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Kostas Test 2

This is a test Innovation report

Unified Fire Protection Units and System-UFPUS

Knowing that forest fires represent a constant threat to ecological systems, infrastructure, economy and human lives, it is necessary to undertake joint actions towards increasing the level of protection against forest fires.

Developed by EXINN TECHNOLOGY CENTER
The main scope of the invention is to provide an Artificial Intelligence system and method for fire identification and extinguishing in real time, by combining and embodying existing tools, technology systems and products puzzle referred to as an innovation algorithm. This technology in the future should serve to protect the planet from natural fire emergencies and is ready to be test first in Albania by developing a pilot testing area/zone in one of our national forest parks in the country.

TRL5. Laboratory testing of integrated system
Technology validated in relevant environment. Fidelity of breadboard technology increases significantly. The basic technological components are integrated with reasonably realistic supporting elements so they can be tested in a simulated environment. Examples include “high-fidelity” laboratory integration of components. After we concluded on the AI theoretical model, we achieved its code implementation (a prototype), and because we are lacking a NRT fire map feed, we tried to simulate it by using conventional methods of online maps. Furthermore, on the hardware side, we have achieved to operate the drone programmatically (with computer code), thus the AI system is able to automatically give coordinates to the drone (or also known as Planning a flight route). Below is a list of our main achievements until now, regarding the software and hardware communication side: • AI theoretic model • AI implementation of the model (software prototype) • AI simulations, feedback and calibration of the model • AI notification system for personnel • Software communication with drones • Programmatical plan of drone flight path from AI. With further effort, we plan for the AI system to also be able to command the drones to drop the fireextinguishing materials on the fire, once the drone has arrived at the coordinates, as one of our main goals is to make the whole system automatic. Note that the drones, once arrive to the fire location, they don’t need any more guidance from the satellite, as they are able to “see” it by themselves. Again, this is how we design to complete our AI model and system to automatically coordinate the drone to drop the material exactly on the fire source.

See more information about this level and the TRL and SRL levels.

The main components of the system have been tested separately, and an initial integration exercise has been conducted.

Credits
Collaborators

How does it work?

We intend to make use of an existing satellite technology, which provides NRT (Near-Real-Time) map feed with information about fires on the terrain. These satellites, technologically speaking, are able to detect and reflect fires on the map with red-dots. Now this is where comes the AI model that we designed, which, provided a real-time map feed, is able to detect little red-dots on the map as fire by itself, with no need to be supervised by human factor. After it has identified the fire/s, it notifies for its finding/s and provides the drone with coordinates for it to follow. The AI system, provided a near-real-time fire feed map, is able to scan, identify and automatically coordinate drones with the location of the fires.

Limitations/conditions under which this innovation does not work or is less effective

Further tests are needed to detect the variable conditions under which this innovation could be less effective.

Added value
The drone/s has an attached smartphone on its body, for being able to communicate with the central AI system, even when out of range to its respective drone-house, for getting real-time commands and streaming live image from the environment. The smartphone also gives the drone semi-autonomous behavior and decision-making capabilities, to self-optimize its flight path and further increase the detail of path of dropping the fire-extinguishing balls into the source of fire, by taking advantage of the drone’s camera, the smartphone’s computational power and by using image-recognition technology, specifically trained to detect fires and identify their source. In case that the AI system identifies a fast-spreading fire, it should dispatch a fleet of nearby drones to try and contain the situation.

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