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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Modular Accelerator Robotics Framework for AD Robotics

Accelerator tunnels, such as the ones at Fermilab, remain highly radioactive after beam shutoff due to induced radiation from the beam. This residual radiation creates a hazardous environment for manual inspection and repair of beamline components. To minimize worker radiation dose and reduce beam downtime, the AD Robotics Initiative previously built a fleet of low-cost custom mobile robots. However, the custom Python sockets server-client architecture lacked standardization, causing development delays and complicating the integration of new sensors and actuators. Here, we developed a modular system using ROS2 and Docker to standardize the teleoperation and control interfaces. This system was validated by implementing a teleoperation controller with real-time, low-latency, and high-definition video feedback. The aim of this framework is for a new feature or even a robot to be integrated into the system simply by documenting the hardware configuration. Current integration of LiDAR, Odometry, and Depth Cameras provides the foundation for Simultaneous Localization and Mapping (SLAM) tasks. Finally, future work involves integration into the accelerator control system and the attachment of a 6 degree-of-freedom robotic arm for telemanipulation.

Rayyan Khan, M. [Fermilab; Rensselaer Poly.; Unlis↗
DOE OSTI2026

ARCH Technology Snapshot Autonomous Robot Control Hierarchy (ARCH): A universal software system that removes the need to rebuild robotic software for every new platform or task

Robots are increasingly used to perform repetitive, hazardous, and time-sensitive tasks, improving safety and operational efficiency. However, most robotic systems remain difficult to adapt because they are tightly tied to specific hardware and require extensive reprogramming for each new configuration.

42 ENGINEERING↗
DOE OSTI2026

A Real2Sim Digital Twin Pipeline for Photorealistic Robot Simulation: Evaluating VLA Policy Deployment on a Bimanual Mobile Robot

Digital twins that are automatically constructed from robot sensor data offer a promising pathway for scalable Real2Sim and Sim2Real transfer. However, it remains an open question whether photorealistic reconstruction alone is sufficient to support reliable deployment of vision-language-action (VLA) policies. We present a generative-AI-assisted Real2Sim pipeline that generates simulation-ready digital twins from real-world RGB observations with minimal manual intervention. The pipeline uses prompted segmentation to isolate scene components and a generative 3D model to directly produce simulation assets, eliminating the need for traditional multi-view reconstruction or manual 3D modeling.\r\nTo evaluate simulation fidelity, we deploy and compare policies from two VLA models in both the real robot and the reconstructed\r\nsimulation under identical tasks and initial conditions. We compare joint-level action trajectories and analyze how divergence evolves over time in closed-loop execution. Although the reconstructed environments are visually accurate, we observe increasing trajectory divergence during closedloop operation. These results indicate that photorealistic reconstruction alone is insufficient to preserve closed-loop control behavior\r\nin VLA policies, particularly in contact-rich manipulation settings where small perceptual errors compound over time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Herbicide‐resistant weed management with robots: A weed ecological–economic model

The heavy reliance on herbicides for weed control has led to an increase in resistant weeds in the United States. Robotic weed control is emerging as an alternative technology for removing weeds mechanically using artificial intelligence. We develop an integrated weed ecological and economic dynamic (I‐WEED) model to examine the biophysical and economic drivers of adopting robotic weed management and simulate the optimal timing and intensity of robotic adoption within and across growing seasons. We specify a cohort‐based weed growth model that relates yield damages to effective weed density and treats the susceptibility of weeds to herbicides as a renewable resource that can be regenerated by using mechanical weeding robots, due to a fitness cost that makes resistant weeds less prolific. Compared to myopic weed management which ignores resistance development, forward‐looking management leads to earlier adoption of robots and treating robots as complements instead of substitutes to herbicides. This weed management results in adopting fewer robots, deploying robots on a smaller portion of the land, higher profitability, and lower yield loss in the long run, relative to myopic management. Counterintuitively, myopic management leads to a lower resistance level through its higher robot adoption intensity. We also find that a lower level of initial weed seed resistance and/or a higher fitness cost result in a higher level of resistance because they create incentives for farmers to delay the adoption of robotic weed control. Our analysis shows the importance of jointly considering the interactions between weed ecology and economics in analyzing the incentives and effects of robotic weed management on weed resistance.

agricultural robotics↗
DOE OSTI2026

Online task-space motion control for positioner-coordinated multi-robot manufacturing systems

Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.

Arbogast, Alex [ORNL] (ORCID:0000000154740723)↗
DOE OSTI2026

Synthetic Digital Environments for Training Robots on Earth & Beyond [Poster]

Creating physical replicas of real-world environments to train robots for challenging outdoor tasks, whether constructing energy infrastructure like solar farms on Earth or on the Moon and Mars, is prohibitively expensive. This project will prototype a high-fidelity digital twin framework using NVIDIA IsaacSim to create realistic digital representations of robotic systems and their operating conditions, including varied terrains and environmental factors, allowing robots to learn and adapt in a faster, safer, and more affordable way to tackle unpredictable challenges in terrestrial and extraterrestrial applications. The Robotic Space Exploration (RoSE) Lab at Colorado School of Mines focused on the development and testing of synthetic digital twins to explore multi-physics interactions between robots and unstructured environments, with emphasis on lunar conditions such as deformable regolith, reduced gravity, and terrain-robot contact dynamics. The Industrialized Construction Innovation (ICI) team at National laboratory of the Rockies (NLR), simulated robotic apparatus and construction workflows using synthetic digital twins to inform real-world deployment, targeting application-driven use cases such as robotic construction of a scaled prototype of a photovoltaic energy infrastructure. Joint efforts between RoSE and ICI are continuing to explore how environment-scale multi-physics modeling and application-level robotic system simulation could be integrated to support robotic construction of energy infrastructure in highly unstructured environments, including scenarios relevant to the Lunar South Pole.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗
DOE OSTI2024

Robotics for Systems Integration in Buildings: Pilot Study of Viable Approaches to Install Hygrothermal and Rigid Electrical Systems

The Industrialized Construction Innovation (ICI) team at the National Renewable Energy Laboratory (NREL) has been exploring the use of robotics to integrate hygrothermal, mechanical, electrical, and plumbing systems in prefabricated building assemblies (off-site construction) and 3D-printed buildings (on-site construction). Such multisystem integration tasks often require specialized robots and custom end effectors to handle a range of rigid and nonrigid building components. This paper begins with a brief overview of the current state of robotics in construction, followed by a pilot study exploring the use of robotics to integrate a simple prototype multitrade wall assembly composed of structural studs, hygrothermal layer, wall finishing, and electrical fixtures. The study was funded by the US Department of Energy's (DOE's) Advanced Materials and Manufacturing Technologies Office (AMMTO). Insights about the implementation of design for manufacturing and assembly (DfMA) principles in designing the prototype wall for robotic assembly, and selection of appropriate robotic end-effector hardware to handle these components are included. Detailed comparison of computational toolpath simulations of the robotic assembly process and real-life demonstration of the same are presented. Finally, limitations and lessons learned from this study are included, along with future research recommendations for robotic assembly of more complex multitrade assemblies, including potential scenarios such as robotic outfitting of facilities in extraterrestrial environments.

advanced manufacturing↗
DOE OSTI2026

Robotics for Energy Infrastructure Construction on Earth and Beyond: Pilot Study Exploring Level 3-4 Semi-Autonomous Installation of a Reduced-Scale Photovoltaic Power Plant Prototype: Preprint

There is a growing need to build energy infrastructure at massive scale on both Earth and in space. On Earth, increasing electricity demand from applications such as AI data centers is driving rapid expansion of power systems, while in space, long-duration missions such as lunar operations require reliable and resilient energy infrastructure that can be deployed quickly. These needs outpace the capabilities of conventional construction methods. Robotic construction offers a path to faster, lower-cost, and more consistent deployment, yet most existing approaches focus on automating individual tasks rather than end-to-end assembly. The Industrialized Construction Innovation (ICI) team at the National Laboratory of the Rockies (NLR), in collaboration with the Colorado School of Mines, has been investigating robotics and automation strategies for rapid end-to-end energy infrastructure construction. This paper presents a pilot study demonstrating level 3-4 semi-autonomous, end-to-end robotic assembly of a reduced-scale photovoltaic power plant prototype under terrestrial conditions. The approach combines hardware redesign of photovoltaic components for robotic compatibility, discrete-event and physics-based robotic simulations in synthetic environments, and sim-to-real deployment to execute end-to-end robotic assembly of[NM1.1][NM1.2] tracking components, structural members, photovoltaic modules, and embedded electrical connections. The pilot identifies key limitations like tolerance management, perception and localization, end effector capability, fastening methods, and multi-physics simulation fidelity, that must be addressed to enable end-to-end robotic assembly of full-scale energy infrastructure. The study highlights how these insights inform scaling strategies for terrestrial energy infrastructure and adaptation to unstructured and extreme environments, including off-world scenarios such as robotic assembly of vertical tall lunar towers.

14 SOLAR ENERGY↗
DOE OSTI2025

Teleoperation of Construction Robots Using Human Interaction: Case Study Examples: Preprint

The integration of advanced teleoperation methods in construction robotics has the potential to enhance productivity and safety in demanding environments. Among different methods, hand gestures have emerged as an intuitive approach, enabling seamless communication between human operators and robotic systems. This study demonstrates the use of hand gestures for controlling a model robotic excavator and a virtual robotic arm in construction applications. Two case study examples are presented. In the first case study, a series of hand gestures are performed to control a model robotic excavator's bucket and arm movements. The second case study focuses on using hand gestures to control a virtual robotic arm to perform a material handling task in a simulated environment. In both cases, the performed hand gestures are all successfully captured and interpreted by the gesture-based teleoperation method to conduct the corresponding tasks, which demonstrates the application of the gesture-based control method for various types of construction robots. Also, the limitations of the gesture-based teleoperation method (e.g., recognition delay, sensitivity to motion outliers) are discussed. Future work will focus on developing a digital twin system to support gesture-based teleoperation for a wider range of construction robots.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗
DOE OSTI2025

Integrated soft snake robot for gamma spectroscopy of radiologically contaminated ducting

Anticipated growth in the number and complexity of nuclear facilities entering the decommissioning phase of their lifespan requires innovative methods of mitigating radiological and safety hazards for workers. Advancements in robotics provide opportunities for remote characterization of radiologically contaminated ducts and pipes, reducing worker exposure. However, the nuclear decommissioning industry has shown a hesitancy in adopting new technology due to concerns regarding capital costs and a lack of demonstration data in representative environments. This work aims to leverage novel soft robotics to develop and demonstrate a sensor-integrated characterization tool for contaminated ducting systems. Two gamma spectrometers – one scintillation-based and one solid-state – were selected and integrated into a pneumatically-actuated snake-like soft robot using stretchable electronic cabling. The robot was then demonstrated at the Idaho National Laboratory’s Critical Infrastructure Test Range Complex. The robot maneuvered through a pseudo-constrained corridor containing several sealed sources and the integrated spectrometers collected spectra at various points along the path of travel. Both spectrometers successfully collected spectra with identifiable, isotope-specific peaks. To evaluate the effect of contamination buildup on the collected spectra, the robot with the integrated scintillation detector was tested using a liquid radioactive source. The resulting spectra showed little detection interference from the liquid contamination; however, a short-lived isotope was used which may not be representative of longer-lived contamination.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Positioning Accuracy in a Concurrent Robot-CNC Hybrid Manufacturing System

Abstract Additive manufacturing (AM) has gained notoriety for offering advantages over traditional manufacturing methods, such as increased design complexity and flexibility. However, it has not found widespread use beyond rapid prototyping. One hindrance to the acceptance of AM processes in industry is the time and cost of fabrication per component. While metal AM by itself can be inexpensive, extra manufacturing steps in the form of subtractive manufacturing (SM) may need to be performed to reach final part tolerances, leading to hybrid additive-subtractive manufacturing (HASM) of a part, which increases time and cost. A potential area to reduce cost is through increasing the efficiency of the HASM process by conducting additive and subtractive manufacturing simultaneously. Usually, HASM is performed in a process where AM is completed in one machine or cell and transferred to another machine or cell for SM in a sequential assembly line process. This efficiency decreases part cost, but high aspect ratio parts or parts with internal geometry that require interleaved additive deposition and machining cannot be produced. One unexplored solution to simultaneous HASM that allows for interleaved operations is to operate the deposition head and machining spindle concurrently within the same machine envelope, known as concurrent HASM (CHASM). In this type of process, both AM and SM occur simultaneously on a batch of small parts or a single large part, maintaining a high efficiency without sacrificing the full range of complex geometries that AM allows for. A potential approach to the single-machine method could be to combine a robot and mill within the same envelope. A challenge to this approach, however, is control of both systems. Most machine controllers have limited external communication or, if a robot has been integrated, only offer movement of either the robot or mill at any given time. As a result, systems must pause either the AM or SM process to switch between them rather than working simultaneously. The present work investigates the positional accuracy of such a CHASM system comprised of a robotic arm and a 3-axis mill. Open-loop tests with limited communication between machines are performed on the system to verify positional error during concurrent robot-mill movements. Under certain conditions, it is demonstrated that position error can stay within 2 mm for the duration of a single layer; however, these tests show that, generally, the open-loop positioning performance of the system is inadequate for CHASM without part-specific hand-tuning of parameters. Based on these results, a set of requirements for successful robot-CNC CHASM is proposed for future integrations.

Goodwin, Jesse↗
DOE OSTI2026

An adaptive model-free robotic force control strategy for hydrodynamic real-time hybrid simulation of floating offshore wind turbines

Real-time hybrid simulation (RTHS) - a cyber-physical testing approach - promises to enhance the simulation fidelity of the model-scale experiments used to prototype floating offshore wind turbines (FOWTs). In hydrodynamic RTHS (hydro-RTHS), actuators emulate aerodynamic forces on model-scale FOWT specimens subjected to physical waves in a hydrodynamic laboratory. Robotic arms are promising candidates for actuation in hydro-RTHS due to their compact multi-degree-of-freedom (DOF) capabilities. Unlike classical RTHS for seismic applications, which typically relies on displacement control, hydro-RTHS requires 6-DOF force control on newly designed floating prototypes in a model-scale setting, which presents significant challenges, including modeling uncertainties, directional asymmetry, configuration drift, bandwidth limitations, and time-varying delays. To mitigate these constraints without extensive pre-test calibration, this study proposes an adaptive model-free robotic force control strategy that combines task-space explicit force control with a secondary joint-space pose-keeping task. The Adaptive Feedforward Compensator (AFC) is integrated into the force control loop to compensate for time-varying delay. Experimental testing was conducted using a Franka Emika Panda robotic arm with a 1:50 scale FOWT specimen under operational wind and wave conditions. Results demonstrate stable and consistent 6-DOF force tracking. Effective delay compensation was observed, with low-frequency delay reductions ranging from 71.4% to 91.8% and improvements in low-frequency surge force tracking of 25.0% to 52.1%. This study enhances robotic actuation performance in hydro-RTHS and introduces a force control strategy that supports reliable robotic operation in uncertain floating environments. Future work will explore disturbance-observer mechanisms to further enhance wave rejection capabilities under extreme wind and wave conditions.

17 WIND ENERGY↗
DOE OSTI2025

Field-Driven Out-of-Equilibrium Collective Patterns for Swarm Micro-Robotics

Soft robotics has been rapidly advancing, offering significant improvements over traditional rigid robotic systems through the use of compliant materials that enhance adaptability and interaction with the environment. However, current approaches face critical challenges, including the reliance on complex “top-down” fabrication techniques and the difficulty of wireless powering and control at the microscale. Swarm robotics introduces a paradigm shift, leveraging collective dynamics to achieve cooperative and adaptable behaviors among multiple robotic units. Inspired by nature, this “bottom-up” approach enables swarm robots to execute task-specific reconfigurations, enhancing flexibility and robustness. Field-driven active colloids emerge as a promising platform for swarm microrobotics, capable of self-propulsion and self-organization into dynamic collective patterns under external field excitation and manipulation. These systems mimic biologically inspired swarm behaviors, such as flocking and vortex formation, providing a versatile foundation for designing innovative swarm microrobots. Here, this review discusses the principles of electric and magnetic field-driven collective self-organization, focusing on the particle dynamics, the emergence of collective swarm patterns, and illustrative examples of functional swarm microrobots. It concludes with future perspectives on harnessing these systems for adaptive, scalable, and multifunctional microrobotic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Autonomous elemental characterization enabled by a low cost robotic platform built upon a generalized software architecture

Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.

Cao, Xuan [Lawrence Berkeley National Laboratory (↗
DOE OSTI2025

A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high throughputs. We demonstrate the performance of this approach by autonomously driving a 4-DOF robotic probe for 24 hours to characterize semiconductor photoconductivity at 3025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs of more than 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing defects. With this self-supervised neural network–driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

Science & Technology - Other Topics↗