improve effective branching factor.

42 ENGINEERING↗
DOE OSTI2025

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS 2 ANE (Integrated Novelty Score−Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less promising by conventional criteria. We validate this approach on a preacquired data set with a known ground truth comprising of image−spectral pairs. We further implement the process on autonomous scanning probe microscopy experiments. INS 2 ANE significantly increases the diversity of explored phenomena in comparison to conventional optimization routines, enhancing the likelihood of discovering previously unobserved phenomena. These results demonstrate the potential for autonomous microscopy experiments to enhance the scientific discovery by navigating complex experimental spaces to uncover novel phenomena.

Materials↗
DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗