# Survival 1.0: A Novel Autonomous Industrial Mobile Warehouse (AIMW) for Industrial Environments
## 1 Introduction
### Autonomous Mobile Robot (AMR) vs. Automated Guided Vehicle (AGV)  
The first known automated guided vehicle (AGV) was introduced by Barret Electronics of Northbrook, Illinois, USA, in 1953 [8]. However, its appearance in the industry in a massive way began a little over a decade ago, driven by the advances introduced in the fourth industrial revolution.  
One of the essential initial premises is to understand the difference between an Autonomous Mobile Robot (AMR) and an automated guided vehicle (AGV) [9; 10].  
An AGV has minimal on-board intelligence and can only obey simple programming instructions. To navigate, it must be guided by cables, magnetic strips or sensors such as RFID (Radio Frequency Identification) [11], which generally require the installation of extensive (and expensive) facility upgrades, during which production may be disrupted [12]. The AGV is restricted to fixed routes, which require additional cost and interruption if changes are needed in the future. The AGV can detect obstacles in front of it, but cannot navigate around them, so it simply stops short until the obstacle is removed [10].  
On the contrary, the AMR navigates through maps built through a learning process, which consists of scanning the environment by using sensors or through previously drawn maps that represent the environment of the facility [12]. This ability can be compared to a car with a GPS and a set of pre-loaded maps. When it is shown the address of the owner's home and work, it generates the most direct route based on simple positions on the map [10]. This is similar to the way the AMR is taught to find out the location to pick and drop the parts. The AMR uses data from cameras, built-in sensors and laser scanners, as well as sophisticated software that allows it to detect its environment and choose the most efficient route to the target. It works completely autonomously, and when forklifts, pallets, people or other obstacles are in front of it, the AMR will safely manoeuvre around  
Figure 2: System for distribution of materials.  
them, using the best alternative route. This optimizes productivity by ensuring material flow remains on schedule [10].  
AMRs are often used for material handling. They are usually designed in the form of robot arms or actuators built on mobile platforms [12]. AGVs, on the other hand, are most often used in industrial applications to move materials around the manufacturing line or in a warehouse [4; 12]. In some cases, an AMR can be built with an AGV that is used as a mobile base to achieve the stated objectives [13; 14].  
Currently, research on AGVs and AMRs has become a topic, motivated mainly by the rise of Industry 4.0 technologies and by the fierce competitiveness of companies. In the case of AGVs, research is continuing to improve these systems, especially when there are multiple AGVs sharing the same routes, see [15], and research is being carried out to increase their capabilities and transform them into Smart AGVs that can interact with humans, see [16]. In the case of AMRs, one of the main lines of research is to make them more robust for industrial environments in the process of autonomous navigation, continuous self-location and representation of the goal [17]. In [9], they try to optimize the behaviour of AMRs in industry through the \"Highway code\" concept. The idea is the generation of high-level rules that manage AMRs, in a first version, in simulation environments.  
One of the applications that AMRs have in the industry is found in what is known as Industry 4.0 warehouses, smart warehouses [18]. In this research, we work on how to optimize the automatic management of warehouses through the AMRs and how they interact with each other, etc. In general, the industrial solutions, see [19], usually involve programming an AMR to carry a shelf or cabinets where operators place the goods and then send them to another site, see Figure 3. These robots are prepared to work in controlled environments rather than in industrial environments such as factories.