## 4 Detailed Proposed Technology and Test Results  
### Acquisition of High-Quality Deep Learning and Inspection Data  
In environments such as the manual assembly process in car production factories, which are narrow and complex, and where obstacles like bolts and nuts exist, a visual acquisition device must be developed that can acquire images in real-time and in the same space as the operator. In this study, we utilized a Boston Dynamics' (Waltham, MA, USA) four-legged robot, called SPOT, which has a relatively small body and the ability to move through narrow spaces with its four-legged walking system, as well as the self-SLAM technology that enables it to avoid obstacles. Additionally, the 4K camera attached to the SPOT package's seven-axis robot arm enables the easy acquisition of the part images [9]. However, due to the characteristics of the four-legged walking system, the repeated positioning accuracy exhibits a deviation of more than \(\pm\)200 mm from the body base; there is also deviation when acquiring images using the camera attached to the arm. This is a common problem with all mobile robots used in industrial sites, and it can degrade the quality of the data, thereby affecting the performance of AI inspection. Unlike stationary robots that employ positional constraints, mobile robots utilizing methods such as visual SLAM can experience location errors ranging from 10 cm to 1 m. This variation in  
Figure 2: Integrated vision AI inspection system flowchart for quality testing in industrial environments. the positioning accuracy further complicates data acquisition and can significantly impact the effectiveness of AI-based inspections. Therefore, a solution must be developed to address this issue. 1.1 Landmark (Fiducial Mark) Centering Technique for Improving the Repeat Positioning Accuracy of SPOT  
The SPOT robot uses five ToF cameras on its body for visual SLAM-based position movement. However, the reference vehicle moves using an AGV or conveyor for component imaging, causing position dispersion. Furthermore, the characteristics of quadrupedal walking result in poor repeat positioning accuracy with errors of over \(\pm\)200 mm, as shown in Figure 2(a). To address this problem, short-range communication devices, such as UWB technology, have been used to improve the repeat positioning accuracy in the industrial field [10,11]. However, this method incurs additional costs for installing infrastructure such as UWB transceivers in the surrounding environment, as well as AGV or vehicle attachment and removal of UWB. The location accuracy may also be reduced in environments such as car factory structures that can cause wireless signal fading. In this study, a landmark (Fiducial Mark) was attached to the AGV to consider the characteristics of the industrial field and minimize investment costs, which serves as the reference for position movement. A vertical reference point was specified between the SPOT body and the F-Mark to align the body accurately. This method does not require additional modification or cost even when the process changes or when new vehicles are introduced, as only the F-Mark must be attached without requiring additional infrastructure installation. Using this proposed method, the SPOT robot utilizes its front-facing camera to recognize the size of the attached F-Mark and measures its size and angle upon reaching the inspection site, as depicted in Figure 2(b). By centering the SPOT body to be perpendicular to the F-Mark and adjusting the pre-set distance values, the positional error was reduced from \(\pm\)200 mm to as low as \(\pm\)14 mm. However, even if the error of the SPOT body is small, the positional error of the camera at the end of the arm that acquires the image accumulates based on the arm pose, causing a large deviation in the acquired image.