Greece – 6G-VERSUS

Greek Pilot - Collaborative robotics towards an early wildfire detection concept

Sustainability Aspects: Collaborative Robotics for Search & Rescue

Targeted Vertical: Life-saving environmental preservation

Partners:

Institute of Communication & Computer Systems (ICCS): Use Case leader, Developer and Integrator
National Center for Scientific Research ‘Demokritos’ (NCSRD) / ORGANISMOS TILEPIKOINONION TIS ELLADOS (ΟΤΕ): Testbed Providers
ORGANISMOS TILEPIKOINONION TIS ELLADOS (ΟΤΕ): Telecom Operator
INFOLYSiS: Developer & Integrator
Elliniki Omada Diasosis Attikis (HRTA): End User

This use case focuses jointly on collaborative robotics search and rescue and early wildfire detection concepts. The exact trial site’s location will be determined during the preparation phase and the trials plan.

Motivation and challenges:

Wildfires present significant challenges for search and rescue operations due to their rapid spread, remote locations, and hazardous conditions. In addition, first responders face numerous safety challenges during wildfire search and rescue operations. These include exposure to smoke, extreme heat, and unpredictable terrain conditions, which can jeopardize their health and wellbeing. Furthermore, communication constraints in remote areas may hinder the coordination efforts and decrease the operational efficiency. Traditional methods often face limitations in accessing affected areas and providing timely assistance to victims, and they rely heavily on manual intervention, placing responders in potentially dangerous situations. By integrating robotic dogs and UAVs, and advanced autonomous and human-robotic interactions together with 5G/B5G technologies, the scope is to increase efficiency and effectiveness of SaR operation and wildfire preparedness and response scenarios. This innovative approach leverages: a) fire detection; b) autonomous navigation; c) collaborative perception and decision making; d) augmented reality applications; e) network relay capabilities to optimize preparedness and rescue operations while ensuring the safety of responders and victims.

Solutions/Trial scenarios to address the challenges

The trial scenarios demonstrate the use case innovations through a storyline: In a remote forest area threatened by a wildfire, AI algorithms detect smoke or fire early through real-time analysis of HD/4K IP camera footage (step 1 in Figure 1) connected to the internet via 5G (2). Then audio/visual alerts are sent to relevant stakeholders for immediate response and a user-friendly web interface provides centralized control and monitoring, including real-time visualization and access to past events (3). Upon detection, a UAV provides aerial imagery of the area and can detect additional objects of interest (4), while a robotic dog is deployed in dense canopy areas for scouting (5). The UAV enhances the dog’s capabilities by providing up-to-date information and acts as a network relay node for seamless communication (6). The robotic dog transports essential supplies, establishes communication with trapped victims, and supports search and rescue operations (7).

The main 6G-VERSUS application components (app triplet) are summarized in Table 1.

Table 1: 6G-VERSUS application components for Greek use case

V-apps	(1) Collaborative robotics search and rescue (e.g UAV will enhance the dog’s perception and decision-making); (2) orchestrate all available assets toward specific search and rescue activities; (3) fire detection; (4) minimize ecological footprint by utilizing EEaaS approaches. Devices hosted in the field: sensors, UAV cameras, and robotic dogs
AI-apps	(1) enhance situational awareness and strategic planning; (2) analysis of relevant data in real-time, immediate insights and actionable information; (3) complementing the activities of the control room in decision making; (4) realization of dynamic decision-making algorithms that allocate resources dynamically according to the service needs of all involved components (e.g. robotic dog, UAV) to maximise the impact; (5) early wildfire detection based on real-time analysis of video streams.
N-Apps	(1) seamless and uninterrupted communication of all involved entities; (2) alleviate coverage issues related with the drastic changes of the environment; (3) adaptation of cloud-based solutions located in the MEC for additional processing power, enabling very fast in-depth analysis and pattern recognition; (4) minimize the ecological footprint of the proposed solution as a whole, by offering an EEaaS API toward the applications.

V-apps

(1) Collaborative robotics search and rescue (e.g UAV will enhance the dog’s perception and decision-making); (2) orchestrate all available assets toward specific search and rescue activities; (3) fire detection; (4) minimize ecological footprint by utilizing EEaaS approaches. Devices hosted in the field: sensors, UAV cameras, and robotic dogs

AI-apps

(1) enhance situational awareness and strategic planning; (2) analysis of relevant data in real-time, immediate insights and actionable information; (3) complementing the activities of the control room in decision making; (4) realization of dynamic decision-making algorithms that allocate resources dynamically according to the service needs of all involved components (e.g. robotic dog, UAV) to maximise the impact; (5) early wildfire detection based on real-time analysis of video streams.

N-Apps

(1) seamless and uninterrupted communication of all involved entities; (2) alleviate coverage issues related with the drastic changes of the environment; (3) adaptation of cloud-based solutions located in the MEC for additional processing power, enabling very fast in-depth analysis and pattern recognition; (4) minimize the ecological footprint of the proposed solution as a whole, by offering an EEaaS API toward the applications.

As sustainability is of vital importance in 6G-VERSUS, the main sustainability challenges are summarized together with the expected outcomes of this use case in Table 2.

Table 2: Main sustainability challenges and expected outcomes for Greek use case.

Main Sustainability Challenges
1) Environmental sustainability: Minimization of the ecological footprint of the proposed solution, by adopting EEaaS approaches
2) Societal sustainability: a) Personal health and protection from harm; b) Protect quality of life in remote communities
Expected Outcomes
1) Ultra reliable and low latency communication between all collaborative entities by offering a plethora of communication technologies that can be utilized in an ad-hoc manner. Low latency communication will be met by the utilization of MEC capabilities into several entities of the use case: on the drone, close to RRU/BBU, close to a group of RRUs/BBUs, in the cloud.
2) Utilization of high accuracy AI-based algorithms with high processing power needs by capitalizing on the MEC capabilities. Dynamic resource and service allocation approach and dynamic decision-making algorithms will be adopted, which realize service migration to appropriate MEC locations on demand.
3) EEaaS will increase energy efficiency, while energy related decision-making procedures can be executed on the application side (e.g. low energy consumption on regular periods (green path), maximize operational efficiency during incidents).

Acronyms:

UAV: Unmanned Aerial Vehicle
SaR: Search and Rescue
B5G: Beyond 5th Generation of mobile network technologies
4K: Ultra High Definition video with 4K resolution
MEC: Multi-access Edge Computing, a network architecture concept enabling cloud computing at the edge of the network
EEaaS: Energy Efficiency as a Service
API: Application Programming Interface as a set of protocols for building and integrating application software
RRU: Remote Radio Unit for radio transmission and reception
BBU: Base-band Unit for processing base-band signals in mobile networks