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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-D-BRF-06: A Brachytherapy Simulator with Realistic Haptic Force Feedback and Real-Time Ultrasounds Image Simulation for Training and Teaching

Purpose: Surgical procedures require dexterity, expertise and repetition to reach optimal patient outcomes. However, efficient training opportunities are usually limited. This work presents a simulator system with realistic haptic force-feedback and full, real-time ultrasounds image simulation. Methods: The simulator is composed of a custom-made Linear-DELTA force-feedback robotic platform. The needle tip is mounted on a force gauge at the end effector of the robot, which responds to needle insertion by providing reaction forces. 3D geometry of the tissue is using a tetrahedral finite element mesh (FEM) mimicking tissue properties. As the needle is inserted/retracted, tissue deformation is computed using a mass-tensor nonlinear visco-elastic FEM. The real-time deformation is fed to the L-DELTA to take into account the force imparted to the needle, providing feedback to the end-user when crossing tissue boundaries or needle bending. Real-time 2D US image is also generated synchronously showing anatomy, needle insertion and tissue deformation. The simulator is running on an Intel I7 6- core CPU at 3.26 MHz. 3D tissue rendering and ultrasound display are performed on a Windows 7 computer; the FEM computation and L-DELTA control are executed on a similar PC using the Neutrino real-time OS. Both machines communicate through an Ethernet link. Results: The system runs at 500 Hz for a 8333-tetrahedron tissue mesh and a 100-node angular spring needle model. This frame rate ensures a relatively smooth displacement of the needle when pushed or retracted (±20 N in all directions at speeds of up to 2 m/s). Unlike commercially-available haptic platforms, the oblong workspace of the L-DELTA robot complies with that required for brachytherapy needle displacements of 0.1m by 0.1m by 0.25m. Conclusion: We have demonstrated a real-life, realistic brachytherapy simulator developed for prostate implants (LDR/HDR). The platform could be adapted to other sites or training for other types of needle-based procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

MO-DE-210-03: Ultrasound imaging is an attractive method for image guided radiation treatment (IGRT), by itself or to complement other imaging modalities

Ultrasound imaging is an attractive method for image guided radiation treatment (IGRT), by itself or to complement other imaging modalities. It is inexpensive, portable and provides good soft tissue contrast. For challenging soft tissue targets such as pancreatic cancer, ultrasound imaging can be used in combination with pre-treatment MRI and/or CT to transfer important anatomical features for target localization at time of treatment. The non-invasive and non-ionizing nature of ultrasound imaging is particularly powerful for intra-fraction localization and monitoring. Recognizing these advantages, efforts are being made to incorporate novel robotic approaches to position and manipulate the ultrasound probe during irradiation. These recent enabling developments hold potential to bring ultrasound imaging to a new level of IGRT applications. However, many challenges, not limited to image registration, robotic deployment, probe interference and image acquisition rate, need to be addressed to realize the full potential of IGRT with ultrasound imaging. Learning Objectives: Understand the benefits and limitations in using ultrasound to augment MRI and/or CT for motion monitoring during radiation therapy delivery. Understanding passive and active robotic approaches to implement ultrasound imaging for intra-fraction monitoring. Understand issues of probe interference with radiotherapy treatment. Understand the critical clinical workflow for effective and reproducible IGRT using ultrasound guidance. The work of X.L. is supported in part by Elekta; J.W. and K.D. is supported in part by a NIH grant R01 CA161613 and by Elekta; D.H. is support in part by a NIH grant R41 CA174089.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Reference respiratory waveforms by minimum jerk model analysis

Purpose: CyberKnife{sup ®} robotic surgery system has the ability to deliver radiation to a tumor subject to respiratory movements using Synchrony{sup ®} mode with less than 2 mm tracking accuracy. However, rapid and rough motion tracking causes mechanical tracking errors and puts mechanical stress on the robotic joint, leading to unexpected radiation delivery errors. During clinical treatment, patient respiratory motions are much more complicated, suggesting the need for patient-specific modeling of respiratory motion. The purpose of this study was to propose a novel method that provides a reference respiratory wave to enable smooth tracking for each patient. Methods: The minimum jerk model, which mathematically derives smoothness by means of jerk, or the third derivative of position and the derivative of acceleration with respect to time that is proportional to the time rate of force changed was introduced to model a patient-specific respiratory motion wave to provide smooth motion tracking using CyberKnife{sup ®}. To verify that patient-specific minimum jerk respiratory waves were being tracked smoothly by Synchrony{sup ®} mode, a tracking laser projection from CyberKnife{sup ®} was optically analyzed every 0.1 s using a webcam and a calibrated grid on a motion phantom whose motion was in accordance with three pattern waves (cosine, typical free-breathing, and minimum jerk theoretical wave models) for the clinically relevant superior–inferior directions from six volunteers assessed on the same node of the same isocentric plan. Results: Tracking discrepancy from the center of the grid to the beam projection was evaluated. The minimum jerk theoretical wave reduced the maximum-peak amplitude of radial tracking discrepancy compared with that of the waveforms modeled by cosine and typical free-breathing model by 22% and 35%, respectively, and provided smooth tracking for radial direction. Motion tracking constancy as indicated by radial tracking discrepancy affected by respiratory phase was improved in the minimum jerk theoretical model by 7.0% and 13% compared with that of the waveforms modeled by cosine and free-breathing model, respectively. Conclusions: The minimum jerk theoretical respiratory wave can achieve smooth tracking by CyberKnife{sup ®} and may provide patient-specific respiratory modeling, which may be useful for respiratory training and coaching, as well as quality assurance of the mechanical CyberKnife{sup ®} robotic trajectory.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Spatial and rotational quality assurance of 6DOF patient tracking systems

Purpose: External tracking systems used for patient positioning and motion monitoring during radiotherapy are now capable of detecting both translations and rotations. In this work, the authors develop a novel technique to evaluate the 6 degree of freedom 6(DOF) (translations and rotations) performance of external motion tracking systems. The authors apply this methodology to an infrared marker tracking system and two 3D optical surface mapping systems in a common tumor 6DOF workspace. Methods: An in-house designed and built 6DOF parallel kinematics robotic motion phantom was used to perform motions with sub-millimeter and subdegree accuracy in a 6DOF workspace. An infrared marker tracking system was first used to validate a calibration algorithm which associates the motion phantom coordinate frame to the camera frame. The 6DOF positions of the mobile robotic system in this space were then tracked and recorded independently by an optical surface tracking system after a cranial phantom was rigidly fixed to the moveable platform of the robotic stage. The calibration methodology was first employed, followed by a comprehensive 6DOF trajectory evaluation, which spanned a full range of positions and orientations in a 20 × 20 × 16 mm and 5° × 5° × 5° workspace. The intended input motions were compared to the calibrated 6DOF measured points. Results: The technique found the accuracy of the infrared (IR) marker tracking system to have maximal root-mean square error (RMSE) values of 0.18, 0.25, 0.07 mm, 0.05°, 0.05°, and 0.09° in left–right (LR), superior–inferior (SI), anterior–posterior (AP), pitch, roll, and yaw, respectively, comparing the intended 6DOF position and the measured position by the IR camera. Similarly, the 6DOF RSME discrepancy for the HD optical surface tracker yielded maximal values of 0.46, 0.60, 0.54 mm, 0.06°, 0.11°, and 0.08° in LR, SI, AP, pitch, roll, and yaw, respectively, over the same 6DOF evaluative workspace. An earlier generation 3D optical surface tracking unit was observed to have worse tracking capabilities than both the IR camera unit and the newer 3D surface tracking system with maximal RMSE of 0.69, 0.74, 0.47 mm, 0.28°, 0.19°, and 0.18°, in LR, SI, AP, pitch, roll, and yaw, respectively, in the same 6DOF evaluation space. Conclusions: The proposed technique was found to be effective at evaluating the performance of 6DOF patient tracking systems. All observed optical tracking systems were found to exhibit tracking capabilities at the sub-millimeter and subdegree level within a 6DOF workspace.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-575: Verification of a Monte-Carlo Small Field SRS/SBRT Dose Calculation System

Purpose: To develop a methodology for validation of a Monte-Carlo dose calculation model for robotic small field SRS/SBRT deliveries. Methods: In a robotic treatment planning system, a Monte-Carlo model was iteratively optimized to match with beam data. A two-part analysis was developed to verify this model. 1) The Monte-Carlo model was validated in a simulated water phantom versus a Ray-Tracing calculation on a single beam collimator-by-collimator calculation. 2) The Monte-Carlo model was validated to be accurate in the most challenging situation, lung, by acquiring in-phantom measurements. A plan was created and delivered in a CIRS lung phantom with film insert. Separately, plans were delivered in an in-house created lung phantom with a PinPoint chamber insert within a lung simulating material. For medium to large collimator sizes, a single beam was delivered to the phantom. For small size collimators (10, 12.5, and 15mm), a robotically delivered plan was created to generate a uniform dose field of irradiation over a 2×2cm{sup 2} area. Results: Dose differences in simulated water between Ray-Tracing and Monte-Carlo were all within 1% at dmax and deeper. Maximum dose differences occurred prior to dmax but were all within 3%. Film measurements in a lung phantom show high correspondence of over 95% gamma at the 2%/2mm level for Monte-Carlo. Ion chamber measurements for collimator sizes of 12.5mm and above were within 3% of Monte-Carlo calculated values. Uniform irradiation involving the 10mm collimator resulted in a dose difference of ∼8% for both Monte-Carlo and Ray-Tracing indicating that there may be limitations with the dose calculation. Conclusion: We have developed a methodology to validate a Monte-Carlo model by verifying that it matches in water and, separately, that it corresponds well in lung simulating materials. The Monte-Carlo model and algorithm tested may have more limited accuracy for 10mm fields and smaller.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

ROS-Ethereum: A Convenient Tool to Bridge ROS and Blockchain (Ethereum)

Robot Operating System (ROS) has received widespread utilization with the development of robotics, self-driving, etc., recently. Meanwhile, the other technology blockchain is frequently applied to various fields with its trustworthy characteristics and immutability in data storage. However, ROS has no ability to interact with the blockchain, which hinders research in related fields. Therefore, we wonder if we can develop a convenient tool to bridge ROS and blockchain. Inspired by this, we propose ROS-Ethereum. It bridges ROS and Ethereum, a widely used blockchain platform. ROS-Ethereum is based on the User Datagram Protocol (UDP) communication mechanism and the SM algorithm family along with Ethereum technology. Simply put, ROS-Ethereum allows users to invoke the contract when interacting with the blockchain, which makes this process easier and safer. We conduct experiments in real robots to verify the effectiveness of ROS-Ethereum and evaluate it from the following metrics: (1) the encryption efficiency and stability of the algorithm and (2) ROS-Ethereum transaction response time and packet loss rate.

Zhang, Shenhui↗
DOE OSTI2016

Contact Control, Version 1

The contact control code is a generalized force control scheme meant to interface with a robotic arm being controlled using the Robot Operating System (ROS). The code allows the user to specify a control scheme for each control dimension in a way that many different control task controllers could be built from the same generalized controller. The input to the code includes maximum velocity, maximum force, maximum displacement, and a control law assigned to each direction and the output is a 6 degree of freedom velocity command that is sent to the robot controller.

von Sternberg, Alex↗
DOE OSTI2024

spot_bt_ros v.0.1.0

SAND2024-08478O The spot_bt_ros tool allows users to easily construct complex autonomous behaviors for Boston Dynamic's Spot robot without having to overly rely on the Spot software development kit (SDK). This package specifically allows a user to run the Robot Operating System 2 alongside the SDK. Using a form-of-state machine called a "behavior tree,” the software develops different "leaves" or actions that the robot undergoes. Attaching multiple actions in sequence creates a "branch" and several of these make up the "tree." Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kakish, Zahi↗
DOE OSTI2013

Autonomous Intelligent Assembly Systems LDRD 105746 (Final Report)

This report documents a three-year to develop technology that enables mobile robots to perform autonomous assembly tasks in unstructured outdoor environments. This is a multi-tier problem that requires an integration of a large number of different software technologies including: command and control, estimation and localization, distributed communications, object recognition, pose estimation, real-time scanning, and scene interpretation. Although ultimately unsuccessful in achieving a target brick stacking task autonomously, numerous important component technologies were nevertheless developed. Such technologies include: a patent-pending polygon snake algorithm for robust feature tracking, a color grid algorithm for uniquely identification and calibration, a command and control framework for abstracting robot commands, a scanning capability that utilizes a compact robot portable scanner, and more. This report describes this project and these developed technologies.

Anderson, Robert J.↗
DOE OSTI2018

IMU integration into Sensor suite for Inspection of H-Canyon

The Purpose of this internship was to Integrate the sensor suite which will be mounted onto a robotic inspection tool used to survey the H-Canyon Air Exhaust (CAEX) tunnel the Savannah River Site. This sensor suite incorporates 3D-mapping of its surroundings, along with a 360 degree picture of its surroundings stitched in with the li-dar readings. Incorporation of the IMU would enable us to track the position of the robot inside the cave, such that the robot handlers can know where inside the exhaust tunnel the scans have been taken. The Inertial Measurement Unit (IMU) used had ROS compatibility, allowing for high leavel programming and ease of software modification. The sensor transmitted Data in the form of Acceleration. A good portion of the internship concerned the investigation of discrete integration techniques, such that we can acquire precise position information quickly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Automating Testing of DUNE Electronics via a Finite State Machine

The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six total institutions performing testing. The outcomes of this internship contribute to Fermilab s mission to advance high-energy physics and support the DOE s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.

Kang, Caleb [William Rainey Harper Coll.]↗
DOE OSTI2014

Automatic monitoring of vibration welding equipment

A vibration welding system includes vibration welding equipment having a welding horn and anvil, a host device, a check station, and a robot. The robot moves the horn and anvil via an arm to the check station. Sensors, e.g., temperature sensors, are positioned with respect to the welding equipment. Additional sensors are positioned with respect to the check station, including a pressure-sensitive array. The host device, which monitors a condition of the welding equipment, measures signals via the sensors positioned with respect to the welding equipment when the horn is actively forming a weld. The robot moves the horn and anvil to the check station, activates the check station sensors at the check station, and determines a condition of the welding equipment by processing the received signals. Acoustic, force, temperature, displacement, amplitude, and/or attitude/gyroscopic sensors may be used.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Contact Control, Version 1

The contact control code is a generalized force control scheme meant to interface with a robotic arm being controlled using the Robot Operating System (ROS). The code allows the user to specify a control scheme for each control dimension in a way that many different control task controllers could be built from the same generalized controller. The input to the code includes maximum velocity, maximum force, maximum displacement, and a control law assigned to each direction and the output is a 6 degree of freedom velocity command that is sent to the robot controller.

von Sternberg, Alex↗
DOE OSTI2017

Automatic monitoring of the alignment and wear of vibration welding equipment

A vibration welding system includes vibration welding equipment having a welding horn and anvil, a host machine, a check station, and a welding robot. At least one displacement sensor is positioned with respect to one of the welding equipment and the check station. The robot moves the horn and anvil via an arm to the check station, when a threshold condition is met, i.e., a predetermined amount of time has elapsed or a predetermined number of welds have been completed. The robot moves the horn and anvil to the check station, activates the at least one displacement sensor, at the check station, and determines a status condition of the welding equipment by processing the received signals. The status condition may be one of the alignment of the vibration welding equipment and the wear or degradation of the vibration welding equipment.

47 OTHER INSTRUMENTATION↗