## 2 Materials and Methods  
To facilitate the use of robot-location tracking for decision support, a graphical user interface (GUI) for NDE operations was written in the.NET framework using the Visual Studio integrated development environment based on the Windows Presentation Foundation and C# as the programming language. XAML is used as the GUI designer and Model-View-Viewmodel is used as the display pattern. The GUI displays video footage from cameras on the robot traveling in the air slots and real-time data from the ultrasonic sensor at the selected measurement sites. A composite image constructed from ultrasonic sensor data collected at multiple sites can replace the real-time data if desired. The GUI moves a marker on a map of air slots to illustrate the location of the robot during data collection. This display also shows tank-specific weld patterns connecting plates that make up the bottom of the primary tank in spatial relation to air slots in the refractory concrete pad under the primary tank. Data to construct the overlay of welds and air slots were kindly provided by WRPS staff. The pattern of welds in the bottom of the primary tank varies for tanks constructed at different times. We used data provided by WRPS to generate overlays for AZ-, SY- and AW-type DSTs. AN- and AP-type DSTs have the same tank-bottom weld pattern as AW-type DSTs. All these DST types have the same air-slot pattern in the refractory pad. Image analysis to enhance the information content of video footage from the cameras on the robot was carried out in the Scientific Python Development Environment, Spyder 4.0.1, using cv2, a computer vision software package that includes OpenCV Canny Edge Detection [8]. Initial analysis was conducted on a 15-min video from visual inspection of air slot 31-1 in DST tank AP-107 [9] and kindly provided to us by WRPS staff. Additional video footage was obtained, and the results of the analysis were tested in our laboratory at WSU-TC using an air-slot mockup kindly provided to WSU by PNNL. Data were obtained with a vehicle pulled manually through the mockup while carrying a small laser range finder and two cameras recording forward and rear videos. The U81 laser range finder (JRT Meter Technology) was chosen for its small size (\(17\times 41\times 7\) mm), weight (4 g), and voltage requirements (2.5-3.3 V). The VoCam264 (ameriDroid) was chosen for its small size (\(20\times 20\times 25\) mm) and to achieve 30 fps at resolution \(1920\times 1080\). The entrance to the channel was covered with graph paper with 5-mm squares, on which a horizonal line was drawn 30 mm long and at the height of the camera in the robot mockup. This line was used to determine the effective \(F\)-value to use in the triangle-similarity method [10] to determine the distance of the camera from an object. The length of the line in pixels was measured in an image with the camera a known distance from the entrance of the channel as measured by the laser range finder. Pulling the robot mockup down the channel using cables coming from the camera and laser induced roll into the motion. The extent of roll was reduced by a cable management system but could not be eliminated. Since tilting the robot toward either side of the channel changes the perspective of the image relative to that of the calibration set up, we collected the data necessary to implement the triangle-similarity method in the presence of roll. To accomplish this, we lined the channel with 5-mm-square graph paper, being careful that the grid was parallel and perpendicular to the top edge of the channel. We highlighted four grid points--two on each side of the channel 50 mm apart--at the top of the channel, and recorded video footage with the robot mockup tilted to the left- and right-side of the channel. Figure 1 shows a conceptual drawing of a robot to move sensors through the air slots under DSTs for NDEs.