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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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Automated Construction of Artificial Lattice Structures with Designer Electronic States
Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.
Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning
Abstract Diverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.
Powering the Blue Economy: A Survey of Station-Keeping Methods for Mooringless Platforms
The term “ocean platform” is used to reference everything from stationary, typically moored, buoys to mobile water vehicles, whether they operate on the ocean’s surface or underwater. For certain applications for which relatively stationary station-keeping conditions are desired, the use of mooring systems is not always a viable alternative either for economic, environmental, regulatory, or otherwise practical reasons, or a combination thereof, (e.g., short deployments, sensitive ecosystems, very deep project sites). Maintaining a platform at a single waypoint or reference location without being moored would require additional control systems and a power source to counteract the drift forces that would naturally displace it. Mobile platforms, which are usually untethered except for remotely operated vehicles, typically require energy input to power their station-keeping capabilities so that they hold or control their location in the ocean. Currently, most of these platforms use combustion engines or batteries for this purpose, which, depending on the specific systems, may be costly, pollute the environment, or create limitations on the length of the deployment. However, powering this kind of platforms with surrounding renewable resources (waves, currents, winds, or sun) has been identified as a promising solution to expand their application. The intent of this report is to investigate station-keeping methods for various ocean platforms that are not moored or otherwise anchored to the ocean floor, or another platform or vessel, paying particular interest to technologies that use marine renewable resources to power their operation, because that is of particular interest to the U.S. Department of Energy’s Powering the Blue Economy (PBE) initiative. As a first step, 72 articles and technical reports related to mooringless station-keeping methods were collected for review. The preliminary literature review provided a broad overview of common themes across the literature from which a descriptive methodology for analyzing various platforms was developed. That is, station-keeping methods were categorized based on their predominant energy source and consumption (renewable, nonrenewable, or hybrid if the platform uses renewable and nonrenewable resources equally), and their localization strategy (drift reduction, “path-planning or “waypoint-holding”). In addition, platform types were segregated into the following groups: buoys, surface drifters, and unoccupied surface vehicles (USVs); offshore renewable energy systems; and unoccupied underwater vehicles (UUVs). The main types of station-keeping methods encountered in this report achieve their intended localization strategy by means of drift mitigation, steering, and/or propulsion. Drift mitigation is commonly accomplished via drogues and sea anchors. Stand-along steering subsystems use control surfaces (e.g., ship rudder, wing sail, etc.) that react to ocean currents, waves, or winds to provide varying-degrees of course adjustments. Combined steering and propulsion subsystems include differential thrusters, directional thrusters separate from a primary thruster that cause the platform to pitch up/down or yaw clockwise/counterclockwise, or vectored thrusters that direct the propulsion in a range of directions relative to the platform’s local coordinate system. Propulsion is often achieved by running a motor and applying active control strategies but can also involve buoyancy shifts and using sails to generate lifting forces that propel a platform in a desired direction. Future research is primarily expected to take place in the form of a technoeconomic analysis that would aim to determine the technological viability, cost, and added value of mooringless station-keeping use cases identified through this research, including docking for UUV recharging or for georeferencing drifter buoys, deep-sea floating wind farms, U.S. Navy sonar arrays, and a Pacific Ocean wave buoy network.
Informative Path Planning for Mapping Radiation
This project involves making measurements of contaminated areas using portable radiation detectors and small robots. Tasks involve operating detectors and small robots, combining data acquisition for the two systems, and automating the systems. The end result is a radiation map of contamination areas. Localization of radioactive sources and mapping of contamination distribution is essential when responding to emergencies, or when dealing with cleanup efforts. Radiation maps are used for situational awareness and for establishing a safe perimeter. However, it may be hazardous or impossible for humans to safely enter the site and survey the area. Informative Path Planning (IPP) is used to design a path for a robotic sensor platform to gather the most information about the radiation distribution while operating under the set of constraints given by the dynamics of the robot and other requirements such as minimizing the overall mission time. The key challenge for IPP is the tight coupling in multiple layers of decisions: Selection of locations to visit; Allocation of sensors to those locations; Processing of information gathered. Given a robot equipped with a basic gamma detector and Lidar sensor, how can we efficiently explore an unknown environment in such a way that we can satisfy the robot's constraints while simultaneously maximizing the accuracy of the map and minimizing the total runtime? This work presents a systematic procedure for IPP and environmental mapping that: Selects the best locations at which to sample; Finds the best path for robots; Estimates the measurement value at unknown locations using a Gaussian Process regression framework. Given a set of points where radiation measurements were taken, we are utilizing a Gaussian Process Regression with a Matern kernel to reconstruct the shape of the underlying ground truth. Mapping is done via the Robot Operating System's OpenSlam Gmapping package, which creates a 2D occupancy grid map. Simulated trials were run with a radiation source placed at random locations, and allowing the robot to select and move towards 20 samples at separate locations, as well as sampling along the path while moving. The simulated sensor model assumed an efficiency of 0.3, collection time of 1 second. Various weighting schemes were tested and compared. Quantitative evaluations often favored exploitation-heavy weighting schemes where μ was emphasized, with the Root Mean Squared Error (RMSE) quickly tending towards 2, whereas qualitative comparisons would favor exploration-biased schemes with greater weight on σ. The difference comes from operators often favoring knowing the general location of a source, rather than the exact properties of a source. Weighting of the path-length π often reduced the quality of the regression as less exploration occurred, however this may be deemed a worthwhile trade-off under certain circumstances, such as with sensitive robots. Successfully developed a methodology for informative path-planning for a robot to efficiently gather information and create a map of radiation. In the future, we would like to focus on: Hardware trials with integration of the robot platform and the IPP software; More refined sensor and environmental modeling; Multiple robot platforms.
6. Closed-Loop Toolpath Generation
Closed-loop paths, also known as closed-loop contours, are paths that start and stop at the same point and are typically used to define the perimeter or outermost boundary of the polygon that represents a given layer of an object, referred to as the “layer polygon.” Finding the location of a closed-loop path involves applying two offsetting steps to the edge of the layer polygon. The offsetting steps respectively serve to (1) find the area that contains the toolpath and (2) find the centerline of that area, to define the path itself. After finding the perimeter path, the remaining geometry from the layer polygon can be used for additional path-planning steps. This chapter will discuss how the location for a closed-loop path is found, including various problem scenarios, and the different types of closed-loop paths used in additive manufacturing.