## 2 Preliminary  
### Equipment  
The 3D diagram and the chassis structure of the implemented small-sized mobile robot are shown in Figure 1. The size of the robot is 18 \(\times\) 18 \(\times\) 21 cm\({}^{3}\). The key hardware components are listed in Table 1. To fulfill all the computational performance requirements needed for the study, we use Intel NUC and Jetson AGX Xavier to let the robot have good computing processing capability and graphics processing capability. The challenge of this design and implementation is to effectively arrange all the necessary hardware components within the limited space. Despite the larger size of the Jetson AGX Xavier, the high-performance GPU is an indispensable part of this design. Through meticulous layout, this study successfully integrates the Intel NUC, OpenCR, two XM-430 motors, batteries, a DC-DC stabilizing chip, a 2D LiDAR, and two fisheye cameras into the compact structure of the small-sized robot. This setup satisfies all requirements for environmental data collection, algorithm computation, and motor control of this study. The Jetson AGX Xavier has exceptional image processing and deep learning capabilities, so it is selected as the primary computing unit. It supports Nvidia's Compute Unified Device Architecture (CUDA), enabling us to perform General-Purpose computing on Graphics Processing Units (GPGPUs) on NVIDIA GPUs. This approach significantly accelerates processing speed by directly accessing the virtual instruction set and parallel computing elements in the GPU, offering a performance several times, or even tens of times, faster than using a CPU alone. Figure 1: Description of the implemented small-sized mobile robot: (**a**) 3D diagram of the robot; (**b**) the chassis structure of the robot. Moreover, the small-sized robot is equipped with an RPLIDAR-A1 LiDAR on its top. The specification of RPLIDAR-A1 is listed in Table 2. With a resolution of less than 1 degree, it effectively provides 2D data points, significantly enhancing environmental perception capabilities. The high-precision obstacle detection offered by this LiDAR is crucial for obstacle avoidance, providing key data for this function. The input/output relationships of the system architecture are shown in Figure 2. It primarily includes three components: sensors, data processing modules, and control systems. Two fisheye cameras and a 2D-LiDAR transmit RGB images and point cloud data via USB, which are processed by the YOLO model, line following system, and virtual force field system to generate object information and target direction information. Data are transmitted through DDS and UART to the data integration module, where it is consolidated to produce road information. The control systems receive the road information and send control signals to the motor via UART, adjusting the motor speed to achieve autonomous navigation. Each component is deployed on hardware platforms such as Jetson AGX Xavier, Intel NUC, and OpenCR, ensuring efficient system operation. The visual processing of the system is primarily handled by the Jetson AGX Xavier, which is connected via Universal Serial Bus (USB). The Jetson supports USB Video Class (UVC) and Linux V4L2 (Video for Linux 2), ensuring compatibility with most cameras. After receiving images from the cameras, object detection is conducted using the YOLOv4-tiny model, which is capable of quickly and accurately detecting all road markings. The Intel NUC is the main control platform, which uses ROS2's DDS as a communication bridge with the Jetson AGX Xavier to handle more complex computational tasks, such as road detection and data processing. It receives data from the LiDAR and another fisheye camera. Leveraging the CPU's advantage in handling complex logic and control flows as well as memory access, the NUC can provide these computational capabilities in realtime. Finally, OpenCR, as the motor's control center, communicates with the Intel NUC via UART.