## 2 Literature Review  
Over the past few years, the automation of internal logistics has spread to both large and small companies [1]. With material handling automation, organisations can optimise productivity and plan deliveries more efficiently [2]. Material handling technology has been developing rapidly in recent decades. The most significant advances in material handling technology have been the development of Automated Guided Vehicles (AGVs) and later the development of autonomous mobile robots (AMRs) [3]. With the technological level and experience with this automation technology available today, AGVs have found their way into almost all industries and production areas. The history of AGVs began in the USA in the mid-1950s [4]. An AGV is an unattended automatically guided vehicle equipped with contactless guidance with a microcontroller as the control core and a battery as the power source. The basic functions of an AGV are automatic driving, stopping point recognition, and load transfer [5]. Autonomous mobile robots are becoming increasingly important in a variety of fields; this is due to their ability to navigate different environments without the need for human intervention and electromechanical device guidance [6]. The automation of internal logistics and inventory-related tasks is one of the main challenges of modern manufacturing companies. Currently, the most advanced technology for such applications is autonomous mobile robots, which, due to their high adaptability in dynamic environments, are replacing more traditional solutions such as AGVs, which are relatively limited in terms of flexibility and require costly equipment upgrades for their installation [7; 8]. Autonomous mobile robots that move inventory within a facility may transport goods, raw materials, or finished goods from one location to another. These autonomous mobile robots are equipped with LiDAR, cameras, and ultrasonic sensors that allow them to autonomously navigate within the facility. They follow pre-defined routes or use real-time mapping to determine the most efficient route to their destination. Examples of these types of autonomous mobile robots include self-propelled forklifts and specified warehouse robots [9; 10; 11]. Autonomous mobile robots that assist in picking can streamline the order fulfilment process in both warehouses and distribution centres. These robots work in conjunction with human operators and reduce the physical effort involved in repetitive tasks such as picking items from shelves or baskets. Not only that, but these autonomous mobile robots use advanced technologies such as machine vision, artificial intelligence algorithms, and collaborative robotic arms to accurately identify and select the right items [12; 13]. Autonomous mobile robots have a wide range of small and energy-efficient sensing technologies such as integrated laser scanners, 3D cameras, accelerometers, gyroscopes, etc., that allow them to digitize the environment. Processing the sensing data using simultaneous positioning and mapping technology allows autonomous mobile robots to create a map of their environment and calculate their location [14]. However, conventional material handling equipment makes the production system inflexible to changes in process layout and routing. The availability of technologies using artificial intelligence for positioning and navigation can support the improvement of transportation in manufacturing systems by using intelligent vehicles such as autonomous mobile robots to obtain feasible solutions to increase the flexibility and productivity of manufacturing systems [15]. The techniques and knowledge developed have improved mobile robots at both the device and systems level. Artificial intelligence techniques have pushed mobile robot navigation towards autonomous driving and obstacle avoidance [16]. In general, the most used AGVs in industry are often bulky and require frequent human intervention to load and unload the equipment.