Engineering topics

Wells, William

Publications and source records attributed to Wells, William.

3 records

DOE OSTI2020

Robotic Assisted Non-Destructive Testing (NDT)

Mission Statement: Concrete wall characterization for structural integrity evaluation of H-Canyon exhaust tunnel using a remote controlled robotic arm with a NDT Concrete Instrument. Challenges: Rough and Curved Surfaces, Remote location. The UR5 is a collaborative robotic arm capable of: Payload: 11 lbs (5 kg), Reach: 33.5 in (850 mm), Footprint: 5.87 in (149 mm) diameter, Weight: 45.4 Ibs (20.6 kg). The force torque sensor reacts to a set value inputted by the user that can be utilized for sensitive products. This allows the UR5 to react to surfaces using the built-in function to search for a wall and orient itself normal to that plane. The Proceq Pundit 250 array is a nondestructive device that the user applies against a concrete wall and scans using ultrasonic transducers. The back wall and other defects can be show through the touchscreen. A fixture to hold the Proceq Pundit 250 array onto the UR5 robotic arm was designed and 3D printed. It includes openings for easy access to the buttons from the Pundit array as well as a reinforced structural design. The scan from the Pundit array can show the back-wall, rebars, and other defects to assist in determining the structural integrity. In order to scan, a program was made to search for the wall first. 1. The UR5 robotic arm will go to the desired location away from the wall. 2. It will then search for the wall by moving towards it slowly. 3. After contact, the force applied will slowly but steadily increase to a preset value inputted by the user. 4. The force torque sensor will use those values and communicate with the UR5 by adjusting the arm to become normal to the wall. 5. The arm will then apply force to push back the transducers on the Pundit array to avoid gaps. 6. It will then prompt the user to collect data and wait until finished. The UR5 teach pendant allows the user to program the robot to perform automated and repeatable tasks. It includes a free drive mode which allows the user to move the arm to a desired position by hand. The user can also apply restricted planes which include either stopped or slower motion to ensure safety. The concrete sample being tested includes rebars and different grades of surface roughness to understand the readings from the Proceq Pundit 250 array. Desired concrete samples are currently being fabricated that will simulate the terrain being tested.

3D PRINTING↗

DOE OSTI2020

Radioactive Source Localization via Bayesian Particle Filter

In the event of a misplaced radioactive source or other emergency situation, measuring a radiation field, mapping its distribution, and determining a source location are essential tasks to ameliorating the situation. However, radiation fields may be extremely hazardous to human surveyors and minimizing received radiation doses is just as essential. Robots appear to be a potential solution to these problems. Beyond simply measuring radiation, the robot's computer processing capabilities offer a way to apply complex data analysis methods to radiation measurements in real-time. Methods which predict likely source locations can then feed this information into other processes, potentially improving path planning and enabling more efficient measurements. Given a robot mounted with a gamma-ray detector, can we: develop a methodology to account for detector performance across a wide range of source angles, distances, and photon energies? operate an autonomously navigating robot to effectively survey and characterize an area of interest? implement a data analysis method, conventionally used in measurements of motion, for source localization purposes? An open-source TurtleBot 3 robot, running Robot Operating System (ROS) on Ubuntu 16.04 LTS, was fitted with a Kromek GR1{sup R} Cadmium Zinc Telluride (CZT) solid-state gamma-ray detector. As a part of ROS, the packages OpenSlam, gmapping, and amcl were used to perform Simultaneous Localization and Mapping (SLAM), determining the robot's position and mapping the surrounding area. Data was acquired via Lidar mounted on top the TurtleBot 3. Detector Calibration Fit: The equation was fit to 365 counts of various energies, distances, and angles. A MATLAB{sup R} program was written to simulate measurements taken a robot on a random walk, with count data and positions discretized into finite element pixels. Using this program, a sample of 100 runs was performed on a map with a simulated source at the center, with a total of 200 of 2 pixels each. Similarly, multiple runs of the filter were performed on recorded robot measurement data. In both simulation and real tests, when corrected for errors (particles placed outside of bounds or on the robot, and simulation-specific errors), corresponding t-tests of predicted x and y-coordinates were within a 95% confidence interval of the actual position. For the real trial, these positions are slightly skewed right in the x-axis as the robot remained mainly to the left side of the source within the sample area. These simulations demonstrate potential validity for the usage of a particle filter as method of radioactive source localization. In the future, true real-time implementation and data fusion may further augment the performance of the robot to localize lost sources. Additionally, identification of multiple sources, determination of source types, and usage of a collimator are areas to potentially be explored.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Savannah River Site H-Canyon Advancing Technologies for Remote Inspections - 20345

In 2017, the DOE Environmental Management Office of Technology Development (DOE-EM TD) sponsored the H-Canyon Advanced Technology Demonstration (ATD) to demonstrate to DOE facilities the value of using new commercial-off-the-shelf (COTS) and near-ready technologies to solve difficult problems and enhance worker safety. The DOE Savannah River Site (SRS) H-Canyon Air Exhaust Tunnel (HCAEX) inspection task was identified as representative of the hazardous, human denied environments which could benefit from advanced technologies. The HCAEX underground concrete tunnel is visually inspected biannually using a camera mounted on a remotely operated vehicle (ROV) designed and built by SRNL. While tunnel images have provided valuable visual information, it is desirable to have a higher order of understanding of the environment to support a more thorough structural integrity (SI) analysis and for long term planning purposes. As part of the ATD, the Concrete Integrated Product Team (CIPT) was formed to identify and evaluate available sensors and methods mature enough to remotely obtain tunnel concrete characterization data of high value and with a high probability of success. The team included SMEs and H-Canyon stakeholders in the field of concrete, nondestructive examination (NDE), structural integrity, sensors and remote systems from SRNL, SRNS, LANL, DOE-SR and the Army Corps of Engineering. The CIPT completed an in-depth identification of customer concrete inspection needs and potential technology solutions. Sensors and methods were evaluated on performance, data usefulness, cost and the feasibility of a successful deployment given the unique tunnel access challenges and environment. Two technologies were identified as promising by the CIPT for near term demonstration and evaluation: Lidar (Light Detection and Ranging) 3-dimensional (3D) mapping and remote robotic deployment of NDE instrumentation. Laser spectroscopy to characterize tunnel surface chemical changes was also of interest, but presently cost prohibitive. This paper will include a discussion of the two efforts underway to evaluate and implement the CIPT recommendations. First, the status of the November 2019 deployment of Lidar at a single location into the tunnel is presented. This initial deployment provided the team a learning curve and lessons learned on the challenges of tunnel deployment to include remote operation and data collection, stabilization of the sensor in high air flow (∼30 mph), ability to achieve a tolerance accuracy of 0.25-inches, and the probability to identify change in tunnel dimensions over time. Secondly, a discussion on the development of the Robotic Arm Concrete Inspection Test Bed capable of deploying NDE instruments to examine custom concrete forms will be presented. Concrete forms simulating the rough concrete surfaces, strength, composition and potential structural defects that can be found at our DOE EM facilities have been designed and built for the test bed. Two state-of-the art concrete NDE instruments have been identified as having potential to work on rough concrete walls, they are being tested and characterized as to their ability to provide desired structural integrity data to include wall thickness and defect identification on the developed test beams. Lastly, lessons learned, and the path forward will be presented. (authors)

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