## 1 Introduction  
Reconfigurable manufacturing systems are necessary for the development of flexible production systems [1, 2]. For this reconfigurability to be possible, industrial robots must receive real-time sensor feedback, an important part of which is visual feedback. This is necessary for a variety of applications, such as navigation, part identification and handling, human collaboration, etc. Visual feedback also may be fused with data from other sensors for a more complete digital representation of the surrounding environment. Generally, high accuracy is needed for the vision applications, but that accuracy varies according to each application. For example, a mechanical gripper will require much more accuracy to manipulate an object without damaging it, compared to a suction gripper. According to the desired accuracy and data types, 2D or 3D, different camera types are chosen, pinhole or stereo. Depending on the application, 2D or 3D data are extracted. The vision system can be either object-related or scene-related. Object-related vision systems regard the detection of objects, which allows the handling of them, such as picking or placing objects in workstations. For object-related vision systems, the camera is usually mounted near the acting tool of the robot arm. Scene-related vision systems regard the awareness of the robot for the environment around it. Examples of scene-related systems are vision systems for localization or collision avoidance, and the camera for these systems is usually mounted on the mobile robot. The above categories can be classified into two types of robotic perception. Object-related vision belongs to process perception, which represents the cognitive ability of the robot to identify the process parameters and successfully execute a manufacturing task. Scene-related vision belongs to environment perception, which is the cognitive ability of the robot to understand its environment in order to detect obstacles, areas of interest, its own place within a robotic cell, etc. There are many examples in industry where process and shopfloor perception have been implemented. In [3], a method using contour detection was proposed in order to avoid collisions when navigating a mobile robot, by recognizing the boundaries between objects and free space. Methods such as CAD matching have been used in many cases for object detection and manipulation [4]. In [5], a single camera was used to detect landmarks that are used for accurate localization of a mobile platform, which is necessary for the successful execution of tasks in complex manufacturing environments. There are challenges in using a vision system in processes in a robust manner. Even when initial calibration of the camera system is performed, which estimates the parameters of a lens and image sensor of a video camera, temperature changes or light changes can greatly affect the vision precision. There are also cases in which too much data is unnecessary and burdens the system, and simple solutions must be found instead. This is due to systems with limited resources such as memory and bandwidth, where too much data causes severe challenges [6]. The technologies of this work were implemented on a robotic system based on a hybrid dynamic reconfigurable shopfloor that was suggested by [PERSON] et al. [7] enabled through mobile dual-arm workers, the so-called mobile robot platforms (MRPs) visualized in Figure 1. It based its reconfigurability on its ability to adapt to changes on the shopfloor, which are detected online by the sensors. There has been a lot of research and work done on vision systems in robotics [8], while various other novel technologies for controlling mobile robots have been implemented, including new technologies such as augmented reality and brain-computer interfaces [9; 10].