## 2 Literature Review  
Automated guided vehicle systems have been around for more than fifty years, during which time various technical advances have been made, ranging from improved drives and power supplies to completely new sensor concepts. Companies are looking to automate production, warehouse operations, or other logistics processes [1]. Automated guided vehicles are most often used for transporting goods in warehouses. Several authors have addressed the issue of automated guided vehicle trolles. If companies are in the logistics business, they may face challenges in recruiting and maintaining a full workforce, which can lead to difficulties in meeting productivity targets and ensuring compliance with delivered goods. Recent supply chain trends such as labour shortages and increased demand appear to be persisting with no signs of abating [2]. In response to these challenges and the need for better process management, logistics managers are increasingly turning to the automatisation of warehouses. However, a significant drawback of full warehouse automation is the substantial investment that is later required, including the installation of conveyor belts, aisle cranes, and tracking systems [3]. In addition, this installation process may render the facility non-operational until completion. Nevertheless, there is an appealing alternative to certain types of human-controlled transportation equipment in the form of autonomous guided vehicles (automated guided vehicles), which are increasingly reliable and cost-effective [4]. Automated guided vehicles can be used either on their own or in conjunction with an automated storage and re-storage system (ASRS--automated storage and re-storage system) and are highly customisable to different aspects of your supply chains, whether at the container or load unit level. This adaptability makes automated guided vehicles a versatile solution to address key challenges in logistics and supply chain management [5; 6]. Automated guided vehicle localisation is very important when running a whole-lot automation system, so in papers [7; 8], the authors propose a low-cost algorithm for localising automated guided vehicle trucks based on single-camera landmarks for warehouse operations. The proposed algorithm includes a computer vision algorithm for landmark recognition and distance estimation between the landmark and the single-camera automated guided vehicle. The landmark recognition accuracy is 93.26% overall. The average error of the localisation algorithm was 237.29 mm and the standard deviation was 184.27 mm. In conclusion, the automated guided vehicle localisation algorithm based on landmarks for warehouse applications has been successfully developed. The positioning of mobility robots is also addressed by the authors of [9], who define seven categories of positioning systems: (1) odometry, (2) inertial navigation, (3) magnetic compasses, (4) active beacons, (5) global positioning systems, (6) landmark-based navigation, and (7) model matching. For each category, the characteristics and examples of existing technologies are given. The field of mobile robot navigation is active and vibrant, with more great systems and ideas being developed all the time. The ability to navigate accurately is one of the fundamental capabilities of a mobile robot to perform a variety of tasks efficiently, including docking, transport, and handling. Since real-world environments often contain changing or ambiguous regions, existing features may be non-sufficient for mobile robots to produce reliable navigation behaviour. According to the authors of [10,11], the goal of the method is to find the minimum number of landmarks for which a boundary on the robot's maximum deviation from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places the landmarks using linearised versions of the robot's system dynamics, which allows for an efficient computation of the deviation guarantee.