Use Case 3: 5G-enhanced healthcare/logistic robots

In this use case, we will simulate a health case or industrial logistics scenario to demonstrate how a 5G-enhanced robot can help in such situations. The chosen platform will be The Robotnik RB-THERON. 

This robot is a versatile indoor autonomous mobile robot designed for logistics applications in industrial or healthcare environments. With a small footprint of 687 x 550 x 305 mm, improved traction system, lasers, and industrial-grade safety PLC, it can safely operate in mixed environments with people and in constrained or narrow spaces.

This lightweight robot weighs only 55 kg and can transport a payload of up to 200 kg using its lifting system. This makes it ideal for moving linen baskets, food or medical equipment trolleys, and other industrial items. it can travel at up to 1.25 m/s with almost silent movement, so it won’t disturb workers, patients, or visitors.

The RB-THERON is equipped with 360-degree safety lasers and dual RGB-D cameras for 3D perception. It also has a state-of-the-art 5G router for communication with nearby edge devices, other robots, and powerful cloud servers. The robot uses the open-source ROS 2 architecture, which makes it easy to integrate with other systems.


As part of the 5G-ERA project, we will test this robot in a large indoor industrial/healthcare environment with 5G infrastructure at Robotnik’s facilities in Valencia, Spain. This use case will demonstrate the benefits of offloading demanding tasks to edge or cloud devices to extend robot autonomy and enhance computational capabilities, with minimal effort required from robotic developers.

Network applications


This network application will allow the robot to create a robotic map and locate itself within it using 3D data extracted from the environment using a 3D LiDAR, 2D laser scans, or RGB-D cameras.

These tasks are CPU-intensive, and the configuration can outrun the resources of the robot. Transferring them to other devices allows the use of more powerful devices without the robot’s computational constraints, and will show a decrease in battery consumption.

This network application will be used in both Use Case 1 and 3.

5G-Signal Mapper

This network application will create a semantic map of the 5G signal quality of an area. This is extremely useful for offloading applications that rely on a good 5G link to transfer data to an external device.

Once the signal quality map is created, the robot can avoid low-signal areas, resulting in a smooth offloading experience. This is also extremely useful for the 5G network operator, as it provides information about the signal and can be used to improve the quality of service given to users.


This network application will be used in both Use Case 1 and 3.

Human Presence

This network application will send live streaming video of the robot and use cutting-edge AI object recognition to track humans and their position in the environment. This will help rescuers track trapped humans in disaster events and navigate safely to their last known location in a second round.


This network application will be used in Use Case 1.

Fleet management

This network application will significantly improve robotic fleet management by enabling the seamless and effortless deployment of a complex fleet management system with smart location placement (different cloud or edge devices) and without a complicated onboarding process.

The network application will rely on the OpenRMF fleet manager and will be used in Use Case 3.


This network application will allow a remote user to teleoperate a robot with low latency. This application will demonstrate the capabilities of 5G-era slices to meet the network requirements for teleoperation. It will be used in both Use Cases 1 and 3.

Frontier Exploration

This network application will group together the SLAM, teleoperation, and signal mapping network applications. This will allow users to explore an unknown area and create a map of the environment and signal quality.

This network application will be used in Use Case 1.