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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 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↗
NASA NTRSDate not supplied

Assessment of Sensor Data Accuracy within Gazebo/ROS for High-Precision Autonomous In-Space Robotic Operations

Modeling high-precision in-space servicing, assembly, and manufacturing operations in a simulated environment is a critical step in the development of robotic systems that will be used to autonomously assemble large-scale structures in space. Limited facility size and high costs for manufacturing prototypes make it challenging to conduct full-scale operational testing under appropriate environmental conditions; therefore, testing in a modular, high-fidelity simulation environment is necessary for verification and validation of technology and architecture designs prior to launch. Several modeling and simulation environments exist both within NASA and industry that can be used to test robotic system design and operations, including the widely used commercial tool Gazebo integrated with Robotic Operating System software. Because the performance of autonomous robotic systems relies heavily on the quality of sensor input data, this paper focuses on assessing the accuracy of pose data from an optical sensor model in the Gazebo environment against the behavior of real hardware. The results of the tests will help developers using Gazebo for large-scale, high-precision simulation to account for modeling inaccuracies within their robotic control system algorithms.

simulation↗
NASA NTRS2014

Swarming Robot Design, Construction and Software Implementation

In this paper is presented an overview of the hardware design, construction overview, software design and software implementation for a small, low-cost robot to be used for swarming robot development. In addition to the work done on the robot, a full simulation of the robotic system was developed using Robot Operating System (ROS) and its associated simulation. The eventual use of the robots will be exploration of evolving behaviors via genetic algorithms and builds on the work done at the University of New Mexico Biological Computation Lab.

construction overview↗
NASA NTRS2013

NASA Tech Briefs, January 2013

Topics include: Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector; Surface-Enhanced Raman Scattering Using Silica Whispering-Gallery Mode Resonators; 3D Hail Size Distribution Interpolation/Extrapolation Algorithm; Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels; Artificial Intelligence Software for Assessing Postural Stability; Transformers: Shape-Changing Space Systems Built with Robotic Textiles; Fibrillar Adhesive for Climbing Robots; Using Pre-Melted Phase Change Material to Keep Payloads in Space Warm for Hours without Power; Development of a Centrifugal Technique for the Microbial Bioburden Analysis of Freon (CFC-11); Microwave Sinterator Freeform Additive Construction System (MS-FACS); DSP/FPGA Design for a High-Speed Programmable S-Band Space Transceiver; On-Chip Power-Combining for High-Power Schottky Diode-Based Frequency Multipliers; FPGA Vision Data Architecture; Memory Circuit Fault Simulator; Ultra-Compact Transputer-Based Controller for High-Level, Multi-Axis Coordination; Regolith Advanced Surface Systems Operations Robot Excavator; Magnetically Actuated Seal; Hybrid Electrostatic/Flextensional Mirror for Lightweight, Large-Aperture, and Cryogenic Space Telescopes; System for Contributing and Discovering Derived Mission and Science Data; Remote Viewer for Maritime Robotics Software; Stackfile Database; Reachability Maps for In Situ Operations; JPL Space Telecommunications Radio System Operating Environment; RFI-SIM: RFI Simulation Package; ION Configuration Editor; Dtest Testing Software; IMPaCT - Integration of Missions, Programs, and Core Technologies; Integrated Systems Health Management (ISHM) Toolkit; Wind-Driven Wireless Networked System of Mobile Sensors for Mars Exploration; In Situ Solid Particle Generator; Analysis of the Effects of Streamwise Lift Distribution on Sonic Boom Signature; Rad-Tolerant, Thermally Stable, High-Speed Fiber-Optic Network for Harsh Environments; Towed Subsurface Optical Communications Buoy; High-Collection-Efficiency Fluorescence Detection Cell; Ultra-Compact, Superconducting Spectrometer-on-a-Chip at Submillimeter Wavelengths; UV Resonant Raman Spectrometer with Multi-Line Laser Excitation; Medicine Delivery Device with Integrated Sterilization and Detection; Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME; Airborne Tomographic Swath Ice Sounding Processing System; flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter; Estimating Torque Imparted on Spacecraft Using Telemetry; PowderSim: Lagrangian Discrete and Mesh-Free Continuum Simulation Code for Cohesive Soils; Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences; Metric Learning to Enhance Hyperspectral Image Segmentation; Basic Operational Robotics Instructional System; Sheet Membrane Spacesuit Water Membrane Evaporator; Advanced Materials and Manufacturing for Low-Cost, High-Performance Liquid Rocket Combustion Chambers; Motor Qualification for Long-Duration Mars Missions.

Source record↗
NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a mobile robotic bucket-drum excavator platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for estimating the quantity of regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete In-Situ Resource Utilization (ISRU) missions. To teleoperate or run autonomously, it is crucial for the amount of regolith mass ingested to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of the total mass in the drums, this type of high-level planning is not possible. Three distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR, none of which require modification to the hardware. All take in system states, such as arm/drum motor positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums. The developed models run in real-time, outputting predictions for the front drum mass, rear drum mass, timestamp of the last prediction, and total drum mass (sum of front and rear) in RASSOR’s drums. Models deployed to the hardware have low error (<7.5% mean error over the mass range, and <2.6% mean error when drums are more than half full) when making predictions in real-time. Our modeling approach can be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results of this work are promising and show that models can be developed to accurately estimate excavated regolith mass.

Bucket Drum Excavators↗
NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a mobile robotic bucket-drum excavator platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for estimating the quantity of regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete In-Situ Resource Utilization (ISRU) missions. To teleoperate or run autonomously, it is crucial for the amount of regolith mass ingested to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of the total mass in the drums, this type of high-level planning is not possible. Three distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR, none of which require modification to the hardware. All take in system states, such as arm/drum motor positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums. The developed models run in real-time, outputting predictions for the front drum mass, rear drum mass, timestamp of the last prediction, and total drum mass (sum of front and rear) in RASSOR’s drums. Models deployed to the hardware have low error (<7.5% mean error over the mass range, and <2.6% mean error when drums are more than half full) when making predictions in real-time. Our modeling approach can be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results of this work are promising and show that models can be developed to accurately estimate excavated regolith mass.

bucket drum excavators↗
NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of how much mass is in the drums, this type of high-level planning is not possible. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All take in system states, such as arm/drum positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums.1) A neural network model that takes a vector of normalized system states; 2) A model that uses the integrated power consumption of an arm-raise (normalized by velocity); 3) A model that uses average drum current over a variable length interval of the drum disengaged from the surface; and 4) A real-time estimation model that aggregates excavation drum current. The developed models run in real time, outputting predictions for the front and rear drums, timestamp of the last prediction, and total mass in RASSOR’s drums. Further testing is required to validate the arm-raise model (2), though initial tests indicate reasonable performance (<10% mean error) on the hardware. The linear fit of average drum-current model (3) had a front value of r^2=0.99 and a rear value of r^2=0.98 on the validation dataset. This model currently has the best performance on unseen data. The real time model (4) is still in development, though initial results on a small subset of the training data show that it has high accuracy in predicting the increase in mass during excavation. Though work remains to be done with deploying a high-fidelity model to the physical system that makes predictions with error below the desired threshold, the modular architecture for model development allows quick adjustment of parameters to increase model fidelity. This architecture can also be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results are promising as it has been shown that models can be developed that accurately estimate excavated regolith mass.

rassor↗
NASA NTRS2008

Video Guidance Sensor for Surface Mobility Operations

Robotic systems and surface mobility will play an increased role in future exploration missions. Unlike the LRV during Apollo era which was an astronaut piloted vehicle future systems will include teleoperated and semi-autonomous operations. The tasks given to these vehicles will run the range from infrastructure maintenance, ISRU, and construction to name a few. A common task that may be performed would be the retrieval and deployment of trailer mounted equipment. Operational scenarios may require these operations to be performed remotely via a teleoperated mode,or semi-autonomously. This presentation describes the on-going project to adapt the Automated Rendezvous and Capture (AR&C) sensor developed at the Marshall Space Flight Center for use in an automated trailer pick-up and deployment operation. The sensor which has been successfully demonstrated on-orbit has been mounted on an iRobot/John Deere RGATOR autonomous vehicle for this demonstration which will be completed in the March 2008 time-frame.

Fernandez, Kenneth R.↗
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)↗
NASA NTRS2021

Mass Inferencing Model Creation And Deployment To Lunar Excavation Robot, RASSOR

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. This research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. Radio wave propagation time to the Moon and back is ~2.56 seconds. Though teleoperation is possible with this delay, autonomous capability that enables RASSOR to plan and execute excavation missions intelligently and efficiently is preferred. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation (e.g. knowledge of whether drums are full informs the task of highest priority, whether it be continuing to dig, or returning to a processing plant to offload regolith). A configurable data reduction and analysis pipeline was created to allow for straightforward incorporation of new data, such as that from lunar excavation, to improve model performance in new environments. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All four models take in system states and output a mass prediction for each set of the robot’s bucket drums. Initial results from deployment to RASSOR and testing in a simulated lunar environment show that the models have <10% mean error during robot operation. Future work includes refinement of a model that estimates regolith mass in real-time during excavation as well as further testing of the developed models on the hardware.

ROS↗
NASA NTRSDate not supplied

Space ROS TOFU: Flying Space ROS with Containerized Hybrid Trust

Space ROS is a distribution of Robot Operating System 2 (ROS2) targeting the specific requirements of flight software and spaceborne robotics while maintaining the flexibility that has made ROS indispensible for robotics research and industrial system integration. With Space ROS, a project can leverage the existing ROS2 ecosystem to reduce redundant development while tackling increasingly complex demands for on-device intelligence; however, there is no substitute for flight heritage to combat the risk-aversion common to spaceflight projects, and Space ROS has yet to fly. To break the collective-action standoff and gain valuable flight experience, the Distributed Spacecraft Autonomy (DSA) team at NASA Ames Research Center developed Opportunistic Software Experiments for Spacecraft Autonomy Testbeds (OSE-SAT) architecture, utilizing containerization to execute lower-trust software under traditionally verified heritage flight software for demonstration on-orbit. OSE-SAT leverages a hybrid-trust model where flexible, complex components like Space ROS can run without risk to the host spacecraft while providing validated feedback to a highly scrutinized, trusted core. We leverage this testbed to demonstrate Space ROS in flight, building heritage, and experience for projects with "Trust On First Use" (TOFU) requirements for flight heritage. We present a Space ROS component for OSE-SAT, lessons learned integrating Space ROS into a flight software stack, and the results of the first known use of Space ROS in orbit.

small satellites↗
NASA NTRS1997

Robotic Materials Handling in Space: Mechanical Design of the Robot Operated Materials Processing System HitchHiker Experiment

The Goddard Space Flight Center has developed the Robot Operated Materials Processing System (ROMPS) that flew aboard STS-64 in September, 1994. The ROMPS robot transported pallets containing wafers of different materials from their storage racks to a furnace for thermal processing. A system of tapered guides and compliant springs was designed to deal with the potential misalignments. The robot and all the sample pallets were locked down for launch and landing. The design of the passive lockdown system, and the interplay between it and the alignment system are presented.

Voellmer, George↗
NASA NTRS2017

An Integrated System for Autonomous Search and Track with a Small Unmanned Aerial Vehicle

This article presents a framework for searching, detecting, and tracking an object of interest with a small unmanned aerial vehicle (sUAV). The vehicle is given an area to search for an object of interest. Once the object is detected, the sUAV follows the target while maintaining a fixed distance and centered on its image plane. This paper describes an architecture for integrating autonomy capabilities for search and track for sUAVs. The system has been implemented in the Robot Operating System (ROS) framework using the Parrot ARDrone platform. In this paper we present our technical approach, the system architecture and demonstrate the principles of the approach in a simulation environment and indoor flight tests.

Unmanned Aerial Vehicle↗
NASA NTRS1987

Development of a semi-autonomous service robot with telerobotic capabilities

The importance to the United States of semi-autonomous systems for application to a large number of manufacturing and service processes is very clear. Two principal reasons emerge as the primary driving forces for development of such systems: enhanced national productivity and operation in environments whch are hazardous to humans. Completely autonomous systems may not currently be economically feasible. However, autonomous systems that operate in a limited operation domain or that are supervised by humans are within the technology capability of this decade and will likely provide reasonable return on investment. The two research and development efforts of autonomy and telerobotics are distinctly different, yet interconnected. The first addresses the communication of an intelligent electronic system with a robot while the second requires human communication and ergonomic consideration. Discussed here are work in robotic control, human/robot team implementation, expert system robot operation, and sensor development by the American Welding Institute, MTS Systems Corporation, and the Colorado School of Mines--Center for Welding Research.

Jones, J. E.↗
NASA NTRS2018

Astrobee Robot Software: A Modern Software System for Space

Astrobee is a new free-flyer robot designed to operate inside the International Space Station (ISS). Astrobee capabilities include markerless navigation, autonomous docking for recharge, perching on handrails to minimize power and modular payloads. Astrobee will operate without crew support, controlled by teleoperation, plan execution, or on-board third parties software. This paper presents the Astrobee Robot Software, a NASA Open-Source project, powering the Astrobee robot. The Astrobee Robot Software relies on a distributed architecture based on the Robot Operating System (ROS). The software runs on three interconnected smart phone class processors. We present the software approach, infrastructure required, and main software components. The Astrobee Robot Software embrace modern software practices while respecting flight constraints. The paper concludes with the lessons learned, including examples usage of the software. Several research teams are already using the Astrobee Robot Software to develop novel projects that will fly on Astrobee.

Astrobee↗
NASA NTRS2010

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 6 - Development and Demonstration of a Self-Organizing Diagnostic System for Structural Health Monitoring

This report describes a significant advance in the capability of the CSIRO/NASA structural health monitoring Concept Demonstrator (CD). The main thrust of the work has been the development of a mobile robotic agent, and the hardware and software modifications and developments required to enable the demonstrator to operate as a single, self-organizing, multi-agent system. This single-robot system is seen as the forerunner of a system in which larger numbers of small robots perform inspection and repair tasks cooperatively, by self-organization. While the goal of demonstrating self-organized damage diagnosis was not fully achieved in the time available, much of the work required for the final element that enables the robot to point the video camera and transmit an image has been completed. A demonstration video of the CD and robotic systems operating will be made and forwarded to NASA.

Batten, Adam↗
NASA NTRSDate not supplied

Lunar Excavator Mission Operations using Dynamic Movement Primitives

To support sustainable infrastructure on the Moon, NASA must leverage robots to extract lunar resources for in-situ processing and construction. As part of this effort, NASA is launching the in-situ resource utilization (ISRU) Pilot Excavator later this decade to validate a robotic regolith excavator based on the Regolith Advanced Surface Systems Operations Robot (RASSOR). RASSOR is designed to extract and transport regolith to meet the needs of ISRU architectures. During its mission, Pilot Excavator will be tasked with driving in test patterns to demonstrate the operational concept. One of these tests is a circular trajectory around the lander while avoiding miscellaneous surface hazards such as lunar rocks. To this end, we utilize dynamic movement primitives to represent navigation sequences as primitive trajectories. Here, we introduce a novel obstacle avoidance parameter, which is configured to avoid rocks throughout testing exercises. We demonstrate the effectiveness our method in a newly developed simulation tool called the Simulated Excavation Environment for Lunar Operations (SEELO) using models based on the NASA RASSOR 2.0 excavator. After making key changes to the obstacle avoidance formulation, our results show that the robot is able to safety and robustly navigate the lunar surface with densely populated rock obstacles while retaining the desired circle pattern behavior.

Space Robotics and Automation↗