## 1 Introduction
Semantic segmentation [26; 31] divides images into parts and classifies each part at the pixel level, using algorithms like DeepLab and FCN [32]. The field of object recognition is also developing rapidly, including real-time processing, multi-task learning, multi-modal learning, and transfer learning, and can even be practiced in edge devices, so that it can be applied in the field of rapid response requirements. On the other hand, as the application of these techniques increases in sensitive areas, the need for interpretability is also expanding, which is always an important issue to be addressed [33; 34]. As for road sign recognition, [PERSON] et al. [35] enhanced the YOLO model, improving precision and recall, particularly for small objects and complex scenes, thereby boosting the safety and environmental awareness of autonomous driving systems. In summary, object recognition technology plays a crucial role in robotics and autonomous driving. With the continuous development of deep learning technology, many advanced algorithms have demonstrated their powerful ability and wide application potential in various complex visual tasks. In terms of obstacle avoidance, it mainly focuses on the robot's real-time identification and avoidance of obstacles, using various advanced sensors to collect information from the environment and perform visual algorithms based on either traditional or deep learning computations to enable the robots to respond to the obstacles [36]. Key research areas include sensing technology, environment sensing and data processing, path planning and decision-making, and multi-robot collaboration. Sensing technology employs RGB/RGB-D cameras, 2D/3D optical radar, radar, ultrasonic sensors, and infrared sensors, often combined to leverage their strengths [37]. Environment sensing and data processing focus on data fusion, scene understanding, and real-time performance for accurate and reliable system operation. Dynamic obstacle avoidance path planning emphasizes real-time detection and response to moving obstacles [38; 39]. For this part, Dynamic Path Planning (DPP) can more fully include the whole scope, from recognizing moving obstacles to real-time path adjustment, which are all discussed in DPP, and even GPP and LPP are usually included for perfect path planning. [PERSON] et al. [40] proposed a local path planning algorithm using high-precision GNSS/GPS for global path location, real-time dynamic sensing for environmental information, and B-sample curvature interpolation for smooth local paths, enhancing performance and stability. [PERSON] et al. [41] proposed an AGV path planning algorithm that incorporates the improved A* algorithm and the Dynamic Window Approach (DWA) for obstacle avoidance. Meanwhile, the use of deep learning in obstacle avoidance is growing, with technologies still evolving. [PERSON] et al. [42] proposed a Deep Q-Network (DQN)-based approach for navigating an autonomous driving robot. Their experiments in a Gazebo simulation environment demonstrated significant improvements in the robot's navigation efficiency and accuracy. Therefore, by integrating advanced sensing technology with data processing, path planning, and decision-making, the obstacle avoidance system can realize higher accuracy and reliability. To summarize, with the rapid advancement of robotics technology, autonomous mobile robots are increasingly used in industrial, service, and academic fields. The aim of this study is to explore the applications of mobile robots in road detection, sign recognition, and obstacle avoidance. This study not only integrates the existing technologies but also provides more possibilities for the application of small-sized mobile robots.