## 1 Introduction  
In Industry 4.0 and smart factory evolution, companies are developing from fully automated assembly lines to complete automation encompassing the entire production floor to the warehouse [1]. Automated Guided Vehicles (AGVs), first introduced in 1955, are developed as high-automated, low-labor logistic platforms to support large-scale material flows in production plants, warehouses, and distribution centers. Replacing conventional trucks with AGV systems greatly benefits the sustainable development of logistic operations [2] because of the safety, efficiency, economy, accuracy and productivity [3]. Industry 4.0 signifies a fresh industrial framework catalyzed by disruptive technologies, with the potential to revolutionize manufacturing into a cyber-physical system seamlessly uniting products, people, and processes. [PERSON] et al. opt for AGVs and the Internet of Things (IoT) as focal points to devise an operational strategy aiding managers in incorporating Industry 4.0 technologies into their manufacturing systems, thereby realizing lean automation objectives [4]. However, the adaptation challenge of AGVs becomes particularly prominent [5]. Frequent changes take place in large-scale flat warehouse environments, which make accurate prediction and description difficult to obtain, especially in multi-variety, small-batch, and flexible manufacturing production plants. The dense arrangement of finished products, raw materials, and assorted equipment presents significant hurdles. The frequent movement of goods in and out of storage results in frequent shifts in obstacle positions, bringing inconvenience and even danger to navigation based on predefined static maps [6]. In a factory assembly line environment, Vehicle-to-Everything (V2X) technology, based on IoT engineering, allows AMRs to interact with each other and with workstation units to efficiently complete scheduling and production tasks. However, V2X technology encounters significant challenges in achieving environmental perception in warehouse settings, where most obstacles are unable to exchange information [7]. Furthermore, the limited sensing range of onboard sensors restricts the effectiveness of environment perception and navigation [8]. The lack of comprehensive global directional guidance may cause AGVs to struggle to anticipate pathway changes, potentially leading to deviations caused by circumnavigation errors or even loss of orientation [9]. In order to solve the problem, AGV guidance systems have progressed through various stages including mechanical, optical, induction, inertial, and laser guidance, culminating in today's vision-based systems, which have promoted the traditional AGV to AMR [10]. The advantage of visual-based systems lies in the ability to enhance adaptability to various environments through diverse algorithms to ensure competitiveness [11], compared with traditional methods such as laying magnetic strips on the ground and detecting obstacles using 2D and 3D radars [12]. NASA and the Jet Propulsion Laboratory's autonomous robots rely on IMU-based visual-inertial odometry and precomputed trajectory planning for navigating static obstacle courses [6]. Ubiquitous sensors, powerful onboard computers, artificial intelligence (AI) methods, and SLAM technologies are utilized to perceive the environment and navigate within facilities without the need for predefined and implemented reference points [13]. Road recognition plays a pivotal role in geographic information processing and computer vision, with the primary objective of accurately identifying and extracting road information from visual images or map data [14]. In outdoor road applications, the convergence of multiple LiDAR devices may create a high-energy laser mist. Although it has not yet been experimentally proven whether prolonged or repeated exposure to this laser mist is harmful to the human retina, scholars still prefer to prioritize the development of vision systems for these scenarios [15].