## 4 Results and Discussion  
In this study, a highly integrated and efficient small-sized mobile robot is designed and implemented. It consists of a CPU (Intel NUC), a GPU (NVIDIA Jetson AGX Xavier), sensors (a 2D LiDAR and two fisheye cameras), a power system, and the proposed configuration of  
Figure 6: Description of environmental data obtained by LiDAR and fisheye camera.  
IO interfaces. The primary objectives of this study are to achieve three core functions of the small-sized robot: road detection, sign recognition, and obstacle avoidance.  
For road detection, we divide the captured image into four areas and use the Intel NUC processor to perform road detection calculations. The proposed method can significantly reduce the load on the system. In addition to successfully and accurately detecting lines on both sides of the road, the proposed method also has a high processing speed of 25 fps. In complex environments, this fast processing is critical to avoid the potential of using delayed data. It ensures that the robot can instantly eliminate images and avoid obstacles.  
For sign recognition, we use the YOLOv4-tiny model and use a data augmentation strategy to significantly improve the computing performance of this model. From a series of experiments, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. Although YOLOv4-tiny is a simplified model of YOLOv4 in structure, the data augmentation strategy used in this study ensures its detection accuracy in complex environments. In addition, the high-speed processing capabilities of YOLOv4-tiny in this study can show that it has obvious advantages in object recognition for small-sized robots.  
For obstacle avoidance, we propose a distance-based filtering mechanism, which can simplify the processing of data points collected by LiDAR. The proposed method can filter out some important data points and assign appropriate weights, which can effectively reduce the amount of data processing and improve the robot's response speed to avoid obstacles.  
As shown in Figure 7, testing on the Jetson AGX Xavier platform showed an average processing speed of 24.10 fps in 30 W mode and 21.90 fps in 15 W mode. There is not much difference in processing speed performance between the two. When the power supply is sufficient, the 30 W mode can significantly improve the overall efficiency of the system. However, taking into account power consumption and the operating speed of other programs, this study chose the 15 W mode. In conclusion, this study successfully implemented functions such as road detection, sign recognition, and obstacle avoidance on a small-sized robot. The system demonstrates strong resistance to environmental disturbances, proving its robustness. The results of this study have important implications for the design and application of small robots, especially in system architectures with limited resources. Through good data augmentation and algorithm optimization, even the highly simplified YOLOv4-tiny model can achieve high accuracy while maintaining high processing speed.  
In future work, we plan to continue to expand and optimize the functionality and applications of the implemented small-sized mobile robot. The focus will include in-depth optimization algorithms, especially deep learning models, to enhance the accuracy and robustness of the system in more changing and complex environments. Advanced data augmentation techniques and adaptive algorithms will be explored to better adapt  
Figure 7: Average processing speed in 30 W and 15 W modes on the Jetson AGX Xavier platform.  
to dynamic environments, extreme conditions, and complex road obstacles. End-to-end learning will be one of the directions for improvement. In addition, sensor fusion techniques will be further explored by integrating various sensors such as LiDAR and RGB-D cameras to improve the accuracy of environmental perception and dynamic obstacle detection.