# The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution

Christina Lessi, George Agapiou, Marios Sophocleous, Ioannis P. Chochliouros, Renxi Qiu, Stelios Androulidakis

## ► To cite this version:

Christina Lessi, George Agapiou, Marios Sophocleous, Ioannis P. Chochliouros, Renxi Qiu, et al. The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.148-155, 10.1007/978-3-031-08341-9_13. hal-05335631

HAL Id: hal-05335631

https://inria.hal.science/hal-05335631v1

Submitted on 28 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution

Christina Lessi<fnref n="1" />(✉), George Agapiou<fnref n="2" />, Marios Sophocleous<fnref n="3" />, Ioannis P. Chochliouros<fnref n="1" />, Renxi Qiu<fnref n="4" />, and Stelios Androulidakis<fnref n="1" />

**Abstract.** Lately, autonomous robots have been dynamically appearing in the foreground, being the solution to many problems that concern different areas of everyday life. 5G-ERA Project's ambition is to propose solutions based on robotic applications that are targeting vertical sectors such as transport, healthcare, Public Protection and Disaster Relief (PPDR), and Industry 4.0. These sectors require intensive data transmission and processing, offering services that could take advantage from 5G networks capabilities in order to implement robotic collective intelligence approaches that were impossible in the past. However, 5G-ERA does not target a provider-centric 5G architecture. The project aims at a user-centered approach, where the main focus is on the Quality of Experience offered for vertical customers. Based on this approach, the designers of the 5G experimental facility should take into consideration the requirements of vertical sectors in order to be able to meet the new challenges that will arise. In this paper, the 5G-ERA healthcare and PPDR use cases will be presented, providing the proposed solution and the scenario that is designed. Additionally, the network design and requirements will be analyzed based on the needs of the use case.

**Keywords:** Autonomous robots · 5G networks · Management and orchestration · Network functions virtualization (NFV) · Virtual network function (VNF) · Standalone architecture · Reliability

## 1 Introduction

The main characteristic of an autonomous robot is its ability to perform tasks with a high degree of autonomy. However, in a realistic case taking place in an environment that is unstructured and very complex, learning all possible navigation and manipulation tasks before deployments, is impossible. Therefore, the degree of autonomy will be adapted based on circumstances [1]. There will be scenarios pre-programmed, while others can

<footnote n="1">¹ Hellenic Telecommunications Organization (OTE) S.A., 99 Kifissias Avenue, 15124 Maroussi, Athens, Greece
clessi@oteresearch.gr</footnote>

<footnote n="2">² WINGS ICT Solutions, 189, Siggrou Avenue, 17121 Athens, Greece</footnote>

<footnote n="3">³ eBOS Technologies Ltd., Arch. Makariou III and Mesaorias 1, Lakatamia, 2090 Nicosia, Cyprus</footnote>

<footnote n="4">⁴ School of Computer Science, University of Bedfordshire, Luton, UK</footnote>
be executed collaboratively with a high level of intelligence on Cloud or with human
operators, especially when the robots are not able to operate completely autonomously.
For instance, a robot could support remote trajectory planning services on the cloud
semi-autonomously for as much as possible, e.g., by ensuring collision-free motion
or by assisting the remote operators when grasping an object. And then enhancing its
capabilities through learning from this semi-autonomous experience.

Cloud-based robots are essential for realizing a high degree of autonomy for robotics. Supported by a “brain” in the cloud/edge, the robustness of autonomous robots will be improved significantly. The brain may be located in a data center, which makes use of Artificial Intelligence and other advanced software technologies to deal with resource-critical tasks or unexpected tasks. Traditionally, they are undertaken by a local, on-board controller due to connective constraints. Compared to robots running locally, “cloud robots will generate new value chains, new technologies, new architectures, new experiences, and new business models” [2]. The combination of cloud and robotics offers global libraries knowledge sharing and enables augmented human-robot interactions, as part of robotic services. Compared to traditional internet-based trajectory control, the key focus of cloud-based, autonomous robots is on knowledge collaboration. By shifting part of the cognitive capabilities from robots to the cloud, it enhances the potential of the robotic autonomous operations by utilizing the cloud’s massive computation power and global knowledge. Many projects have been delivered in this field, such as [3–5].

In order to combine the capabilities of autonomous robots and clouds, the robot
must be able to perceive and understand its environment. The perception enables the
operational knowledge to be shared between both sides and digital data representing
physical objects to be constructed on the cloud. This specifically includes the detection
of objects relevant to semi-autonomous or autonomous manipulation and the perception
of the environment by simultaneous localization and mapping (SLAM) objects and
obstacles that could be in the way of navigation or grasping. Since it is hard to predict
what objects the robot may face in the operational phase, it needs to be able to acquire all
relevant knowledge about new objects and to update it continuously during operations.

The 5G-ERA will implement the above learning model, using the system architecture
that is presented in Fig. 1. The main layers of the architecture are:

* Interface layer, which is responsible for accepting commands and presenting the information.

* Application Layer, which manages the activities and is responsible for performing tasks.

* Domain layer, which generates intent-based network policy based on specific domain knowledge pre-stored in semantic model and information model.

* Infrastructure enablement layer, which is responsible to manage virtualized hardware resources. It ensures the compatibility, scalability, and transparency of the testbeds.

* Infrastructure resource layer, which provides connectivity, computing resources, and storage.

* Smart event bus which connects event across the layers via event-based communica-
tion.
<figure><img src="image_3.png" /><figcaption>Fig. 1. 5G-ERA system architecture</figcaption></figure>

## 2 Robotics in Healthcare Vertical Sector

In the healthcare use case, the Robot will replace the nurse in a representation of a hospital environment where a patient is in quarantine. More specifically, the robot will be able to carry the medical treatment to the appropriate spot, place them in a specific area inside the room and check the condition of the patient by streaming a video of his current state to the nurse or doctor who is responsible for him/her. When the patient will take the medicine, then the robot will move back to its original position [6].

To perform the above actions, the robot must be able to support some functionalities. It has to select and use a specific map of the room and navigate autonomously inside the desired room. Additionally, it will use perception to find and manipulate the treatment in a predefined space in the room and use human detection to find the patient. Finally, it will be able to stream live content of the patient and send it to an application.

In Fig. 2, the use case is presented in more detail: the robot enters the room and moves to the medicine cabinet, which is located in a pre-defined spot inside the room.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Healthcare scenario</figcaption></figure>

The robot detects and picks the proper medicine. At the same time, the robot streams a live video and health condition measurements of the patient (e.g., his/her temperature) allowing doctors to know the patient's condition. Finally, it approaches the patient and provides the medicine. Then, the robot exits the room and goes to its original position.

## 3 Robotics in PPDR Vertical Sector

In this use case the robot attempts to prevent a hazardous event to take place in order to avoid a catastrophic situation. In particular, a robot WX200 enters a dangerous place, where a gas switch is located in an indoor environment. The robot identifies the gas switch location and fixes the problem by closing the switch. After that, a detection routine starts to check if any person is inside the room and sends an alert if a human is identified. The series of events that take place are:

1. In cases of extreme natural phenomena, a person will trigger the robotic action or a sensor identifies a gas leakage and trigger the robot.

2. The robot will enter the “dangerous” room where e.g. a gas switch is located.

3. The robot scans the space, identifies the switch and goes to the gas switch.

4. Using the manipulator, closes the switch and secures the room.

5. Robot starts human detection routine, if a human is identified an alert is sent.

This series of events is shown in the following Fig. 3.
<figure><img src="image_5.png" /><figcaption>Fig. 3. PPDR scenario</figcaption></figure>

# 4 Networks in 5G-ERA

Based on the description of the Healthcare and PPDR Use Cases, it is important to identify the characteristics of the network that will be able to support them. 5G networks are able to support heterogeneous requirements, offering network operators the capability to provide new services and fulfill the needs of the end-users. There are three generic services in 5G technology, which can include all the services that network providers design and implement for end-users [7, 8]:

* eMBB: Enhanced Mobile Broadband is used in use cases where very high peak rates across a wide coverage area are required. Its downlink speeds could be 1 Gbps in indoor environments and 300 Mbps in outdoor environments

* URLLC: Ultra-Reliable and Low Latency Communications supports low latency transmission of small payloads (in some cases latency could be 1 ms or less). At the same time, it offers high reliability, which could even be 99.99999%

* mMTC: Massive Machine Type Communications is used in use cases where a massive number of devices are required on a small area. For example, it could support 1M devices per km², unlike 4G which supports 1K devices per Km².

For the needs of 5G-ERA, a 5G Standalone (SA) Rel. 16 network will be integrated. The end-to-end network will include both hardware (e.g., antennas, packet core) and software (e.g., VNFs) components. The high-level architecture of the network is presented in Fig. 4, where it is separated into five layers:

* The RAN includes the antenna, the Radio Unit and the Baseband Unit. It is based on the New Radio (NR) technology, allowing slicing on the RAN.

* The packet core is deployed based on the SA Rel. 16 architecture, as it is presented in Fig. 3, allowing the installation of core VNFs and supporting slicing.
<figure><img src="image_6.png" /><figcaption>Fig. 4. 5G Network high level architecture</figcaption></figure>

* The Multi-access Edge Computing, where the edge VNFs are installed, is a network architecture, which allows cloud computing features, introduced by ETSI [9]. MEC leads to lower latency communications and increases network reliability by decreasing errors and data packet loss;

* The transport network, which uses the packet optical networking solution

* The Orchestrator, which is deployed by using Open-Source Mano (OSM) [10–12] and is responsible for controlling the available resources.

Slicing is another important characteristic of the network since it allows the use of different types of services supporting the heterogeneous requirements of the use case that could not be fulfilled by one type of service. Slicing is the key feature of the 5G networks that offers flexibility and programmability, accommodating new applications requirements, such as the ultra-low latency and the high reliability at the same time.
For the 5G-ERA use cases that were described, seven network requirements were identified. The Network Requirements are the characteristics that the network should offer in order to support the requested applications. These requirements are:

Data type: it indicates the type of data that should be transmitted. For example, video or voice communication or other types of data.

Latency: it is the time that is required for a small data packet to be transmitted. For critical applications, this time should be very short.

Reliability: It describes the amount of sent packets successfully delivered to the destination within the time constraint required by the targeted service, divided by the total number of sent packets.

Availability: it is the term that describes whether a service is provided 24 h/day 7days/week continuously. It is specified as a percentage, with values that are usually very close to 100%, e.g., 99.999%.

Mobility: it describes the user's need to receive and transmit information while moving.

Throughput: it describes the minimum data rate that is required for the user to get a quality experience.

Location accuracy: it allows the detection of user equipment in a specific area. It is important in critical use cases, like the 5G-ERA one, since it is important to know the exact place where the robot is located.

The values of these requirements for the 5G-ERA healthcare and PPDR use cases are presented in Table 1.

<table><caption>**Table 1.** 5G-ERA healthcare and PPDR use cases requirements</caption><thead><tr><th>Requirements</th><th>Value</th></tr></thead><tbody><tr><td>Data type</td><td>Video: Yes<br>Voice: No<br>Other data: Yes</td></tr><tr><td>Throughput</td><td>100 Mbps &lt; high ≤ 1.000 Mbps</td></tr><tr><td>Mobility</td><td>&lt;walking-running-cycling speed</td></tr><tr><td>Location accuracy</td><td>High: ≤ 1 m</td></tr><tr><td>Latency</td><td>&lt;25 ms</td></tr><tr><td>Reliability</td><td>High: 99.99999%</td></tr><tr><td>Availability</td><td>High: 99.99999%</td></tr></tbody></table>

## 5 Conclusion

In this paper the 5G-ERA healthcare and PPDR use cases were presented, as well as the 5G network design and requirements. The complexity of the environment where robots will be used, forces to a new approach on robot navigation and tasks manipulation
deployment. Cloud-based robots are essential for realizing a high degree of autonomy in robots. However, in order to combine the capabilities of autonomous robots and clouds, the robot must be able to perceive and understand its environment. To do so, 5G-ERA proposes an architecture, which was presented in this paper, which will be used in a case where a robot will be used and requires a network's high reliability and availability and high location accuracy.

Additionally, taking into consideration the special characteristics of the use cases, the network that will be integrated was presented, which will be based on the 5G Standalone (SA) Rel.16 architecture. Finally, the network requirements were identified and analyzed. As a result of this analysis, it was identified that it is important to implement a network that will support large throughput and reliability, while the location accuracy must be less than one meter in order to be able to accurately locate the robot.

**Acknowledgments.** This work has been performed in the scope of the 5G-ERA European Research Project and has been supported by the Commission of the European Communities (Grant Agreement No. 101016681).

## References

1. Qiu, R., et al.: The development of a semi-autonomous framework for personal assistant robots - SRS project. Int. J. Intell. Mechatron. Robot. (IJIMR) **3**(4), 30–47 (2013). https://doi.org/10.4018/ijimr.2013100102

2. GTI 5G and Cloud Robotics White Paper (2017). https://www.huawei.com/uk/industry-insights/outlook/mobile-broadband/xlabs/insights-whitepapers/gti-5g-and-cloud-robotics-white-paper

3. RoboEarth Project (2015). http://roboearth.ethz.ch

4. KnowRob Project (2014). http://knowrob.org

5. SRS Project (2013). http://wiki.ros.org/srs_public

6. 5G-ERA project. https://www.5g-era.eu

7. Yu, H., Lee, H., Jeon, H.: What is 5G? Emerging 5G mobile services and network requirements. Sustainability **9**, 1848 (2017). https://doi.org/10.3390/su9101848

8. Soldani, D., et al.: 5G mobile systems for healthcare. In: 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pp. 1–5 (2017). https://doi.org/10.1109/VTCSpring.2017.8108602

9. ETSI Multi-access Edge Computing. https://www.etsi.org/technologies/multi-access-edge-computing

10. Open-source Management and Orchestration (OSM). https://osm.etsi.org

11. ETSI Open-source Management and Orchestration. https://www.etsi.org/technologies/open-source-manual

12. Rafiq, A., Mehmood, A., Ahmed Khan, T., Abbas, K., Afaq, M., Song, W.-C.: Intent-based end-to-end network service orchestration system for multi-platforms. Sustainability **12**, 2782 (2020). https://doi.org/10.3390/su12072782