# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 2 Use Case Mobile Robotics and Requirements
### Factory Cloud-Based Control of Mobile Robotics  
In cloud robotics, the control functionality of the robot is offloaded from the robot platform into an edge-cloud computing platform, such as a factory cloud, as shown in Figure 1. The control functionality includes motion planning, PLC, localization and mapping, and all the monitoring and computational demanding processes. The robot controller in the factory cloud computes the trajectory and predicts how to act in the environment with obstacles. This decision is based on the data that a mobile robot transmits from internal and external sensors to the factory cloud system for processing via wireless communication [19]. The evaluation and accuracy of the data are strictly connected with the choice of sensors. There are many approaches for data evaluation, since the correct and quick perception of the environment is seen as the most important characteristic of a mobile robot. Especially, approaches from computer vision and deep learning in connection with various camera-based sensors have shown a high potential for this.  
Figure 1: Architecture of a Factory Cloud-Controlled Robotics Application.  
Although mobile robots have great potential for many applications, they are still at the beginning of development and have only found their way into production chains in isolated cases. This is, among other things, due to some important challenges of current mobile robots. The most important challenges are listed below [20]:  
* Localization, navigation, and trajectory planning are highly complex and are still the subject of current research;
* Up to now, safe cooperation with people has only been possible in restricted movement modes;
* Detection of obstacles is not possible in the required time, depending on the sensor technology used.  
The limitation of the safe operation of a robot in a production environment is usually not specified by the moving mass but by the speed, detection, and reaction time. However, the sophisticated algorithms for obstacle detection and reaction often demand extensive computational resources that are generally unavailable on mobile robots, e.g., for energy consumption considerations. To establish complex anatomies, the decentralized processing of data acquired by the mobile robots enables high flexibility and supplies the computational resources required for complex tasks but has not been possible in the past due to the latency and reliability limitations of conventional wireless communication systems. As a new wireless technology, 5G is designed to satisfy both low-latency and high-reliability communication demands and provides enough bandwidth to resolve the issues mentioned above.  
Both 5G and TSN enable the real-time connectivity of mobile robots to the factory cloud systems, and with that, mobile robots can transfer computationally intensive tasks to the factory cloud systems [21]. By offloading such tasks from mobile robots, sophisticated and complex algorithms are possible for the localization tasks, safe cooperation, and real-time obstacle detection on the factory cloud systems. In this research, the cooperation between two mobile robots is targeted to be analyzed. Both 5G and TSN play a crucial role in mobile robots' synchronized movement when picking the material cooperatively in this use case.