UT Shield

Nuclear

, Filed Under: AugRe, Nuclear, Research

Digital Twin Augmented Reality – Radiation in AR

This project extends the Alpha Radiation Survey demo by enabling 3D visualization of radiation.

Using AugRE, we represent alpha, beta, and gamma particles through dynamic particle effects. The system incorporates spatial mapping for accurate placement in the environment and mesh generation that reacts dynamically to variations in radiation intensity.

Currently, the system uses simulated data, with ongoing work to integrate real sensor readings from devices such as Compton cameras and alpha flashlight sensors, enabling accurate, real-time 3D visualization of radiation levels.


, Filed Under: AugRe, Autonomous Platforms, Defense, Nuclear, Research

AugRE

AugRE (Augmented Robot Environment) is a scalable mixed reality-based human-robot teaming system that enables users to easily command, control, and supervise large multi-agent unmanned ground and aerial vehicles. The current system enables multiple users to collaborate with up to 50 robotic agents simultaneously in unknown indoor and outdoor environments. The system features real-time localization, an efficient by directional communication layer, and spatially aligned holographic representations that enable users to easily visualize and interact with real-time robot sensor data. Check out the AugRE project page to learn more: https://utnuclearroboticspublic.github.io/Augmented-Robot-Environment/

, Filed Under: Manipulation, Nuclear, Research

Affordance Templates for Complex DOE Robotics Tasks

A screw-based affordance template framework enables adaptive robotic execution of complex contact tasks (CCTs) in DOE decontamination and decommissioning (D&D) environments.

By modeling tasks with screw and wrench primitives, the approach supports real-time reconfiguration, force-adaptive control, and scalable task generalization. A Pre-Planner module refines motion plans by integrating collision constraints and dynamically adjusting trajectories based on real-time Force/Torque (F/T) feedback.

The framework has been validated at the NRG lab and the Hanford site, demonstrating scalability, robustness, and generalization across robotic platforms for challenging nuclear maintenance tasks.

, Filed Under: AugRe, Nuclear, Research

Autonomous Alpha Radiation Sensing and Reporting

To facilitate the job of a radiation cleanup crew in the event of radiation contamination, we propose the use of a Hololens2 augmented reality (AR) headset to provide improved situational awareness. This AR interface shows an operator salient information such as the pinpoint location of the contaminated material, as well as which parts of the workspace have already been surveyed and are therefore safe to traverse. Doing so not only increases worker safety, but also increases their efficiency, allowing facility operation to resume with minimal downtime.

  • Video: https://www.youtube.com/watch?v=4HVGrSGlOro
  • Navarro, A., Van Sice, C., Montgomery, C., La Joie, K., and Pryor, M., “Autonomous Radiation Survey with Augmented Reality for Hazardous Environments,” Task and Motion Planning Workshop at the IEEE International Conference on Intelligent Robotics and Systems, Detroit, MI, October 2023. (link)
  • F. Regal et al., “AugRE: Augmented Robot Environment to Facilitate Human-Robot Teaming and Communication,” 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Napoli, Italy, 2022, pp. 800-805, doi: 10.1109/RO-MAN53752.2022.9900721.

, Filed Under: Manipulation, Nuclear, Research, Space

Closed-Chain Affordance Planner

Robots operating in unpredictable environments require versatile, hardware-agnostic frameworks capable of adapting to various tasks. While a recent screw-based affordance approach shows promise, it faces challenges in avoiding undesirable configurations, singularity navigation, and task success prediction. To address these limitations, we propose the Closed-Chain Affordance (CCA) planner – a novel framework that incorporates explicit gripper orientation control and generates complete joint trajectories in real time for screw-based task affordance execution. Our method models the affordance and manipulator as a closed-chain mechanism, introducing an innovative approach to solving closed-chain inverse kinematics. It encapsulates task constraints and simplifies task definitions, while remaining hardware and robot agnostic, robust to errors, and invariant to the initial grasp. We validate our framework with simulations on a UR5 robot and real-world implementation on a Boston Dynamics Spot robot. Our experiments demonstrate rapid joint trajectory generation (0.0077–0.098 s) for various tasks, including a 420° valve turn with gripper orientation control. Comparison with state-of-the-art methods shows a 4x improvement in planning time, reduced joint movement, and achievement of greater task goals.

, Filed Under: Autonomous Platforms, Nuclear, Research

Minibots

Multi-Robot Collaborative Mapping w/ Remote Mobile Agents:

This project contributes to a broader initiative to develop a comprehensive hardware and software solution for routine radiation surveying in nuclear facilities. The Minibot system includes a central server computer that serves as the coordination and processing hub, and a team of compact, low-powered, cost-effective robot agents. Each agent features a differential drive platform and a small footprint, enabling navigation through confined and hazardous areas where other robots cannot safely operate, such as under furniture and along narrow corridors.

Coordinated by the server, the agents continuously offload environmental data from onboard 2D and 3D LiDAR sensors. The server then executes collaborative SLAM and 3D mapping algorithms to produce detailed maps of the environment, which other robots use for task planning and execution.

, Filed Under: Archived, Autonomous Platforms, Nuclear, Research

Condition Monitoring of Dry Storage Canisters

Summary: Over 90% of SNF dry cask storage systems in the United States use welded dry storage canisters (DSCs). Typically, these canisters represent the confinement barrier in the dry storage system preventing any release of SNF or radioactive noble gases to the environment. As the dry storage terms of SNF are extended, DSC monitoring issues become more important for safe operation of the dry cask storage systems. The objective of this research program is to develop a technology to enable the next generation of “intelligent spent nuclear fuel (SNF) dry storage canisters (DSCs),” that is, canisters with integrated sensing and processing capabilities to enable real-time state awareness of the DSC. This project was funded by DOE NEUP

Collaborator: Dr. Salvatore Salamone (UT Austin), Orano TN, Idaho National Lab

  • Wall climbing robot youtube playlist
  • Elgavish, E., and Pryor, M., “Development and Validation of a 3d Force and Moment Balancing Simulation Software for Evaluating Wall Climbing Robots on Varied Surfaces,” ASME/DETC, November 2024. (doi link)

, Filed Under: Archived, Autonomous Platforms, Nuclear, Research

Self Exploration and Mapping for Indoor Robotic Applications

Summary: The purpose of this effot is to develop a robotic system that is fully capable of autonomous survey tasks with little to no human interaction. The system uses a Veloydne VLP-32 LIDAR sensor and an Intel RealSense D435 camera to simultaneously map the environment and avoid all obstacles autonomously. Combination of these sensor to detect positive, negative, and overhead obstacles results in 3D object detection, which is then used to create a 2D map using SLAM mapping algorithms.