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Material handling activities in an industrial warehouse environment are considered of high risk and lead to major injuries, both for the people who operate the vehicles and the rest of the staff. In addition accidents in and industrial environments are slowing down the production line and lead to money loss. The purpose of this research is to contribute to the automated management of an industrial warehouse (intra-logistics) providing an innovative real-time programming and control tool for managing intelligent autonomous vehicles (IAVs) (autonomous ground vehicle descendant). The proposed tool provides a fully automated real time mapping of the industrial environment. As a result, work flow and security levels are increased while energy consumption decreases due to the selection of the optimized route. According to the above, IAVs are a promising solution for the efficient execution of any process that involves transportation at operational level. Our research concludes that IAVs: (I) Provide real time information about warehouse parameters and conditions, (ii) Reliably operate 24/7, (iii) consume less energy than a conventional human driving machine, (iv) are an essential part of the fourth industrial phase (industry 4.0), (v) improve security levels in the warehouse. AGVs can be used to automate industrial processes and they are capable of managing real time disruptions at operational level by taking dynamic decisions and therefore affect the production rate of the facility. In this paper presented the installation, as well as the learning guild of ROS in Greek language (Chapter 1 and 2). We describe how to create in a simulation environment a model of a robot that meets the needs of an industrial warehouse(Chapter 3). Then we explain the theory and the role of Navigation Stack as well as the methods and the algorithms for mapping, localization, and movement of the robot to each of environments using relative coordinates(Chapter 4). In the last Chapter(5) we developed two algorithms and a model of an industrial warehouse. This first algorithm is about the procedure of SLAM and how it could be automated so it wont be necessary the innervation of human factor. The second algorithm is a simple action client to Navigation Stack. Fist drives the robot to the location of a specific location to pick up a box, then drives the robot at the exit of the warehouse to deliver the box, finally drives the robot to the parking spot to wait the next assignment.