## 1 Introduction  
With the rapid development of robotics technology, autonomous mobile robots (AMRs) have been increasingly used in industry, services, and academia [1; 2; 3; 4]. In academia and research, small and open-source platforms like the TurtleBot3 robot [5] are ideal for exploring complex robotic problems [6]. In industrial applications, AMRs are widely used in factory automation, such as logistics and warehouse management, and can move goods autonomously to improve production efficiency. In the service field, AMR can be used to provide food delivery services in restaurants and guide customers in large shopping malls. Additionally, autonomous driving technology is increasingly used on roads. By integrating data from various sensors, such as cameras, radar, and ultrasonic sensors, automated driving technology enables vehicles to autonomously perform tasks such as navigation, environment sensing, and decision-making without human intervention. Autonomous vehicles enable accurate road detection and sign recognition, improving traffic safety and efficiency. The development of this technology not only changes the traditional transportation model, but also provides new opportunities for the construction of smart cities. However, the realization of efficient autonomous robots requires overcoming many technical challenges to ensure that the robot can reach the target location correctly and safely. Therefore, this study aims to build a small-sized mobile robot and implement road detection, sign recognition, and obstacle avoidance methods on this robot. Road detection enables robots to autonomously recognize and follow roads, crucial for navigation in complex environments. In previous research, [PERSON] et al. [7] proposeda picture-based approach to detect single-line contours but encountered difficulties in accuracy and corners. [PERSON] et al. [8] proposed a low-cost vision-based road following system for autonomous mobile robots working in outdoor environments. The system combines color-based road detection to detect the asphalt road surface and uses the Unscented Kalman Filter (UKF) to enhance the robustness of the system. While effective, this system may cause incorrect navigation on urban roads. [PERSON] et al. [9] proposed a real-time lane detection algorithm that combines geometric and image features dynamically adjusting the Region Of Interest (ROI) based on vehicle speed. It performed well in complex lighting but struggled with strong illumination and shadow changes. In summary, these studies demonstrate different approaches and challenges in road identification and navigation technologies. Image technology has significant advantages in road detection, and despite the challenges of strong light and shadow changes, these limitations can be overcome through improved algorithms and multi-sensor fusion to enhance the navigation capabilities of autonomous mobile robots in complex environments. Sign recognition is one application of object recognition. Object recognition is an important part of robotics that enables robots to understand the information in the environment. Deep learning algorithms have proven effective for complex vision tasks [10; 11; 12]. There are four main branches of object recognition, including image classification, object detection, instance segmentation, and semantic segmentation. Image classification is one of the most basic fields, which mainly utilizes CNN to recognize the whole image [13; 14]. The more common algorithms include AlexNet [15], VGGNet [16], ResNet [17], etc. Object detection can further accurately identify the position of the object [18; 19], usually marking a bounding box around the object. Common algorithms mainly include YOLO [20; 21] and SSD [22; 23] for the one-stage method, and Fast R-CNN [24] and Faster R-CNN [25] for the two-stage method. Instance segmentation is a method that can recognize the location of objects [26; 27] and accurately mark the outline of each object. Common algorithms include Mask R-CNN [28] and YOLACT/YOLACT++ [29; 30].