# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 2 State of the Art
### Mobile Robots for Factory Automation  
Industrial technology advancement and the diversity of manufacturing strategies have raised the need for flexible and robust systems to fulfill different tasks with minimal or no changes. Mobile robots have become the main tool in solving logistics' problems of increasing productivity. The robots that are most commonly used in industrial manufac  
Figure 1: ABB IRB 140 robot and mobile four wheel drive robot in laboratory. turing facilities or warehouses are automated guided vehicles or AGVs. They are portable robots that use marked lines on the floor, radio waves, cameras for vision, or various types of sensors for navigation. They are driverless vehicles, battery powered, and suited for transferring products or equipment in an industrial environment. The AGV can have objects hooked up, such as a trolley or a trailer, which can attach itself automatically [6]. Forklifts and simple carrying beds can also be installed on the AGV in order to both lift and place a product, or to simply carry and store many parts, respectively. In order to move somewhat freely and without many constraints, a set of omni-directional wheels can be installed on the AGV [7]. Omni-directional wheels can be designed as conventional or special wheels. Conventional wheels are made by Swedish engineer [PERSON], and they are also called Ilon's wheels or Mecanum wheels. These rollers have an axis rotation of 45\({}^{\circ}\) and can move and rotate in almost any given direction. Mecanum wheels enable 3 degrees of freedom mobility of mobile robots. Special omni-directional wheels have changes in rollers designs [8] or angle positions of rollers to ensure greater stability [9]. Special omni-directional wheels can also have more freedom in rotation and reposition, so they can be utilized in more challenging tasks. AGVs with installed omni-directional wheels are widely spread and commonly used in industry. The navigation of autonomous mobile robots requires accurate localization. In order to provide a robot with its precise location, a combination of various sensing methods and SLAM algorithms is used. For successful navigation through an environment, this approach needs to satisfy the following:  
* Online computation and decision making, which is required in order to avoid unsafe situations and ensure easier incorporation of algorithms with other processes on system without overloading CPU time. * Ability to adapt to dynamic environments, illumination changes or repetitive environments. * Low-drift odometry provides information about robot position when it cannot localize itself on the map. Until the SLAM algorithm localizes again, odometry drift should be minimized to provide the system with accurate position information so that navigation is still possible. There is a wide variety of SLAM approaches integrated in ROS, such as ones with reliable solutions for planar environments using Rao-Blackwellized particle filters [10]. The availability of enough estimated particles is required to converge to a solution which well represents the environment. Hector SLAM [11] can create fast 2D occupancy grid maps from 2D LiDAR with low computation resources. One of the drawbacks of Hector SLAM is that it does not implement loop closing. (Loop closure algorithms determine whether or not a robot returned to a previously visited area [12]. Loop closure reduces the uncertainty in the mapping and improves the precision of the localization of the robot.) Leaving this feature out was done to maintain low computational requirements. On the other hand, the Hector SLAM approach does not require an external odometry source, which is an advantage in environments with high geometry constraints. In addition to 2D LiDAR-based SLAM approaches, visual SLAM can also be used in mobile robot navigation problems. One of the available visual SLAM approaches is ORB-SLAM2 [13], which is feature-based visual SLAM. ORB-SLAM2 can be used with a stereo camera system, or RGB-D camera.